WO2025075815A1

WO2025075815A1 - Apparatus and methods for generating a three-dimensional (3d) model of an anatomical object via machine-learning

Info

Publication number: WO2025075815A1
Application number: PCT/US2024/047990
Authority: WO
Inventors: Rakesh Barve; Abhijith Chunduru; Uddeshya UPADHYAY; Suthirth Vaidya; Sai Saketh CHENNAMSETTY; Arjun Puranik
Original assignee: Anumana Inc
Current assignee: Anumana Inc
Priority date: 2023-10-04
Filing date: 2024-09-23
Publication date: 2025-04-10
Anticipated expiration: 2026-04-04

Abstract

In one or more embodiments, aspects of the present disclosure describe an apparatus for generating a three-dimensional (3D) model of cardiac anatomy via machine-learning, wherein the apparatus includes a computing device configured to receive a set of images of a cardiac anatomy pertaining to a subject, generate a three-dimensional (3D) data structure representing the cardiac anatomy as a function of the set of images, generate an initial 3D model of the cardiac anatomy, refine the generated initial 3D model of the cardiac anatomy as a function of the 3D data structure representing the cardiac anatomy and generate a subsequent 3D model of the cardiac anatomy as a function of the refinement.

Description

APPARATUS AND METHODS FOR GENERATING A THREE-DIMENSIONAL (3D) MODEL OF AN ANATOMICAL OBJECT VIA MACHINE-LEARNING CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority of U.S. Nonprovisional Application Serial No.18/818,034, filed on August 28, 2024, and entitled “APPARATUS AND METHODS FOR GENERATING A THREE-DIMENSIONAL (3D) MODEL OF AN ANATOMICAL OBJECT VIA MACHINE-LEARNING,” which is a continuation-in-part of Non-provisional Application No.18/750,411 filed on June 21, 2024, and entitled “APPARATUS AND METHODS FOR GENERATING A THREE-DIMENSIONAL (3D) MODEL OF AN ANATOMICAL OBJECT VIA MACHINE-LEARNING,” which is a continuation of Non- provisional Application No.18/376,688 filed on October 4, 2023, and entitled “APPARATUS AND METHODS FOR GENERATING A THREE-DIMENSIONAL (3D) MODEL OF CARDIAC ANATOMY VIA MACHINE-LEARNING,” the entirety of which are incorporated herein by reference. This application also claims the benefit of priority of U.S. Nonprovisional Application Serial No.18/817,870, filed on August 28, 2024, and entitled “APPARATUS AND METHODS FOR SYNTHESIZING MEDICAL IMAGES,” which is a continuation-in-part of Non-provisional Application No.18/509,520, filed on November 15, 2023, and entitled “APPARATUS AND METHODS FOR SYNTHESIZING MEDICAL IMAGES,” the entirety of which are incorporated herein by reference and U.S. Non-provisional Application No. 18/818,152, filed on August 28, 2024, and entitled “APPARATUS AND METHOD FOR GENERATING A THREE-DIMENSIONAL (3D) MODEL OF CARDIAC ANATOMY BASED ON MODEL UNCERTAINTY,” which is a continuation-in-part of Non-provisional Application No.18/426,604, filed on January 30, 2024, and entitled “APPARATUS AND METHOD FOR GENERATING A THREE-DIMENSIONAL (3D) MODEL OF CARDIAC ANATOMY BASED ON MODEL UNCERTAINTY,” the entirety of which are incorporated herein by reference. This application also claims the benefit of priority of U.S. Non-provisional Application No.18/818,311 filed on August 28, 2024, and entitled “APPARATUS AND METHOD FOR GENERATING A THREE-DIMENSIONAL (3D) MODEL OF CARDIAC ANATOMY WITH AN OVERLAY,” which is a continuation-in-part of Non-provisional Application No.18/395,087 filed on December 22, 2023, and entitled “APPARATUS AND 1 Attorney Docket No.1518-103PCT1 METHOD FOR GENERATING A THREE-DIMENSIONAL (3D) MODEL OF CARDIAC ANATOMY WITH AN OVERLAY,” the entirety of which are incorporated herein by reference. This application also claims the benefit of priority of U.S. Non-provisional Application No. 18/648,176 filed on April 26, 2024, and entitled “APPARATUS AND METHODS FOR VISUALIZATION WITHIN A THREE-DIMENSIONAL MODEL USING NEURAL NETWORKS,” the entirety of which is incorporated herein by reference. FIELD OF THE INVENTION The present invention generally relates to the field of machine learning and medical imaging. In particular, the present invention is directed to apparatus and methods for generating a three-dimensional (3D) model of an anatomical object via machine-learning. BACKGROUND A precise reconstruction of anatomical objects is of critical importance in order to achieve efficient and safe results in procedures such as atrial fibrillation (AF) ablation. Current reconstruction methods include Fast Anatomical Mapping (FAM), cardiac CT merging, and Ultrasound assisted anatomy reconstruction; however, existing processes are prone to overly long procedural times and excessive radiation exposure. In addition, existing processes cannot reconstruct differing anatomical objects. SUMMARY OF THE DISCLOSURE In one or more embodiments, aspects of the present disclosure describe an apparatus for generating a three-dimensional (3D) model of cardiac anatomy via machine-learning, wherein the apparatus includes at least a process, and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a set of images of a cardiac anatomy pertaining to a subject, generate a three- dimensional (3D) data structure representing the cardiac anatomy as a function of the set of images, wherein generating the 3D data structure includes receiving cardiac anatomy training data, wherein the cardiac anatomy training data includes a plurality of image sets as input and a plurality of cardiac anatomy models as output, training a cardiac anatomy modeling model using the cardiac anatomy training data, and generating the 3D data structure representing data structure representing the cardiac anatomy as a function of the set of images using the trained cardiac anatomy modeling model, generate an initial 3D model of the cardiac anatomy, refine the generated initial 3D model of the cardiac anatomy as a function of the 3D data structure 2 Attorney Docket No.1518-103PCT1 representing the cardiac anatomy, and generate a subsequent 3D model of the cardiac anatomy as a function of the refinement. In one or more embodiments, receiving the set of images includes receiving the set of images from a patient profile. In one or more embodiments, receiving the cardiac anatomy training data includes generating the cardiac anatomy training data using a synthetic ICE data generator. In one or more embodiments, the 3D data structure representing the cardiac anatomy includes a 3D voxel occupancy representation (VOR) having a plurality of voxels, wherein each voxel of the plurality of voxels includes a corresponding presence indicator. In one or more embodiments, the 3D data structure representing the cardiac anatomy further includes a 3D grid configured to map the presence indicators of the plurality of voxels, wherein the 3D grid includes one or more spatial features extracted from the set of images of the cardiac anatomy. In one or more embodiments, generating the 3D data structure representing the cardiac anatomy further includes generating a set of shape parameters based on the set of images of the cardiac anatomy. In one or more embodiments, generating the set of shape parameters based on the set of images includes training a shape identification model using cardiac geometry training data, wherein the cardiac geometry training data includes the plurality of image sets as input correlated to a plurality of shape parameter sets as output, and generating the set of shape parameters as a function of the set of images using the trained shape identification model. In one or more embodiments, the initial 3D model of the cardiac anatomy includes a template model selected from a plurality of pre-determined template models. In one or more embodiments, refining the initial 3D model of the cardiac anatomy includes deforming the template model to match the generated 3D data structure representing the cardiac anatomy. In one or more embodiments, refining the initial 3D model of the cardiac anatomy includes adjusting the subsequent 3D model of the cardiac anatomy as a function of a set of shape parameters. In one or more embodiments, aspects of this disclosure describe a method for generating a three-dimensional (3D) model of cardiac anatomy via machine-learning, wherein the method 3 Attorney Docket No.1518-103PCT1 includes receiving, by at least a processor, a set of images of a cardiac anatomy pertaining to a subject, generating, by the at least a processor, a 3D data structure representing the cardiac anatomy as a function of the set of images, wherein generating the 3D data structure includes receiving cardiac anatomy training data, wherein the cardiac anatomy training data includes a plurality of image sets as input and a plurality of computed tomography (CT) based cardiac anatomy models as output, training a cardiac anatomy modeling model using the cardiac anatomy training data, and generating the 3D data structure representing the cardiac anatomy as a function of the set of images using the trained cardiac anatomy modeling model, generating, by the at least a processor, an initial 3D model of the cardiac anatomy, refining, by the at least a processor, the generated initial 3D model of the cardiac anatomy as a function of the 3D data structure representing the cardiac anatomy using, and generating, by the at least a processor, a subsequent 3D model of the cardiac anatomy as a function of the refinement. In one or more embodiments, receiving the set of images includes receiving the set of images from a patient profile. In one or more embodiments, receiving the cardiac anatomy training data includes generating the cardiac anatomy training data using a synthetic ICE data generator. In one or more embodiments, the 3D data structure representing the cardiac anatomy includes a 3D voxel occupancy representation (VOR) having a plurality of voxels, wherein each voxel of the plurality of voxels includes a corresponding presence indicator. In one or more embodiments, the 3D data structure representing the cardiac anatomy further includes a 3D grid configured to map the presence indicators of the plurality of voxels, wherein the 3D grid includes one or more spatial features extracted from the set of images of the cardiac anatomy. In one or more embodiments, generating the 3D data structure representing the cardiac anatomy further includes generating a set of shape parameters based on the set of images of the cardiac anatomy. In one or more embodiments, generating the set of shape parameters based on the set of images includes training a shape identification model using cardiac geometry training data, wherein the cardiac geometry training data includes the plurality of image sets as input correlated to a plurality of shape parameter sets as output, and generating the set of shape parameters as a function of the set of images using the trained shape identification model. 4 Attorney Docket No.1518-103PCT1 In one or more embodiments, the initial 3D model of the cardiac anatomy includes a template model selected from a plurality of pre-determined template models. In one or more embodiments, refining the initial 3D model of the cardiac anatomy includes deforming the template model to match the generated 3D data structure representing the cardiac anatomy using an SSM. In one or more embodiments, refining the initial 3D model of the cardiac anatomy includes adjusting the subsequent 3D model of the cardiac anatomy as a function of a set of shape parameters using the SSM. In one or more embodiments, aspects of the present disclosure describe an apparatus for generating a three-dimensional (3D) model of cardiac anatomy via machine-learning, wherein the apparatus includes at least a processor, and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a set of images of a cardiac anatomy pertaining to a subject, generate cardiac anatomy training data using a 3D heart model, wherein the cardiac anatomy training data includes a plurality of synthetic images, train a cardiac anatomy modeling model using the generated cardiac anatomy training data, generate a three-dimensional (3D) data structure representing the cardiac anatomy using the trained cardiac anatomy modeling model, and refine an initial 3D model as a function of the 3D data structure representing the cardiac anatomy. In one or more embodiments, receiving the set of images includes receiving the set of images from a patient profile. In one or more embodiments, the 3D heart model is configured to receive ongoing feedback and corrections to the 3D heart model and provide corrections to subsequent synthetic images. In one or more embodiments, generating the cardiac anatomy training data includes generating the 3D heart model using a plurality of CT scans, generating, as a function of the 3D heart model, a plurality of synthetic ICE frames using a synthetic ICE data generator, and generating the cardiac anatomy training data as a function of the plurality of synthetic ICE frames. In one or more embodiments, the 3D data structure representing the cardiac anatomy includes a 3D voxel occupancy representation (VOR) having a plurality of voxels, wherein each voxel of the plurality of voxels includes a corresponding presence indicator. 5 Attorney Docket No.1518-103PCT1 In one or more embodiments, the 3D data structure representing the cardiac anatomy further includes a 3D grid configured to map the presence indicators of the plurality of voxels, wherein the 3D grid includes one or more spatial features extracted from the set of images of the cardiac anatomy. In one or more embodiments, the synthetic images include synthetic ICE image frames, wherein the synthetic ICE image frames include bold lines and shading to represent extracted contours of an ICE image. In one or more embodiments, the initial 3D model of the cardiac anatomy includes a template model selected from a plurality of pre-determined template models. In one or more embodiments, refining the initial 3D model of the cardiac anatomy includes deforming the template model to match the generated 3D data structure representing the cardiac anatomy. In one or more embodiments, refining the initial 3D model of the cardiac anatomy includes adjusting the refined initial 3D model of the cardiac anatomy as a function of a set of shape parameters. In one or more embodiments, aspects of this disclosure describe a method for generating a three-dimensional (3D) model of cardiac anatomy via machine-learning, wherein the method includes receiving, by at least a processor, a set of images of a cardiac anatomy pertaining to a subject, generating, by the at least a processor, cardiac anatomy training data using a 3D heart model, wherein the cardiac anatomy training data includes a plurality of synthetic images, training, by the at least a processor, a cardiac anatomy modeling model using the generated cardiac anatomy training data, generating, by the at least a processor, a three-dimensional (3D) data structure representing the cardiac anatomy using the trained cardiac anatomy modeling model, and refining, by the at least a processor, an initial 3D model as a function of the 3D data structure representing the cardiac anatomy. In one or more embodiments, receiving the set of images includes receiving the set of images from a patient profile. In one or more embodiments, the 3D heart model is configured to receive ongoing feedback and corrections to the 3D heart model and provide corrections to subsequent synthetic images. 6 Attorney Docket No.1518-103PCT1 In one or more embodiments, generating the cardiac anatomy training data includes generating the 3D heart model using a plurality of CT scans, generating, as a function of the 3D heart model, a plurality of synthetic Ice frames using a synthetic ICE data generator, and generating the cardiac anatomy training data as a function of the plurality of synthetic ICE frames. In one or more embodiments, the 3D data structure representing the cardiac anatomy includes a 3D voxel occupancy representation (VOR) having a plurality of voxels, wherein each voxel of the plurality of voxels includes a corresponding presence indicator. In one or more embodiments, the 3D data structure representing the cardiac anatomy further includes a 3D grid configured to map the presence indicators of the plurality of voxels, wherein the 3D grid includes one or more spatial features extracted from the set of images of the cardiac anatomy. In one or more embodiments, the synthetic images include synthetic ICE image frames, wherein the synthetic ICE image frames include bold lines and shading to represent extracted contours of an ICE image. In one or more embodiments, the initial 3D model of the cardiac anatomy includes a template model selected from a plurality of pre-determined template models. In one or more embodiments, refining the initial 3D model of the cardiac anatomy includes deforming the template model to match the generated 3D data structure representing the cardiac anatomy. In one or more embodiments, refining the initial 3D model of the cardiac anatomy includes adjusting the refined initial 3D model of the cardiac anatomy as a function of a set of shape parameters. In one or more embodiments, aspects of the present disclosure describe an apparatus for generating a three-dimensional (3D) model of an anatomical object via machine-learning, wherein the apparatus includes at least a processor, and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a set of images of an anatomical object pertaining to a subject, generate anatomy training data using a 3D anatomical model, wherein the anatomy training data includes a plurality of image sets as input and a plurality of anatomical object models as output, train an anatomy modeling model using the generated anatomy training data, generate a three- 7 Attorney Docket No.1518-103PCT1 dimensional (3D) data structure representing the anatomical object using the trained anatomy modeling model, and refine an initial 3D model as a function of the 3D data structure representing the anatomical object. In one or more embodiments, the set of images include one or more ultrasonic images. In one or more embodiments, the anatomical object includes an organ. In one or more embodiments, receiving the set of images includes receiving the set of images from a patient profile. In one or more embodiments, receiving the set of images from the patient profile further includes receiving (ECG) data associated with the subject form the patient profile, and the anatomy training data further includes the plurality of image sets and a plurality of ECG data as inputs and the plurality of anatomical object models as outputs. In one or more embodiments, the trained anatomy modeling model includes a multimodal machine learning model. In one or more embodiments, the 3D anatomical model is configured to receive ongoing feedback and corrections to the 3D anatomical model and provide corrections to subsequent synthetic images. In one or more embodiments, generating the initial 3D model includes determining a level of uncertainty of at least one location of a plurality of locations of the initial 3D model. In one or more embodiments, generating the initial 3D model further includes generating a map visualizing a level of uncertainty on the 3D model. In one or more embodiments, the initial 3D model of the anatomical object includes a template model selected from a plurality of pre-determined template models. In one or more embodiments, aspects of this disclosure describe a method for generating a three-dimensional (3D) model of an anatomical object via machine-learning, wherein the method includes receiving, by at least a processor, a set of images of an anatomical object pertaining to a subject, generating, by the at least a processor, anatomy training data using a 3D anatomical model, wherein the anatomy training data includes a plurality of image sets as input and a plurality of anatomical object models as output, training, by the at least a processor, an anatomy modeling model using the generated anatomy training data, generating, by the at least a processor, a three-dimensional (3D) data structure representing the anatomical object using the 8 Attorney Docket No.1518-103PCT1 trained anatomy modeling model, and refining, by the at least a processor, an initial 3D model as a function of the 3D data structure representing the anatomical object. In one or more embodiments, the set of images include one or more ultrasonic images. In one or more embodiments, the anatomical object includes an organ. In one or more embodiments, receiving, by the at least a processor, the set of images includes receiving the set of images from a patient profile. In one or more embodiments, receiving, the set of images from the patient profile further includes receiving (ECG) data associated with the subject form the patient profile, and the anatomy training data further includes the plurality of image sets and a plurality of ECG data as inputs and the plurality of anatomical object models as outputs. In one or more embodiments, the trained anatomy modeling model includes a multimodal machine learning model. In one or more embodiments, receiving, by the at least a processor, the set of images includes receiving the set of images from a patient profile. In one or more embodiments, generating, by the at least a processor, the anatomy training data using the 3D anatomical model includes classifying the set of images to an anatomical categorization, and generating the anatomy training data using the 3D anatomical model as a function of the anatomical categorization. In one or more embodiments, the 3D anatomical model is configured to receive ongoing feedback and corrections to the 3D anatomical model and provide corrections to subsequent synthetic images. In one or more embodiments, generating the initial 3D model further includes generating a map visualizing a level of uncertainty on the 3D model. In one or more embodiments, the initial 3D model of the anatomical object includes a template model selected from a plurality of pre-determined template models. In one or more embodiments, aspects of the present disclosure describe an apparatus for synthetizing medical images, wherein the apparatus includes at least a process, and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a heart model related to a patient’s heart, identify a region of interest within the heart model, wherein identifying the region of interest includes locating at least a point of view on the heart model, and determining a view angle corresponding 9 Attorney Docket No.1518-103PCT1 to the at least a point of view, wherein the at least a point of view and the corresponding view angle define at least one field of view that include at least a portion of the heart model, and generate at least a medical image as a function of the region of interest using an image generator, wherein the at least a medical image captures an anatomical structure of the at least a portion of the heart model. In one or more embodiments, receiving the heart model includes constructing the heart model based on a patient profile pertaining to the patient using computer vision module, wherein the patient profile includes a set of images of the patient’s heart and associated metadata. In one or more embodiments, the patient profile further includes electrocardiogram (ECG) data. In one or more embodiments, receiving the heart model includes transforming the heart model to a second heart model using a Statistical Shape Model as a function of a plurality of mode changers within the Statistical Shape Model, wherein each mode changer of the plurality of mode changers is associated with a model feature of the heart model. In one or more embodiments, the heart model includes a 3D voxel occupancy representation (VOR) of the patient’s heart. In one or more embodiments, generating the at least a medical image includes executing a camera transformation program configured to simulate at least a perspective of a probe using the image generator. In one or more embodiments, executing the camera transformation program includes generating a projection of the anatomical structure by rendering the region of interest as a function of a set of imaging parameters using a virtual camera positioned at the at least a point of view with the corresponding view angle. In one or more embodiments, the image generator includes a generative adversarial network (GAN). In one or more embodiments, generating the at least a medical includes training the GAN using a plurality of anatomical structure projections, and synthesizing at least a medical image using the trained GAN at the at least a point of view with the corresponding view angle. In one or more embodiments, the memory contains instructions further configuring the at least a processor to compile a plurality of medical images into a video as a function of the ECG data, wherein the video is synchronized with a cardiac cycle indicated by the ECG data. 10 Attorney Docket No.1518-103PCT1 In one or more embodiments, aspects of this disclosure describe a method for synthetizing medical images, wherein the method includes receiving, by at least a processor, a heart model related to a patient’s heart, identifying, by the at least a processor, a region of interest within the heart model, wherein identifying the region of interest includes locating at least a point of view on the heart model, and determining a view angle corresponding to the at least a point of view, wherein the at least a point of view and the corresponding view angle define at least one field of view that include at least a portion of the heart model, and generating, by the at least a processor, at least a medical image as a function of the region of interest using an image generator, wherein the at least a medical image captures an anatomical structure of the at least a portion of the heart model. In one or more embodiments, receiving the heart model includes constructing the heart model based on a patient profile pertaining to the patient using a computer vision module, wherein the patient profile includes a set of images of the patient’s heart and associated metadata. In one or more embodiments, the patient profile further includes electrocardiogram (ECG) data. In one or more embodiments, receiving the heart model includes transforming the heart model to a second heart model using a Statistical Shape Model as a function of a plurality of mode changers within the Statistical Shape Model, wherein each mode changer of the plurality of mode changers is associated with a model feature of the heart model. In one or more embodiments, the heart model includes a 3D voxel occupancy representation (VOR) of the patient’s heart. In one or more embodiments, generating the at least a medical image includes executing a camera transformation program configured to simulate at least a perspective of a probe using the image generator. In one or more embodiments, executing the camera transformation program includes generating a projection of the anatomical structure by rendering the region of interest as a function of a set of imaging parameters using a virtual camera positioned at the at least a point of view with the corresponding view angle. In one or more embodiments, the image generator includes a generative adversarial network (GAN). 11 Attorney Docket No.1518-103PCT1 In one or more embodiments, generating the at least a medical image includes training the GAN using a plurality of anatomical structure projections, and synthesizing at least a medical image using the trained GAN at the at least a point of view with the corresponding view angle. The method of claim 74, further includes compiling, by the at least a processor, a plurality of medical images into a video as a function of the ECG data, wherein the video is synchronized with a cardiac cycle indicated by the ECG data. In one or more embodiments, aspects of the present disclosure describe an apparatus for synthetizing medical images, wherein the apparatus includes at least a process, and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive an ultrasound image of a patient’s organ, generate an organ model related to the patient’s organ as a function of the ultrasound image, identify a region of interest within the organ model, wherein identifying the region of interest includes locating at least a point of view on the organ model, and determining a view angle corresponding to the at least a point of view, wherein the at least a point of view and the corresponding view angle define at least one field of view that include at least a portion of the organ model, and generate at least a medical image as a function of the region of interest using an image generator, wherein the at least a medical image captures an anatomical structure of the at least a portion of the organ model. In one or more embodiments, the ultrasound image of the patient’s organ includes one or more of a transesophageal echocardiogram image, transthoracic echocardiogram image, and point-of-care ultrasound image. In one or more embodiments, generating the organ model includes generating a three- dimensional (3D) data structure representing the patient’s organ using an anatomy modeling model. In one or more embodiments, generating the 3D data structure representing the patient’s organ using the anatomy modeling model includes generating anatomy training data, wherein the anatomy training data includes a plurality of image sets as input and a plurality of anatomical object models as output, training the anatomy modeling model using the anatomy training data, and generating the 3D data structure using the trained anatomy modeling model. In one or more embodiments, the image generator includes a generative machine- learning model. 12 Attorney Docket No.1518-103PCT1 In one or more embodiments, generating the at least a medical image of the patient’s organ includes receiving image training data, wherein the image training data includes exemplary organ models correlated to exemplary medical images, training the generative machine-learning model using the image training data, and generating the at least a medical image of the patient’s organ using the generative machine-learning model. In one or more embodiments, identifying the region of interest within the organ model includes selecting a first set of points from a medical image, determining a second set of points on the organ model corresponding to the first set of points, and mapping a plurality of points of the medical image to the organ model using a relationship between the first set of points and the second set of points. In one or more embodiments, mapping the plurality of points of the medical image to the organ model using the relationship between the first set of points and the second set of points includes determining a rigid transformation from the first set of points to the second set of points. In one or more embodiments, generating the organ model includes transforming the organ model to a second organ model using a Statistical Shape Model as a function of a plurality of mode changers within the Statistical Shape Model, wherein each mode changer of the plurality of mode changers is associated with a model feature of the organ model. In one or more embodiments, generating the at least a medical image includes generating a plurality of medical images, and the memory contains instructions further configuring the at least a processor to compile the plurality of medical images into a video, and display the video on a display device. In one or more embodiments, aspects of this disclosure describe a method for synthetizing medical images, wherein the method includes receiving, by at least a processor, an ultrasound image of a patient’s organ, generating, by at least a processor, an organ model related to the patient’s organ as a function of the ultrasound image, identifying, by the at least a processor, a region of interest within the organ model, wherein identifying the region of interest includes locating at least a point of view on the organ model, and determining a view angle corresponding to the at least a point of view, wherein the at least a point of view and the corresponding view angle define at least one field of view that include at least a portion of the organ model, and generating, by the at least a processor, at least a medical image as a function of 13 Attorney Docket No.1518-103PCT1 the region of interest using an image generator, wherein the at least a medical image captures an anatomical structure of the at least a portion of the organ model. In one or more embodiments, the ultrasound image of the patient’s organ includes one or more of a transesophageal echocardiogram image, transthoracic echocardiogram image, and point-of-care ultrasound image. In one or more embodiments, generating the organ model includes generating a three- dimensional (3D) data structure representing the patient’s organ using an anatomy modeling model. In one or more embodiments, generating the 3D data structure representing the patient’s organ using the anatomy modeling model includes generating anatomy training data, wherein the anatomy training data includes a plurality of image sets as input and a plurality of anatomical object models as output, training the anatomy modeling model using the anatomy training data, and generating the 3D data structure using the trained anatomy modeling model. In one or more embodiments, the image generator includes a generative machine- learning model. In one or more embodiments, generating the at least a medical image of the patient’s organ includes receiving image training data, wherein the image training data includes exemplary organ models correlated to exemplary medical images, training the generative machine-learning model using the image training data, and generating the at least a medical image of the patient’s organ using the generative machine-learning model. In one or more embodiments, identifying the region of interest within the organ model includes selecting a first set of points from a medical image, determining a second set of points on the organ model corresponding to the first set of points, and mapping a plurality of points of the medical image to the organ model using a relationship between the first set of points and the second set of points. In one or more embodiments, mapping the plurality of points of the medical image to the organ model using the relationship between the first set of points and the second set of points includes determining a rigid transformation from the first set of points to the second set of points. In one or more embodiments, generating the organ model includes transforming the organ model to a second organ model using a Statistical Shape Model as a function of a plurality 14 Attorney Docket No.1518-103PCT1 of mode changers within the Statistical Shape Model, wherein each mode changer of the plurality of mode changers is associated with a model feature of the organ model. In one or more embodiments, generating the at least a medical image includes generating a plurality of medical images, and the method further includes compiling, by the at least a processor, the plurality of medical images into a video, and displaying, by the at least a processor, the video on a display device. In one or more embodiments, aspects of the present disclosure describe a method of generating a three-dimensional (3D) model of cardiac anatomy, the method including using at least a processor, receiving a first set of images of cardiac anatomy, using at least a processor, generating a first 3D model of the cardiac anatomy as a function of the first set of images, using at least a processor, calculating a level of uncertainty at a plurality of locations on the first 3D model, using at least a processor, receiving a second set of images of the cardiac anatomy corresponding to a high uncertainty region of the first 3D model, and using at least a processor, generating a second 3D model as a function of the second set of images. In one or more embodiments, receiving a second set of images includes using a display device, displaying the first 3D model of the cardiac anatomy to a user, and by the user, positioning a cardiac image capture device for capturing an image of a low confidence region. In one or more embodiments, displaying the first 3D model of the cardiac anatomy to the user includes using a display device, displaying the first 3D model of the cardiac anatomy to a user, generating a first map including a level of uncertainty at each location of a plurality of locations on the generated first 3D model, and overlaying the first map onto the first 3D model. In one or more embodiments, the first map identifies the high uncertainty region of the first 3D model. In one or more embodiments, the first map includes a color-coded heat map configured to visualize one or more areas of uncertainty on the first 3D model. In one or more embodiments, receiving a second set of images includes capturing a second set of images using a cardiac image capture device, wherein the cardiac image capture device includes an intracardiac echocardiography catheter. In one or more embodiments, the method further includes removing an image of the first set of images from the first set of images. 15 Attorney Docket No.1518-103PCT1 In one or more embodiments, the method further includes duplicating an image of the first set of images and adding the duplicate to the first set of images. In one or more embodiments, generating the first 3D model includes generating the first 3D model using a neural network. In one or more embodiments, generating the first 3D model using a neural network includes generating a set of shape parameters based on the first set of images, generating the set of shape parameters includes receiving cardiac geometry training data including a plurality of image sets as inputs correlated to a plurality of shape parameter sets as outputs, training a shape identification model using the cardiac geometry training data, and generating the set of shape parameters using the shape identification model, and the first 3D model is generated based on the set of shape parameters. In one or more embodiments, the high uncertainty region is determined using model output uncertainty. In one or more embodiments, the plurality of shape parameter sets of the cardiac geometry training data is generated using computed tomography. In one or more embodiments, the neural network includes a convolutional neural network. In one or more embodiments, generating the first 3D model further includes using a statistical shape model to generate the first 3D model as a function of the set of shape parameters. In one or more embodiments, the set of shape parameters includes a plurality of numerical descriptors, wherein each numerical descriptor of the plurality of numerical descriptors represents a geometric characteristic of the cardiac anatomy. In one or more embodiments, each shape parameter within the set of shape parameters includes a corresponding parameter range. In one or more embodiments, the method further includes continuously updating, using the processor, the second 3D model as a function of further sets of images. In one or more embodiments, the method further includes displaying the second 3D model to a user. In one or more embodiments, displaying the second 3D model of the cardiac anatomy to the user includes generating a second map by determining a level of uncertainty at each location 16 Attorney Docket No.1518-103PCT1 of a plurality of locations on the generated second 3D model, and overlaying the second map onto the second 3D model. In one or more embodiments, the second map includes a color-coded heat map configured to visualize one or more areas of uncertainty on the second 3D model. In one or more embodiments, aspects of the present disclosure describe an apparatus of generating a three-dimensional (3D) model of a patient’s organ, the apparatus includes at least a processor, and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a first set of images of a patient’s organ, determine, at a trained neural network, a first set of shape parameters as a function of the first set of images, generate a first 3D model of the patient’s organ as a function of the first set of shape parameters, calculate a level of uncertainty at each location of a plurality of locations on the first 3D model of the patient’s organ, receive a second set of images of the patient’s organ corresponding to a high uncertainty region of the first 3D model, determine, at the trained neural network, a second set of shape parameters as a function of the first set of images and the second set of images, and generate a second 3D model of the patient’s organ as a function of the second set of shape parameters. In one or more embodiments, the first set of images and the second set of images of the patient’s organ includes a plurality of ultrasound images, and wherein the plurality of ultrasound images includes one or more of a transesophageal echocardiogram image, transthoracic echocardiogram image, and point-of-care ultrasound image. In one or more embodiments, determining the second set of shape parameters includes combining the second set of images with the first set of images by replacing one or more images corresponding to the high uncertainty region of the first 3D model within the first set of images with the second set of images. In one or more embodiments, determining the second set of shape parameters includes calibrating the trained neural network by fine-tuning the trained neural network using the first set of images, and determining the second set of shape parameters as a function of the second set of images using the trained neural network. In one or more embodiments, generating the first 3D model includes generating, as a function of the first set of shape parameters, the first 3D model using a statistical shape model. 17 Attorney Docket No.1518-103PCT1 In one or more embodiments, generating the second 3D model includes adjusting, at the statistical shape model, the first 3D model as a function of the second set of shape parameters. In one or more embodiments, calculating the level of uncertainty at each location of the plurality of locations of the first 3D model of the patient’s organ includes generating a first map including the level of uncertainty at each location of the plurality of locations on the first 3D model of the patient’s organ, overlaying the first map onto the first 3D model, and displaying, using a display device, the first 3D model of the patient’s organ to a user. In one or more embodiments, generating the second 3D model of the patient’s organ includes generating a second map by re-calculating the level of uncertainty at each location of the plurality of locations on the second 3D model of the patient’s organ, overlaying the second map onto the second 3D model of the patient’s organ, and displaying, using the display device, the second 3D model of the patient’s organ to the user. In one or more embodiments, receiving the second set of images of the patient’s organ includes identifying, on the first map, the high uncertainty region of the first second 3D model of patient’s organ by comparing the level of uncertainty at each location of the plurality of locations to a pre-determined uncertainty threshold. In one or more embodiments, each one of the first map and the second map includes a color-coded heat map configured to visualize one or more areas of uncertainty on the first 3D model and second 3D model of the patient’s organ respectively. In one or more embodiments, aspects of the present disclosure describe a method of generating a three-dimensional (3D) model of a patient’s organ, the method includes using at least a processor, receiving a first set of images of a patient’s organ, using the at least a processor, determining, at a trained neural network, a first set of shape parameters as a function of the first set of images, using the at least a processor, generating a first 3D model of the patient’s organ as a function of the first set of shape parameters, using the at least a processor, calculating a level of uncertainty at each location of a plurality of locations on the first 3D model of the patient’s organ, using the at least a processor, receiving a second set of images of the patient’s organ corresponding to a high uncertainty region of the first 3D model, using the at least a processor, determining, at the trained neural network, a second set of shape parameters as a function of the first set of images and the second set of images, and using the at least a processor, generating a second 3D model of the patient’s organ as a function of the second set of shape parameters. 18 Attorney Docket No.1518-103PCT1 In one or more embodiments, the first set of images and the second set of images of the patient’s organ includes a plurality of ultrasound images, and wherein the plurality of ultrasound images includes one or more of a transesophageal echocardiogram image, transthoracic echocardiogram image, and point-of-care ultrasound image. In one or more embodiments, determining the second set of shape parameters includes combining the second set of images with the first set of images by replacing one or more images corresponding to the high uncertainty region of the first 3D model within the first set of images with the second set of images. In one or more embodiments, determining the second set of shape parameters includes calibrating the trained neural network by fine-tuning the trained neural network using the first set of images, and determining the second set of shape parameters as a function of the second set of images using the trained neural network. In one or more embodiments, generating the first 3D model includes generating, as a function of the first set of shape parameters, the first 3D model using a statistical shape model. In one or more embodiments, generating the second 3D model includes adjusting, at the statistical shape model, the first 3D model as a function of the second set of shape parameters. In one or more embodiments, calculating the level of uncertainty at each location of the plurality of locations of the first 3D model of the patient’s organ includes generating a first map including the level of uncertainty at each location of the plurality of locations on the first 3D model of the patient’s organ, overlaying the first map onto the first 3D model, and displaying, using a display device, the first 3D model of the patient’s organ to a user. In one or more embodiments, generating the second 3D model of the patient’s organ includes generating a second map by re-calculating the level of uncertainty at each location of the plurality of locations on the second 3D model of the patient’s organ, overlaying the second map onto the second 3D model of the patient’s organ, and displaying, using the display device, the second 3D model of the patient’s organ to the user. In one or more embodiments, receiving the second set of images of the patient’s organ includes identifying, on the first map, the high uncertainty region of the first second 3D model of patient’s organ by comparing the level of uncertainty at each location of the plurality of locations to a pre-determined uncertainty threshold. 19 Attorney Docket No.1518-103PCT1 In one or more embodiments, each one of the first map and the second map includes a color-coded heat map configured to visualize one or more areas of uncertainty on the first 3D model and second 3D model of the patient’s organ respectively. In one or more embodiments, aspects of the present disclosure describe an apparatus for generating a three-dimensional (3D) model of cardiac anatomy with an overlay, wherein the apparatus includes at least a process, and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a set of images of a cardiac anatomy pertaining to a subject, generate a set of shape parameters based on the set of images, wherein generating the set of shape parameters includes generating the set of shape parameters as a function of the set of images and a shape identification model, generate a 3D model of the cardiac anatomy based on the set of shape parameters, generate a map by determining a level of uncertainty at each location of a plurality of locations on the generated 3D model, and overlay an image from the set of images onto the 3D model. In one or more embodiments, generating the set of shape parameters further includes inputting the set of images into the shape identification model, and wherein the shape identification model has been trained using cardiac geometry training data including a plurality of image sets as input correlated to a plurality of shape parameter sets as output. In one or more embodiments, generating the set of shape parameters further includes receiving the cardiac geometry training data including the plurality of image sets as input correlated to the plurality of shape parameter sets as output, and training the shape identification model using the cardiac geometry training data. In one or more embodiments, the set of shape parameters includes a plurality of numerical descriptors representing at least a geometric characteristic of the cardiac anatomy. In one or more embodiments, each shape parameter within the set of shape parameters includes a corresponding parameter range. In one or more embodiments, receiving the set of images includes receiving the set of images from a patient profile. The apparatus of claim 142, further including receiving cardiac anatomy training data, wherein receiving the cardiac anatomy training data includes generating the cardiac anatomy training data using a synthetic ICE data generator. 20 Attorney Docket No.1518-103PCT1 In one or more embodiments, the instructions further configured to the at least a processor to overlay the map onto the 3D model. In one or more embodiments, the map includes a color-coded heat map configured to visualize one or more areas of uncertainty on the 3D model. In one or more embodiments, overlaying the 3D model with the map includes utilizing interactive visualization techniques configured to allow user-mediated augmentation of the set of images of cardiac anatomy. In one or more embodiments, generating the 3D model further includes generating a second 3D model as a function of the 3D model, by varying the set of shape parameters, wherein the second 3D model is statistically constrained. In one or more embodiments, overlaying the map onto the 3D model includes overlaying an ICE frame to a corresponding location of the 3D model. In one or more embodiments, aspects of this disclosure describe a method for generating a three-dimensional (3D) model of cardiac anatomy with an overlay, wherein the method includes receiving, by a processor, a set of images of a cardiac anatomy pertaining to a subject, generating, by the processor, a set of shape parameters based on the set of images, wherein generating the set of shape parameters includes generating the set of shape parameters using the set of images and a shape identification model, generating, by the processor, a 3D model of the cardiac anatomy based on the set of shape parameters, generating, by the processor, a map by determining a level of uncertainty at each location of a plurality of locations on the generated 3D model, and overlaying, by the processor, an image from the set of images onto the 3D model. In one or more embodiments, generating the set of shape parameters further includes inputting the set of images into the shape identification model, and wherein the shape identification model has been trained using cardiac geometry training data including a plurality of image sets as input correlated to a plurality of shape parameter sets as output. In one or more embodiments, generating the set of shape parameters further includes receiving the cardiac geometry training data including the plurality of image sets as input correlated to the plurality of shape parameter sets as output, and training the shape identification model using the cardiac geometry training data. In one or more embodiments, the set of shape parameters includes a plurality of numerical descriptors representing at least a geometric characteristic of the cardiac anatomy. 21 Attorney Docket No.1518-103PCT1 In one or more embodiments, each shape parameter within the set of shape parameters includes a corresponding parameter range. In one or more embodiments, receiving the set of images includes receiving the set of images from a patient profile. In one or more embodiments, the method further includes receiving cardiac anatomy training data wherein receiving the cardiac anatomy training data includes generating the cardiac anatomy training data using a synthetic ICE data generator. In one or more embodiments, the method further includes overlay, using the at least a processor, the map onto the 3D model. In one or more embodiments, the map includes a color-coded heat map configured to visualize one or more areas of uncertainty on the 3D model. In one or more embodiments, overlaying the 3D model with the map includes utilizing interactive visualization techniques configured to allow user-mediated augmentation of the set of images of cardiac anatomy. In one or more embodiments, generating the 3D model further includes generating a second 3D model as a function of the 3D model, by varying the set of shape parameters, wherein the second 3D model is statistically constrained. In one or more embodiments, overlaying the 3D model includes overlaying an ICE frame to a corresponding location of the 3D model. In one or more embodiments, aspects of the present disclosure describe an apparatus for generating a three-dimensional (3D) model with an overlay, wherein the apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a set of ultrasonic images of an organ of a subject, generate a set of shape parameters representing the organ’s shape as a function of the set of ultrasonic images and a shape identification model trained on a training dataset including historical ultrasonic images correlated with historical computed tomography scan data, generate a 3D model of the organ based on the set of shape parameters, generate a map by determining a level of uncertainty at each location of a plurality of locations on the 3D model and overlay the map onto the 3D model. 22 Attorney Docket No.1518-103PCT1 In one or more embodiments, the set of ultrasonic images of the organ includes an image selected from a list consisting of a transesophageal echocardiogram image, a transthoracic echocardiogram image and a point-of-care ultrasound image. In one or more embodiments, the memory contains instructions configuring the at least a processor to identify the training dataset, the memory contains instructions configuring the at least a processor to train the shape identification model on the training dataset and identifying the training dataset includes correlating an instance of computed tomography scan data with a historical ultrasonic image as a function of a medical record and a language model. In one or more embodiments, the memory contains instructions configuring the at least a processor to identify the training dataset, the memory contains instructions configuring the at least a processor to train the shape identification model on the training dataset and identifying the training dataset includes generating a synthetic ultrasonic image as a function of an instance of computed tomography scan data. In one or more embodiments, the memory contains instructions configuring the at least a processor to determine a Left Atrial Appendage Occlusion Device placement as a function of the 3D model. In one or more embodiments, the set of shape parameters includes a plurality of numerical descriptors representing at least a geometric characteristic of the organ. In one or more embodiments, each shape parameter within the set of shape parameters is associated with a corresponding parameter range. In one or more embodiments, receiving the set of ultrasonic images includes receiving the set of ultrasonic images from a patient profile. In one or more embodiments, the map includes a color-coded heat map configured to visualize one or more areas of uncertainty on the 3D model. In one or more embodiments, generating the 3D model further includes generating a second 3D model as a function of the 3D model, by varying the set of shape parameters, wherein the second 3D model is statistically constrained. In one or more embodiments, aspects of the present disclosure describe a method of generating a three-dimensional (3D) model with an overlay, wherein the method includes using at least a processor, receiving a set of ultrasonic images of an organ of a subject, using the at least a processor, generating a set of shape parameters representing the organ’s shape as a 23 Attorney Docket No.1518-103PCT1 function of the set of ultrasonic images and a shape identification model trained on a training dataset including historical ultrasonic images correlated with historical computed tomography scan data, using the at least a processor, generating a 3D model of the organ based on the set of shape parameters, using the at least a processor, generating a map by determining a level of uncertainty at each location of a plurality of locations on the 3D model and using the at least a processor, overlaying the map onto the 3D model. In one or more embodiments, the set of ultrasonic images of the organ includes an image selected from a list consisting of a transesophageal echocardiogram image, a transthoracic echocardiogram image and a point-of-care ultrasound image. In one or more embodiments, the method further includes identifying the training dataset, the method further includes training the shape identification model on the training dataset and identifying the training dataset includes correlating an instance of computed tomography scan data with a historical ultrasonic image as a function of a medical record and a language model. In one or more embodiments, the method further includes identifying the training dataset, the method further includes training the shape identification model on the training dataset and identifying the training dataset includes generating a synthetic ultrasonic image as a function of an instance of computed tomography scan data. In one or more embodiments, the method further includes determining a Left Atrial Appendage Occlusion Device placement as a function of the 3D model. In one or more embodiments, the set of shape parameters includes a plurality of numerical descriptors representing at least a geometric characteristic of the organ. In one or more embodiments, each shape parameter within the set of shape parameters is associated with a corresponding parameter range. In one or more embodiments, receiving the set of ultrasonic images includes receiving the set of ultrasonic images from a patient profile. In one or more embodiments, the map includes a color-coded heat map configured to visualize one or more areas of uncertainty on the 3D model. In one or more embodiments, generating the 3D model further includes generating a second 3D model as a function of the 3D model, by varying the set of shape parameters, wherein the second 3D model is statistically constrained. 24 Attorney Docket No.1518-103PCT1 In one or more embodiments, aspects of the present disclosure describe an apparatus that provides visualization within a three-dimensional (3D) model, the apparatus including at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a query image, extract neural network encodings as a function of the received query image, query a synthetic image repository for at least a matching synthetic image based on the extracted neural network encodings, wherein the synthetic image repository includes a plurality of synthetic images, wherein neural network encodings are extracted as a function of each synthetic image within the plurality of synthetic images, each synthetic image within the plurality of synthetic images corresponds to a region of interest in a 3D model and querying the synthetic image repository includes comparing the extracted neural network encodings of the query image with the extracted neural network encodings of each synthetic image within the plurality of synthetic images and display an estimated region of interest within the 3D model by positioning the query image as a function of the at least a matching synthetic image. In one or more embodiments, the query image includes a query intracardiac echocardiography (ICE) frame, and the plurality of synthetic images includes a plurality of synthetic ICE frames. In one or more embodiments, the 3D model is constructed based on a patient profile, wherein the patient profile includes a plurality of heart images and associated metadata. In one or more embodiments, the plurality of synthetic images is generated by executing a camera transformation program configured to simulate at least a perspective of an image capture device. In one or more embodiments, executing the camera transformation program includes generating at least a two-dimensional (2D) projection of a structure by rendering the region of interest as a function of a set of imaging parameters using a virtual camera positioned at the region of interest. In one or more embodiments, at least a synthetic image within the plurality of synthetic images is generated using a generative model. In one or more embodiments, generating the at least a synthetic image using a generative model includes receiving image translation training data including a plurality of training images and a plurality of training 2D projections. training an image translation model by correlating the 25 Attorney Docket No.1518-103PCT1 plurality of training images with the plurality of training 2D projections and synthesizing the at least a synthetic image as a function of the at least a 2D projection using the trained image translation model. In one or more embodiments, the 3D model is constructed from a plurality of computed tomography (CT) scans. In one or more embodiments, the 3D model is constructed using a plurality of magnetic resonance imaging (MRI) scans. In one or more embodiments, the 3D model is constructed using a plurality of transthoracic echocardiogram (TTE) frames. In one or more embodiments, displaying the estimated region of interest within the 3D model includes overlaying a two-dimensional (2D) cross section including the estimated region of interest of the query image within at least a portion of the 3D model. In one or more embodiments, the at least a processor is further configured to receive at least a supplemental query image and iteratively update the estimated region of interest as a function of the at least a supplemental query image. In one or more embodiments, aspects of the present disclosure describe a method that provides visualization within a 3D model, the method including receiving, by at least a processor, a query image, extracting, by the at least a processor, neural network encodings as a function of the received query image, querying, by the at least a processor, a synthetic image repository for at least a matching synthetic image based on the extracted neural network encodings, wherein the synthetic image repository includes a plurality of synthetic images, wherein neural network encodings are extracted as a function of each synthetic image within the plurality of synthetic images, each synthetic image within the plurality of synthetic images corresponds to a region of interest in a 3D model and querying the synthetic image repository includes comparing the extracted neural network encodings of the query image with extracted neural network encodings of each synthetic image within the plurality of synthetic images and displaying, by the at least a processor, an estimated region of interest within the 3D model by positioning the query image as a function of the at least a matching synthetic image. In one or more embodiments, the query image includes a query ICE frame and the plurality of synthetic images includes a plurality of synthetic ICE frames. 26 Attorney Docket No.1518-103PCT1 In one or more embodiments, the 3D model is constructed based on a patient profile, wherein the patient profile includes a plurality of heart images and associated metadata. In one or more embodiments, the plurality of synthetic images is generated by executing a camera transformation program configured to simulate at least a perspective of an image capture device. In one or more embodiments, executing the camera transformation program includes generating at least a two-dimensional (2D) projection of a structure by rendering the region of interest as a function of a set of imaging parameters using a virtual camera positioned at the region of interest. In one or more embodiments, at least a synthetic image within the plurality of synthetic images is generated using a generative model. In one or more embodiments, generating the at least a synthetic image using a generative model includes receiving image translation training data including a plurality of training images and a plurality of training 2D projections. training an image translation model by correlating the plurality of training images with the plurality of training 2D projections and synthesizing the at least a synthetic image as a function of the at least a 2D projection using the trained image translation model. In one or more embodiments, the 3D model is constructed using a plurality of computed tomography (CT) scans. In one or more embodiments, the 3D model is constructed using a plurality of magnetic resonance imaging (MRI) scans. In one or more embodiments, the 3D model is constructed using a plurality of transthoracic echocardiogram (TTE) frames. In one or more embodiments, displaying the estimated region of interest within the 3D model includes overlaying a 2D cross section including the estimated region of interest of the query image within at least a portion of the 3D model. In one or more embodiments, the method further includes receiving at least a supplemental query image and iteratively updating the estimated region of interest as a function of the at least a supplemental query image. The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features and advantages of the 27 Attorney Docket No.1518-103PCT1 subject matter described herein will be apparent from the description and drawings and from the claims. DESCRIPTION OF DRAWINGS For the purpose of illustrating the invention, the drawings show aspects of one or more embodiments of the invention. However, it should be understood that the present invention is not limited to the precise arrangements and instrumentalities shown in the drawings, wherein: FIG.1 is a block diagram of an exemplary embodiment of an apparatus for generating a three-dimensional (3D) model of an anatomical object via machine-learning; FIG.2 shows an exemplary embodiment of an ultrasonic image; FIG.3 is a flow diagram of an exemplary embodiment of an ICE image example generation process; FIG.4 illustrates an exemplary embodiment of a three-dimensional (3D) voxel occupancy representation; FIG.5 is a block diagram of an exemplary machine-learning process; FIG.6 is a diagram of an exemplary embodiment of a neural network; FIG.7 is a diagram of an exemplary embodiment of a node of a neural network; FIG.8 is a schematic diagram of a transesophageal echocardiogram procedure, according to some embodiments; FIG.9 is a flow diagram illustrating an exemplary embodiment of a method for generating a three-dimensional (3D) model of an anatomical object via machine-learning; FIG.10 is a flow diagram illustrating another exemplary embodiment of a method for generating a three-dimensional (3D) model of an anatomical object via machine-learning; FIG.11 is a block diagram of an exemplary embodiment of an apparatus for synthetizing FIG.12 is a flow diagram illustrating an exemplary embodiment of a method for synthetizing medical images; FIG.13 is a flow diagram illustrating another exemplary embodiment of a method for synthetizing medical images; FIG.14 is a block diagram of an exemplary embodiment of an apparatus for generating a three-dimensional (3D) model of patient’s organ via machine-learning; FIG.15 is a diagram illustrating an exemplary embodiment of an overlaid heat map; 28 Attorney Docket No.1518-103PCT1 FIG.16 is a diagram depicting an exemplary method of generating a three-dimensional (3D) model of a patient’s organ; FIG.17 is a flow diagram illustrating an exemplary embodiment of a method for generating a three-dimensional (3D) model of a patient’s organ with an overlay; FIG.18 is a flow diagram illustrating a method for generating a three-dimensional (3D) model of a patient’s organ, according to one exemplary embodiment; FIG.19 is a flow diagram illustrating a method for generating a three-dimensional (3D) model of a patient’s organ, according to another exemplary embodiment; FIG.20 is a block diagram of an exemplary embodiment of an apparatus for generating a three-dimensional (3D) model of a structure via machine-learning; FIG.21 is a flow diagram illustrating an exemplary embodiment of a method for generating a three-dimensional (3D) model of a structure with an overlay; FIG.22 is an exemplary embodiment of an apparatus that provides visualization within a three-dimensional (3D) model using neural networks; FIG.23A is a flow diagram of an exemplary embodiment for a synthetic image generation process; FIG.23B is an exemplary embodiment of a two-dimensional (2D) cross section overlaid within at least a portion of a 3D model, wherein the 2D cross section contains an estimated region of interest of a query image; FIG.24 is an exemplary flow diagram illustrating a method that provides visualization within a 3D model; and FIG.25 is a block diagram of a computing system that can be used to implement any one or more of the methodologies disclosed herein and any one or more portions thereof. The drawings are not necessarily to scale and may be illustrated by phantom lines, diagrammatic representations, and fragmentary views. In certain instances, details that are not necessary for an understanding of the embodiments or that render other details difficult to perceive may have been omitted. Like reference symbols in the various drawings indicate like elements. DETAILED DESCRIPTION At a high level, aspects of the present disclosure are directed to apparatus and methods for generating a 3D model of an anatomical object via machine-learning. 29 Attorney Docket No.1518-103PCT1 Aspects of the present disclosure can be used to simplify the ultrasound assisted anatomy reconstruction system by using an AI based algorithm to learn the positioning and 3D reconstruction directly from the ultrasound images. This is so, at least in part, because apparatus is configured to implement AI-based learning from CT datasets. In an embodiment, neural networks based estimation removes the need for complicated systems, such as manual segmentation and reconstruction methods, specialized hardware, FAM, among others. Aspects of the present disclosure can be used to avoid a LA-RA trans-septal puncture for the same of anatomical visualization. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples. In one or more embodiments, apparatus and methods described herein may involve one or more aspects of precise reconstructing of the left atrium (LA), pulmonary veins (PV), and left atrial appendage (LAA) in atrial fibrillation (AF) ablation. “Atrial fibrillation (AF),” as described herein, is a cardiac arrhythmia characterized by irregular and often rapid heart rate. In some cases, AF may lead to various complications, including, without limitation, stroke, heart failure, and/or the like. “AF ablation,” as described herein, is a procedure that aims to isolate and eliminate the abnormal electrical pathways causing the cardiac arrhythmia. LA, PV, and LAA are key structures involved in AF. In an embodiment, precise 3D reconstruction of LA, PV, and LAA may help in understanding their geometry and relationships which are essential for planning and/or executing AF ablation. In some cases, LA, PV, and LAA may be mapped in order to enable clinicians to identify one or more specific sites responsible for AF, allowing targeted ablation that minimizes damages to surrounding tissues. Additionally, or alternatively, apparatus and methods described herein may reduce the risk of complications such as, without limitation, perforation, stenosis, collateral damage, among others to adjacent structures. Apparatus and methods described herein may ensure ablation energy is delivered to the intended locations. Referring now to FIG.1, an exemplary embodiment of an apparatus 100 for generating 3D model of an anatomical object via machine-learning is illustrated. System includes at least a processor 104. Processor 104 may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Computing device may include, be included in, and/or communicate with a mobile device such as a mobile 30 Attorney Docket No.1518-103PCT1 telephone or smartphone. Processor 104 may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. Processor 104 may interface or communicate with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for connecting processor 104 to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software etc.) may be communicated to and/or from a computer and/or a computing device. Processor 104 may include but is not limited to, for example, a computing device or cluster of computing devices in a first location and a second computing device or cluster of computing devices in a second location. Processor 104 may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. Processor 104 may distribute one or more computing tasks as described below across a plurality of computing devices of computing device, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. Processor 104 may be implemented using a “shared nothing” architecture in which data is cached at the worker, in an embodiment, this may enable scalability of apparatus 100 and/or computing device. With continued reference to FIG.1, processor 104 may be designed and/or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, processor 104 may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed 31 Attorney Docket No.1518-103PCT1 iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Processor 104 may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing. With continued reference to FIG.1, apparatus includes a memory 108 communicatively connected to at least a processor 104, wherein the memory 108 contains instructions configuring at least a processor 104 to perform any processing steps described herein. As used in this disclosure, “communicatively connected” means connected by way of a connection, attachment or linkage between two or more relata which allows for reception and/or transmittance of information therebetween. For example, and without limitation, this connection may be wired or wireless, direct or indirect, and between two or more components, circuits, devices, systems, and the like, which allows for reception and/or transmittance of data and/or signal(s) therebetween. Data and/or signals therebetween may include, without limitation, electrical, electromagnetic, magnetic, video, audio, radio and microwave data and/or signals, combinations thereof, and the like, among others. A communicative connection may be achieved, for example and without limitation, through wired or wireless electronic, digital or analog, communication, either directly or by way of one or more intervening devices or components. Further, communicative connection may include electrically coupling or connecting at least an output of one device, component, or circuit to at least an input of another device, component, or circuit. For example, and without limitation, via a bus or other facility for intercommunication between elements of a computing device. Communicative connecting may also include indirect connections via, for example and without limitation, wireless connection, radio communication, low power wide area network, optical communication, magnetic, 32 Attorney Docket No.1518-103PCT1 capacitive, or optical coupling, and the like. In some instances, the terminology “communicatively coupled” may be used in place of communicatively connected in this disclosure. With continued reference to FIG.1, processor 104 may perform determinations, classification, and/or analysis steps, methods, processes, or the like as described in this disclosure using machine-learning processes. A “machine-learning process,” as used in this disclosure, is a process that automatedly uses a body of data known as “training data” and/or a “training set” (described further below in this disclosure) to generate an algorithm that will be performed by a processor 104/module to produce outputs given data provided as inputs; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. Machine-learning process may utilize supervised, unsupervised, lazy-learning processes and/or neural networks, described further below. With continued reference to FIG.1, processor is configured to receive a set of images 112 of an anatomical object 116 pertaining to a subject 120. As used in this disclosure, a “set of images” refers to a collection or group of visual representations captured using any imaging modality or technique described herein. Set of images 112 may include, without limitation, two-dimensional images. In an embodiment, set of images 112 may include a set of intracardiac echocardiography (ICE) images, wherein the "set of ultrasonic images” is a collection of ultrasound images obtained from within the heart’s chambers or blood vessels. In some cases, ultrasonic images may be captured using a specialized catheter equipped with an ultrasound transducer that is inserted into the body and guided to the heart of subject 120. In one or more embodiments, set of images 112 may include images received from one or more ultrasonic imaging devices. For example, and without limitation, set of images 112 may include images received from an intravascular ultrasound (IVUS), from a doppler ultrasound and the like. In one or more embodiments, sets of images 112 may include ultrasonic images wherein, the “ultrasonic images” are a collection of images received from one or more ultrasonic devices. In one or more embodiments, set of images 112 may include a set of transthoracic echocardiogram (TTE) images. A “Transthoracic echocardiogram image” for the purposes of this disclosure is an image received from an ultrasound device known as an echocardiograph. In one more embodiments, TTE may include a noninvasive process for ultrasonic imaging. In one or 33 Attorney Docket No.1518-103PCT1 more embodiments, TE images may include a two dimensional view of the structures of an individual’s organs. In one or more embodiments, TTE images may be used to assess cardiac function, assess the function of heart valves, detect fluids around the heart, detect heart diseases and the like. In one or more embodiments, generation of TTE image may include the user of a probe configured to emit ultrasonic waves, and an ultrasonic device configured to process the ultrasonic signals received from the probe and generate TTE images. In one or more embodiments, sets of images may include a set of transesophageal echocardiogram (TEE) images. A “transesophageal echocardiogram image” for the purposes of this disclosure is an imaging received from a transesophageal echocardiogram device. In one or more embodiments, transesophageal echocardiography includes the process of inserting a transducer down an individual’s throat in order to receive detailed images of the hearts structure. In or more embodiments, TEE images may capture heart valves, the left atrium, appendages, the aorta and the like. In one or more embodiments, TEE may include a two dimension image of an individual’s heart and various organs near the individual’s heart. In one or more embodiments, TEE images may be used following and/or prior to surgery wherein TEE images may be used to evaluate the structure of an individual’s heart. In one or more embodiments, TEE images may be received by a TEE probe, wherein the TEE probe includes a flexible probe with a transducer configured to be swallowed and positioned in the esophagus. In one or more embodiments, an ultrasound machine may receive signals from the TEE probe and generate images form the signals. In one or more embodiments, sets of images 112 may include images received from one or more ultrasonic devices. This may include, but is not limited to, portable ultrasonic devices, such as point of are devices, echocardiography devise, endoscopic ultrasound devices, elastography devices, high frequency ultrasound devices and the like. In one or more embodiments, sets of images 112 may include images received from abdominal ultrasounds. Gynecologic ultrasound, musculoskeletal ultrasound, vascular ultrasounds, breast ultrasounds and/or any ultrasonic image captured of an individual. In an embodiments, sets of images may provide a detailed and real-time visualization of an anatomical object 116. An “anatomical object” for the purposes of this disclosure refers to any portion of an individual’s body. For example, and without limitation, anatomical object 116 may include a heart, tissue, organs, bones, muscles, limbs, blood nerves and the like. In one or more embodiments, anatomical object 116 may include an organ such as the heart, the liver, the appendix, the brain and the like. In one 34 Attorney Docket No.1518-103PCT1 or more embodiments, anatomical object may include a blood vessel and/or set of blood vessels. In one or more embodiments, anatomical object 116 may include any portions of human’s and/or organism’s body. In one or more embodiments, sets of images 112 may provide detailed and real-time visualization of anatomical objects 116. In one or more embodiments, detailed visualizations may include the structure of organs, the structure of limbs, the structure of muscles and the like. In one or more embodiments, anatomical object 116 may include cardiac anatomy. In an embodiment, set of images 112 may provide a detailed and real-time visualizations of “cardiac anatomy,” which refers to the structural composition of the heart and its associated blood vessels. Set of images 112 may also include internal structures, functions, and bold flow patterns of the heart of subject 120. Other exemplary embodiments of set of images 112 may include, without limitation, X-ray images, magnetic resonance imaging (MRI) scans, computed tomography (CT) scans, ultrasound images, optical images, digital photographs, or any other form of visual data. Additionally, images within set of images 112 may be related in terms of content, time of capture, sequence, or any other relevant parameters described herein. In a non- limiting example, each image of set of images 112 may represent a particular view, angle, or perspective of an object, subject, or scene, and may be in two-dimensional (2D) or 3D format. Images of set of images 112 may include, without limitation, any two-dimensional or three- dimensional images of any anatomy or anatomical structure, including without limitation images of any internal organ, tissue including without limitation muscular, connective tissue, epithelial tissue, and/or nervous tissue, bone, and/or any other element that may be imaged within a human and/or animal body. In one or more embodiments, sets of images 112 may include an organ model. For the purposes of this disclosure, an “organ model” is a digital representation of an organ, capturing its anatomy, geometry, and potentially functional properties. As a non-limiting example, organ model may digitally represent a heart, lung, liver, kidney, pancreas, stomach, intestines, or the like. In some cases, organ model may digitally represent an organ of a human or any individual organism, such as without limitation, a dog, rat, or the like. In one or more embodiments, organ model may include a digital representation of anatomical object 116. In one or more embodiments, apparatus may include any organ model, method of generating an organ model, or method of locating an electrode as disclosed in this disclosure. With continued reference to FIG.1, sets of images 112 may include at least a medical image. For the purposes of this disclosure, a “medical image” is a two-dimensional 35 Attorney Docket No.1518-103PCT1 visual representation containing information pertaining to an interior of a body and functions of organs/tissues therein that may aid clinical analysis and medical intervention. Medical image may include, without limitation, X-ray image, echocardiogram (ECG), magnetic resonance imaging (MRI) scan, computed tomography (CT) scan, ultrasound image including intracardiac echocardiogram (ICE) frame, transthoracic echocardiogram (TTE) frame, magnetic resonance imaging (MRI) scan, and/or transesophageal echocardiogram (TEE) frame, optical image, digital photograph, and/or the like. For the purposes of this disclosure, computed tomography (CT) is a medical imaging technique that uses X-rays to capture cross-sectional images (slices) of a patient’s body; by taking a plurality of slices, a CT scan creates a detailed three-dimensional (3D) representation of internal structures. For the purposes of this disclosure, an “ICE frame” is a 2D ultrasound image that represents anatomy (i.e., walls, chambers, blood vessels, etc.) of at least part of a heart, as described above. For the purposes of this disclosure, a “transthoracic echocardiogram (TTE) frame” is a two-dimensional (2D) ultrasound image collected by placing a probe or ultrasound transducer on patient’s chest or abdomen to collect various views of heart. For the purposes of this disclosure, a “transesophageal echocardiogram (TEE) frame” is a 2D ultrasound image collected by passing a specialized probe containing an ultrasound transducer at its tip into patient’s esophagus; it is an alternative way of performing echocardiography. For the purposes of this disclosure, “echocardiography” is an imaging technique that uses ultrasound to examine heart, the resulting visual image of which is an echocardiogram. Anatomical structures may include, without limitation, chambers (e.g., four chambers including left and right atria and left and right ventricles), valves (i.e., the structures that regulate blood flow between chambers and vessels, including mitral, tricuspid, aortic, and pulmonary valves), vessels (e.g., aorta, pulmonary arteries and veins, and coronary arteries), conduction system (i.e., a network of specialized cells that controls the heart’s electrical activity and rhythm), muscular and connective tissues (e.g., heart’s muscular walls, septa, any other connective tissues that provide structural integrity and enable contraction), left atrial appendage and other appendages, pathological features (e.g., any abnormalities, defects, and/or the like), among others. ICE frame may be either collected and/or recorded by a medical professional using an image capture device, such as an ICE catheter. In one or more embodiments, medical image may be saved to and/or retrieved later from a patient profile and/or a database. In one or more embodiments, sets of images 112 may include any query images as disclosed in this disclosure. 36 Attorney Docket No.1518-103PCT1 Still referring to FIG.1, in a non-limiting example, anatomical object 116 and/or cardiac anatomy may include chambers (e.g., four chambers including left and right atria and left and right ventricles), valves (i.e., the structures that regulate blood flow between chambers and vessels, including mitral, tricuspid, aortic, and pulmonary valves), vessels (e.g., aorta, pulmonary arteries and veins, and coronary arteries), conduction system (i.e., a network of specialized cells that control the heart’s electrical activity and rhythm), muscular and connective tissues (e.g., heart’s muscular walls, septa, any other connective tissues that provide structural integrity and enable contraction), LAA and other appendages, pathological features (e.g., any abnormalities, defects, and/or the like), among others. Still referring to FIG.1, as used in this disclosure, a “subject” refers to an individual organism. In an embodiment, subject 120 may include a human, on whom or on which the procedure, study, or otherwise experiment, such as without limitation, AF ablation described herein, is being conducted. In some cases, subject 120 may include a provider of set of images 112 described herein. In other cases, subject 120 may include a recipient or a participant in a clinical trial or research study. In a non-limiting example, subject 120 may include a human patient with AF who is undergoing a procedure, an individual undergoing cardiac screening, a participant in a clinical trial, patient with congenital heart disease, heart transplant candidate, patient receiving follow-up care after cardiac surgery, healthy volunteer, patient with heart failure, or the like. Additionally, or alternatively, subject 120 may include an animal models (i.e., animal used to model AF such as a laboratory rat). Still referring to FIG.1, in an embodiment, each ultrasonic image of set of ultrasonic images may include a particular view of subject’s 120 heart’s chambers, valves, vessel, anatomical structure and/or the like. In a non-limiting example, set of images 112 may include multiple views e.g., different angles and perspectives of subject’s 120 heart, organs and/or the like. In another embodiment, set of images 112 may be arranged in a temporal sequence. In a non-limiting example, set of images 112 may include a series of images captured over time, allowing for an observation of dynamic cardiac functions such as beating, blood flow, and/or the like. In some cases, each ultrasonic image of set of images 112 may include a corresponding timestamp, wherein the timestamp may include an indicator showing a date and time of when the corresponding ultrasonic image was taken. 37 Attorney Docket No.1518-103PCT1 Additionally, or alternatively, and still referring to FIG.1, various imaging techniques or settings may be applied to set of images 112 that provide specific insights into anatomical object 116. In some cases, anatomical object 116 may include a plurality of physical characteristics, spatial relationships, and function aspects of the heart’s component; for instance, and without limitation, receiving set of images 112 may include applying a doppler imaging technique, wherein the “doppler imaging technique” is a specialized ultrasound technique used to assess the movement of blood within the body, particularly within the heart and blood vessels. Processor 104 may configure a transducer to send high-frequency sound waves into the subject’s 120 body, wherein the sound waves may bounce off moving blood cells and other structures. Reflected waves may then be picked up by the transducer and frequency of the reflected waves changes (Doppler shift) depending on the speed and direction of blood flow may be analyzed to determine one or more blood flow characteristics. In some cases, one or more ultrasonic images within set of images 112 may include visual representations translated based on one or more blood flow characteristics. Such visual representations may be further color-coded, showing the speed and direction of blood flow. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will aware other exemplary modalities of ICE and/or ultrasonic imaging such as, without limitation, computed tomography (CT) scans, magnetic resonance imaging MRI, positron emission tomography (PET) scan, angiography, electrocardiogram (ECG or EKG), single-photon emission computed tomography (SPECT), optical coherence tomography (OCT), thermography, tactile imaging, and/or the like. With continued reference to FIG.1, in one or more embodiments, receiving set of images 112 of anatomical object 116 may include receiving a patient profile pertaining to subject 120. As used in this disclosure, a “patient profile” is a comprehensive collection of information related to an individual patient. In some cases, patient profile may include a variety of different types of data that, when combined, provide a detailed picture of a patient's overall health. In an embodiment, patient profile may include demographic data of patient, for example, and without limitation, patient profile may include basic information about the patient such as name, age, gender, ethnicity, socioeconomic status, and/or the like. In another embodiment, each patient profile may also include a patient’s medical history, for example, and without limitation, patient profile may include a detailed record of the patient's past health conditions, medical procedures, hospitalizations, and illnesses such as surgeries, treatments, medications, and/or the like. In 38 Attorney Docket No.1518-103PCT1 another embodiment, each patient profile may include lifestyle Information of patient, for example, and without limitation, patient profile may include details about the patient's diet, exercise habits, smoking and alcohol consumption, and other behaviors that could impact health. In a further embodiment, patient profile may include patient’s family history, for example, and without limitation, patient profile may include a record of hereditary diseases. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various type of data within patient profiles apparatus 100 may receive and process in consistent with this disclosure. In a non-limiting example, and still referring to FIG.1, patient profile may include one or more ultrasonic images or set of images 112. Receiving set of images 112 may include extracting set of images 112 from patient profile (subsequent to patient identity verification and obtaining consent from subject 120). In some cases, patient profile of subject 120 may be obtained through hospital information system (HIS) or any other data acquisition platform to securely access patient’s electronic medical record (EMR) or other relevant databases. Set of images 112 may be directly or indirectly downloaded or exported. In some cases, each ultrasonic image of set of images 112 may be in a usable and/or computer-readable format such as, without limitation, DICOM format, and necessary metadata (e.g., patient information described above) may be included. Further, receiving set of images 112 may include recording the access and extraction of set of images 112; for instance, and without limitation, this process may be documented, by processor 104, in the patient’s/subject’s 120 medical record, databases, or other appropriate logs. Further, and still referring to FIG.1, in other embodiments, patient profile may include electrocardiogram (ECG) data, wherein the “ECG data,” for the purpose of this disclosure, refers to data related to an electrocardiogram of the patient that corresponds to the patient profile. A “electrocardiogram,” as described herein, is a medical test that records the electrical activity of subject’s heart over a period of time. In an embodiment, ECG data may include one or more recordings captured by a plurality of electrodes placed on patient’s skin. In one or more embodiments, ECG data may include information regarding a P wave, T wave, QRS complex, PR interval, ST segment, and/or the like. Processor 104 may associate set of images 112 with ECG data, or in other cases, receiving set of images 112 may include receiving ECG data pertaining to subject 120 associated with set of images 112. Such ECG data may be 39 Attorney Docket No.1518-103PCT1 collected simultaneously during ICE imaging. In some cases, set of images 112 may be linked with ECG data by one or more unique identifiers, such as without limitations, timestamps or other metadata described herein. In a non-limiting example, ECG data may be used to identify specific cardiac events or phases of the cardiac cycle, and the corresponding ultrasonic images may be analyzed to see how heart’s structure changes during those times. Patient profile and ECG data described herein may be consistent with any patient profile and ECG data disclosed in this disclosure. With continued reference to FIG.1, in other embodiments, receiving set of images 112 may include receiving set of ultrasonic images from an image database 124. In some cases, Image database 124 may be implemented, without limitation, as a relational database, a key- value retrieval database such as a NOSQL database, or any other format or structure for use as a database that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Image database 124 may alternatively or additionally be implemented using a distributed data storage protocol and/or data structure, such as a distributed hash table or the like. Image database 124 may include a plurality of data entries and/or records as described above. Data entries in Image database 124 database may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in Image database 124 or another relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a database may store, retrieve, organize, and/or reflect data and/or records as used herein, as well as categories and/or populations of data consistently with this disclosure. In a further embodiment, and still referring to FIG.1, receiving set of images 112 may involve one or more image preprocessing steps. In some cases, processor 104 may be configured to calibrate one or more ultrasonic images of set of images 112 by correct for distortions and ensure accurate spatial representation of anatomical object 116 pertaining to subject 120. In a non-limiting example, processor 104 may select one or more reference objects within ultrasonic image that needs calibration to correct spatial distortions. In some cases, processor 104 may be configured to place a phantom with pre-determine dimensions in such ultrasonic image and adjust ultrasonic image until the phantom’s dimensions are accurately represented. In another non-limiting example, one or more ultrasonic images’ brightness and 40 Attorney Docket No.1518-103PCT1 contrast may be adjusted, by processor 104 to ensure that echogenicity (reflectivity) of the tissues is accurately represented. One or more tissues with known echogenicity may be selected by processor 104 as reference tissues to adjust corresponding portions of the one or more ultrasonic images. In other cases, standardized correction curves may be applied in or der to correct the echogenicity of ultrasonic images. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, may be aware of various calibration techniques, such as, without limitation, temporal calibration, geometric calibration, among others that can be used by processor 104 to preprocess set of images 112. Additionally, or alternatively, and still referring to FIG.1, receiving set of images 112 may include perform image segmentation on or more ultrasonic images of set of images 112. In some cases, image segmentation may include separating specific structures or regions of interest (ROI) from the background or other structures in a given ultrasonic image. In a non- limiting example, processor 104 may be configured to use edge detection algorithms to outline the heart chambers, separating them from surrounding tissues. One or more filters may be applied to highlight the boundaries between different types of tissues during the segmentation. In another non-limiting examples, valves and vessels may also be segmented by applying thresholding techniques. Processor 104 may be configured to set an intensity threshold based on the known echogenicity of blood and vessel walls and select pixels or regions having intensity below or above the intensity threshold from the given ultrasonic image. In some cases, one or more machine learning models may be used to perform image segmentations, for example, and without limitation, a U-net (i.e., a convolution neural network containing a contracting path as an encoder and an expansive path as a decoder, wherein the encoder and the decoder forms a U- shaped structure). With continued reference to FIG.1, processor 104 is configured to generate a 3D data structure 128 representing anatomical object 116 as a function of set of images 112. In a non-limiting example, 3D data structure 128 may include a 3D voxel occupancy representation (VOR). As used in this disclosure, a "3D voxel occupancy representation (VOR)" of an anatomical object is a 3D digital representation of a spatial structure of the anatomical object, wherein the representation is composed of a plurality of discrete volumetric elements known as voxels 132. A “voxel,” for the purpose of this disclosure, is a 3D equivalent of a pixel in 2D imaging. While a pixel represents a point in a 2D image and may include properties such as color 41 Attorney Docket No.1518-103PCT1 and/or brightness, a voxel may represent a volume in a 3D space and may include additional properties such density/occupancy as described below. In an embodiment, each voxel of plurality of voxels 132 within 3D VOR may represent a specific portion of anatomical object 116. In some cases, voxel may be a smallest distinguishable box-shaped part (i.e., 1px ^1px ^1px) of a three-dimensional image. In some cases, each voxel of plurality of voxels 132 within VOR may be represented as a cube or rectangular prism (although other shapes may be used in specialized applications). Each voxel may include a size that determines a resolution of the 3D image or model. In an embodiment, smaller voxels may provide higher resolution; however, it may require more computational resources (e.g., RAM) for processor 104 to process. In an embodiment, and still referring to FIG.1, each voxel of plurality of voxels 132 within VOR may include one or more embedded values. As used herein, “embedded values” refers to specific numerical or categorical data associated with each voxel. In some cases, embedded values may represent various attributes or characteristics of the corresponding portion of anatomical object 116 that voxel represents. In a non-limiting example, embedded values may include density values, intensity values, texture information, or any other quantitative measures that provide insights into the underlying cardiac tissue. Such embedded values may be derived from set of ultrasonic images or other imaging modalities used to generate data structure 128. In some cases, embedded values may be utilized, by processor 104, to differentiate between different types of cardiac tissues, such as myocardial tissue, blood vessels, or chambers. Embedded values may also facilitate the visualization of dynamic cardiac functions, for example, and without limitation, blood flow or heart beating by encoding temporal information such as timestamps within plurality of voxels 132. Still referring to FIG.1, in an embodiment, each voxel of plurality of voxels 132 may include a presence indicator 136. As used in this disclosure, a “presence indicator” refers to a data element that indicates a presence or absence (i.e., occupancy) of cardiac tissue within that portion. In some cases, and without limitation, presence indicator 136 may include an occupancy status as one of the embedded values described herein. Portion may include a specific location within 3D space where data structure 128 is generated; for instance, and without limitation, a coordinate in 3D space represented in a tuple such as (x, y, z). In an embodiment, 3D VOR may provide a spatial framework that allows for the modeling and visualization of anatomical object 116 in 3D space. In some cases, 3D data structure 128 may include a plurality of layers or slices 42 Attorney Docket No.1518-103PCT1 (either horizontal [e.g., xy plane] or vertical [e.g., xz or yz plane depends on the view direction]), wherein each layer or slices of the plurality of layers or slices is corresponding to a different cross-sectional view of subject’s 120 heart, and collectively forming a comprehensive 3D depiction of the cardiac structure. In a non-limiting example, 3D VOR having plurality of voxels 132 with presence indicators 136 may indicate whether each voxel in 3D space may be occupied by a part of subject’s 120 heart. A binary value such as 0 or 1 may be configured as presence indicator to show ether a pixel of 3D space is occupied (e.g., 1) or empty (e.g., 0). In should be noted that other values may be used as presence indicator 136 such as a Boolean value e.g., TRUE or FALSE. In some cases, and still reference to FIG.1, one or more embedded values, such as, without limitations, occupancy, or density, may be derived from set of images 112 described herein by processor 104. In a non-limiting example, determining occupancy status of each voxel of plurality of voxels 132 may include converting set of ultrasonic images to a set of binary images and determining occupancy status of each voxel as a function of the structure of interest’s binary value. In some cases, occupancy status may include a value representing the likelihood of occupancy of the corresponding heart tissue. In another non-limiting example, density may be calculated, by processor 104, for each voxel as a function of the echogenicity of one or more pixels on a given ultrasonic image, wherein, the brightness of the given ultrasonic image may be analyzed since different tissues reflect ultrasound waves differently. With continued reference to FIG.1, generating 3D data structure 128 of anatomical object 116 may include generating a 3D array. In some cases, processor 104 may divide 3D space into a grid of plurality of voxels 132, each with specific x, y, and z coordinates as embedded values. Each element of 3D array may correspond to a voxel. In some cases, 3D array may allow for easy access and manipulation of plurality of voxels 132, enabling various analyses, visualizations, and transformations either described or not described herein. In a non- limiting example, embedded values may include a density of the tissue at a specific location of a patient’s body derived from one or more ultrasonic images of set of images 112. Additionally, or alternatively, and still referring to FIG.1, 3D data structure 128 of anatomical object 116 may include a 3D grid configured to map presence indicators 136 and/or other embedded values described herein of plurality of voxels 132 (e.g., tissue density, blood flow velocity, echogenicity or acoustic properties, and any other biophysical properties). As used 43 Attorney Docket No.1518-103PCT1 in this disclosure, a “3D grid” refers to a 3D data structure that divides a given volume (e.g., volume of a heart) into a plurality of discrete units called cells (i.e., volume elements). In an embodiment, each cell within 3D grid may be associated with a distinct voxel. Mapping presence indicators 136 or other embedded values may include assigning each presence indicator or embedded value to each points within 3D grid such as corners of each corresponding cell. Such values may be derived from set of images 112 as described above. In yet another embodiment, and still referring to FIG.1, cells may be continuous, meaning that one or more cells may represent one or more continuous regions of space rather than discreate, separate units. In a non-limiting example, instead of being uniform, mapped presence indicator and/or other embedded values may vary continuously across different cells or cell’s volume. In such embodiment, processor 104 may use interpolation to estimate other (unknown) embedded values within a range based on existing values such as known embedded values at specific points, thereby allowing for smooth transitions between cells. Exemplary interpolation methods may include, without limitation, linear interpolation, cubic interpolation, and/or the like. For example, and without limitation, if the corners of a cell have known values interpolation can be used to estimate the values at any point within the cell based on those corner values. In a non-limiting example, and still referring to FIG.1, 3D data structure 128 of anatomical object 116 may include a 3D grid having a plurality of cells e.g., voxels, wherein each cell may contain a continuous range of values representing tissue density, blood flow velocity, or other properties (i.e., embedded values). Processor 104 may be configured to apply trilinear or tricubic interpolation to estimate tissue density within each cell based on presence indicator or other known values at the cell’s boundaries, since tissue densities change gradually; Such 3D grid may provide a smooth, continuous representation of heat’s internal structures, allowing for more nuanced analysis and visualization as described below. In a further embodiment, 3D grid with continuous cells may be additionally used in fluid dynamics simulations. With continued reference to FIG.1, in some case, presence indicators 136 and/or other embedded values may be mapped to 3D grid as a function of array masking, wherein specific array or grid may be selected to modify based on one or more pre-defined criteria. In a non-limiting example, processor 104 may generate a mask e.g., a binary array that defines which 44 Attorney Docket No.1518-103PCT1 voxels or cells are affected. Mask may be used to select or modify specific voxels or cells based on certain attributes; for instance, and without limitation, processor 104 may use mask to isolate the LA within the heart focusing the analysis on that specific region. Such mask may include a criteria defined by specific density thresholds that distinguish the LA’s tissue (i.e., voxels representing LA in 3D grid) from surrounding structures (i.e., neighboring voxels). In some cases, such mask may further include a binary mask, wherein each voxel in the 3D gird may be assigned a first presence indicator such as 1 if the voxel meets the criteria for the LA and a second presence indicator such as 0 if it does not. In some embodiments, mask may be directly applied to 3D grid, selecting, or modifying voxels or cells, thereby enabling processor 104 to highlight, exclude, or otherwise manipulate specific parts of anatomical object 116 within 3D grid. Processor 104 may then perform an element-wise multiplication between 3D grid and the mask. Continuing from the previous non-limiting example, voxels corresponding to the LA (wherein the mask value is 1) may retain their original values, while other voxels (where the mask value is 0) may be set to 0 or other specific value (i.e., excluded or masked out). With continued reference to FIG.1, in some embodiments, 3D grid may include one or more spatial features 140 extracted from set of images 112 of anatomical object 116. As used in this disclosure, “spatial features” are specific characteristics or attributes related to the spatial arrangement, shape, size, texture, or orientation of structures within a 3D space. In some cases, spatial features may include one or more embedded values described herein and their combinations thereof. In a non-limiting example, spatial feature may be represented numerically as a vector, a metric or other mathematical constructs that capture specific spatial characteristics. In some cases, spatial features 140 may also be visualized as contours, surfaces, or other geometric representations. In an embodiment, spatial features 140 may be extracted using edge detection, texture analysis, or other image processing techniques (e.g., cleaning and enhancing images, image segmentation, and/or the like). In another embodiment, one or more machine learning models, such as convolutional neural networks (CNNs) as described in further detail below, may be used to extract complex spatial features 140. Still referring to FIG.1, as used in this disclosure, a “vector” is a data structure that represents one or more a quantitative values and/or measures of one or more spatial features 140. A vector may be represented as an n-tuple of values, where n is one or more values, as described in further detail below; a vector may alternatively or additionally be represented as an element of 45 Attorney Docket No.1518-103PCT1 a vector space, defined as a set of mathematical objects that can be added together under an operation of addition following properties of associativity, commutativity, existence of an identity element, and existence of an inverse element for each vector, and can be multiplied by scalar values under an operation of scalar multiplication compatible with field multiplication, and that has an identity element is distributive with respect to vector addition, and is distributive with respect to field addition. Each value of n-tuple of values may represent a measurement or other quantitative value associated with a given category of data, or attribute, examples of which are provided in further detail below; a vector may be represented, without limitation, in n- dimensional space using an axis per category of value represented in n-tuple of values, such that a vector has a geometric direction characterizing the relative quantities of attributes in the n-tuple as compared to each other. Two vectors may be considered equivalent where their directions, and/or the relative quantities of values within each vector as compared to each other, are the same; thus, as a non-limiting example, a vector represented as [5, 10, 15] may be treated as equivalent, for purposes of this disclosure, as a vector represented as [1, 2, 3]. Vectors may be more similar where their directions are more similar, and more different where their directions are more divergent, for instance as measured using cosine similarity as computed using a dot product of two vectors; however, vector similarity may alternatively or additionally be determined using averages of similarities between like attributes, or any other measure of similarity suitable for any n-tuple of values, or aggregation of numerical similarity measures for the purposes of loss functions as described in further detail below. Any vectors as described herein may be scaled, such that each vector represents each attribute along an equivalent scale of values. Each vector may be “normalized,” or divided by a “length” attribute, such as a length attribute l as derived using a Pythagorean norm: ^^ ൌ ^^{∑^} ^_ୀ^ ^^_^ ^ଶ , where ai is attribute number i of the vector. Scaling and/or normalization may function to make vector comparison independent of absolute quantities of attributes, while preserving any dependency on similarity of attributes. Still referring to FIG.1, in a non-limiting example, one or more spatial features 140 may include one or more shape features (i.e., characteristics related to the shape of specific cardiac structures), such as curvature, surface area, volume, and/or the like. In another non- limiting example, one or more spatial features 140 may include one or more texture features (i.e., characteristics related to the texture or pattern within cardiac tissues, as seen set of images 112), such as gray-level co-occurrence matrix (GLCM) features representing the texture of heart 46 Attorney Docket No.1518-103PCT1 muscle tissue. In another non-limiting example, one or more spatial features 140 may include one or more orientation features (i.e., characteristics related to the orientation or alignment of cardiac structures), such as the angle or alignment of the septum within the heart. In a further non-limiting example, one or more spatial features 140 may include one or more edge and boundary features (i.e., Characteristics related to the edges or boundaries between different cardiac structures or tissues), such as edge detection features highlighting the boundary between the myocardium and the cardiac chambers. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various spatial features extracted from set of images 112 in consistent with this disclosure. With continued reference to FIG.1, in some cases, 3D data structure 128 may be received from a statical shape model. As used in this disclosure, a “statistical shape model (SSM)” is a data structure representing, including, and/or utilizing a mathematical model that captures principal modes of variation in shape across a population of cardiac anatomies. SSM captures a plurality of heart models associated with a plurality of patients. In some cases, SSM may be used to capture the variability in anatomical structures among different patients; for instance, SSM of the human heart may be constructed from a plurality of heart images of a plurality of individuals. In some cases, 3D data structure 128 generated by SSM may capture the “average” heart shape and main ways in which heart shapes may vary among the plurality of individuals. In one or more embodiments, 3D data structure 128 generated by SSM may capture the “average” of the plurality of anatomical objects in which anatomical objects may vary among plurality of individuals. In some cases, SSM may be generated by processor as a function of a set of labeled example shapes, each in a form of point-based representations or meshes. In some cases, example shapes may be represented in a 3D voxel occupancy representation (VOR). In one or more embodiments, 3D data structure 128 may be generated in any way similar to that of heart model as disclosed in this disclosure. With continued reference to FIG.1, in some embodiments, apparatus 100 may include a computer vision model 144 configured to generate 3D data structure 128 of anatomical object 116. A “computer vision model,” for the purpose of this disclosure, is a computation model designed to interpret and make determinations based on visual data. In an embodiment, computer vision model 144 may process set of images 112, to make a determination about a scene, space, and/or object in anatomical object 116. In a non-limiting example, computer vision 47 Attorney Docket No.1518-103PCT1 model 144 may be used for registration of plurality of voxels 132 within a 3D space. In some cases, registration may include image processing described herein, such as without limitation object recognition, feature detection, edge/corner detection, and the like. Non-limiting example of feature detection may include scale invariant feature transform (SIFT), Canny edge detection, Shi Tomasi corner detection, and the like. In some cases, registration may include one or more transformations to orient an ultrasonic image relative a 3D coordinate system; exemplary transformations include without limitation homography transforms and affine transforms. In an embodiment, registration of ultrasonic image to a coordinate system may be verified and/or corrected using object identification and/or computer vision, as described above. For instance, and without limitation, an initial registration to two dimensions, represented for instance as registration to the x and y coordinates, may be performed using a two-dimensional projection of points in three dimensions onto the ultrasonic image; however, a third dimension of registration, representing depth and/or a z axis, may be detected by utilizing depth-sensing techniques such as Doppler imaging. Alternatively, the third dimension may be inferred from the known geometry and orientation of the imaging device (e.g., ICE catheter), or through the application of one or more machine learning models trained to interpret depth from the two-dimensional projection. With continued reference to FIG.1, processor 104 may use a machine learning module 148 to implement one or more algorithms or generate one or more machine learning models, such as an anatomy modeling model 152 to generate 3d data structure 128 of anatomical object 116. However, the machine learning module is exemplary and may not be necessary to generate one or more machine learning models and perform any machine learning described herein. In one or more embodiments, one or more machine-learning models may be generated using training data. Training data may include inputs and corresponding predetermined outputs so that a machine-learning model may use correlations between the provided exemplary inputs and outputs to develop an algorithm and/or relationship that then allows machine-learning model to determine its own outputs for inputs. Training data may contain correlations that a machine- learning process may use to model relationships between two or more categories of data elements. Exemplary inputs and outputs may come from a database, such as any database described in this disclosure, or be provided by a user. In other embodiments, a machine-learning module may obtain a training set by querying a communicatively connected database that includes past inputs and outputs. Training data may include inputs from various types of 48 Attorney Docket No.1518-103PCT1 databases, resources, and/or user inputs and outputs correlated to each of those inputs so that a machine-learning model may determine an output. Correlations may indicate causative and/or predictive links between data, which may be modeled as relationships, such as mathematical relationships, by machine-learning models, as described in further detail below. In one or more embodiments, training data may be formatted and/or organized by categories of data elements by, for example, associating data elements with one or more descriptors corresponding to categories of data elements. As a non-limiting example, training data may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data may be linked to descriptors of categories by tags, tokens, or other data elements. In a further embodiment, training data may include previous outputs such that one or more machine learning models iteratively produces outputs. Still referring to FIG.1, machine learning module 148 may be used to generate anatomy modeling model and/or any other machine learning models, such as, shape identification model as described in further detail below, using training data. Anatomy modeling model 152 may be trained by correlated inputs and outputs of training data. Training data may be data sets that have already been converted from raw data whether manually, by machine, or any other method. In an embodiment, generating data structure 128 of anatomical object 116 includes receiving anatomy training data 156, wherein the anatomy training data 156 may include a plurality of image sets as input and a plurality of computed tomography (CT) based anatomical object models as output, and wherein each image set of plurality of image sets may include any images described in this disclosure. In some cases, anatomy training data 156 may be received from Image database 124 or other databases. In other cases, anatomy training data 156 may be collected by a data acquisition unit from external sources such as one or more medical equipment’s e.g., imaging devices or diagnostic tools, wherein the data acquisition may be configured as an intermediary between the data source and machine learning module 148. In one or more embodiments, anatomy training data 156 may include a plurality of set of images correlated to a plurality of anatomical models. An “anatomical model” for the purposes of this disclosure refers to a 3D representation of anatomical object. In one or more embodiments, a particular set of images 112 within anatomy training data 156 may be correlated to a particular anatomical model. In one or more embodiments, anatomy training data may 49 Attorney Docket No.1518-103PCT1 further include a plurality of ECG data and sets of images 112 correlated to a plurality of anatomical models. In an embodiment, a particular set of images 112 and a particular ECG data may be correlated to a particular anatomical model. In one or more embodiments anatomy training data may include intracardiac echo diagrams, Cardiac CTs, ECG signals and/or ultrasonic images as an input and correlated 3D representations of anatomical objects. In one or more embodiments, machine learning module and/or anatomy modeling model 152 may include a multimodal model configured to receive multiple simultaneous inputs and produce an output. A “multimodal model” for the purposes of this disclosure is a machine learning model configured to receive combined inputs from differing modalities and provide an output. For example, and without limitation, multimodal model may receive both text and/or images as an input and generate an output. In one or more embodiments, multimodal model may include a machine learning model configured to receive inputs from differing modalities. In one or more embodiments, multimodal mode may include a machine learning model configured to receive multiple inputs from different modalities simultaneously in order to generate an output. In one or more embodiments, multimodal model may receive ECG data from patient profile as an input and/or sets of images 112 as an input and output 3D data structure and/or anatomical model. In one or more embodiments, data fusion may be used to determine the spatial relationships between data modalities such as ECG data and set of ECG images 112. In one or more embodiments, data fusion may include the process of extracting features from both ECG data and sets of images 112 during training and determining spatial relationships between ECG data and sets of images using concatenation, attention mechanisms and the like. In one or more embodiments, training of multimodal model may include the use of supervised machine learning technique in which data sets of ECG data and sets of images are fed into the multimodal and the multimodal predicts output. In one or more embedment, multimodal model may be configured to generate 3D representation of an anatomical object such as a cardiac anatomy. In one or more embodiments, a combination of ultrasonic images and mapping catheters may be used to create a more detailed 3D representation of anatomical object. In one or more embodiments, a mapping catheter may be used to receive ECG data such as intracardiac electrograms. In one or more embodiments anatomy modeling model may first be configured to generate a first 3D model of anatomical object wherein data form mapping catheter may be used to generate a final 3D model of anatomical object. In one or more embodiments, mapping catheter and/or ECG data may be 50 Attorney Docket No.1518-103PCT1 used to visualize electric activity of cardiac anatomy. In one or more embodiments, ECG data may be used to visualize a patient’s heart activity on a 3D generated structure. With continued reference to FIG.1, multimodal model may be configured to receive sets of images as an input and output 3D representation of anatomical object. In one or more embodiments, multimodal model may then be configured to receive ECG data and overlay an electroanatomic map onto 3D representation of anatomical object. In one or more embodiments, the combination of ECG data and sets of images may allow for a 3D representation of anatomical object with an overlay of electrical activity associated with the patient. In one or more embodiments, each input into multimodal model may aid in the visualization of a different aspect of 3D representation of anatomical object. In a non-limiting example, Ultrasonic images may generates 3D model of heart chambers in real-time using Intracardiac Echo, even without location sensor, CT/MR imaging biomarkers may Visualize precision structures and biomarkers derived from cardiac CT/MR (fibrosis, wall thickness, epicardial fat) and ECG data may Predict electroanatomic map using multi-modal cardiac data. With continued reference to FIG.1, multimodal model may utilize longitudinal multimodal data in order to generate outputs. “Longitudinal multimodal data” for the purposes of this disclosure refers to information collected form multiple sources over an extended period of time. In one or more embodiments, longitudinal multimodal data may include text, images, audio, video, physiological changes of a patient and the like. In one or more embodiments, patient profile may include longitudinal multimodal data. In one or more embodiments, longitudinal multimodal data may include information such as but not limited to, ECG signals ultrasounds images, medical records, clinical notes, radiology scans, molecular diagnostics, pathology screenings, electrophysiologic results, lab results and the like. In one or more embodiments, longitudinal multimodal data may be used by multimodal model in order to generate more detailed 3D representation of anatomical object. For example, and without limitation, longitudinal multimodal data may be used to generate electroanatomic maps as described in further detail below. Still referring to FIG, 1, as used in this disclosure, a “computed tomography (CT) based anatomical object model” refers to a 3D representation of anatomical object and surrounding structures that is created using data from CT scans. In one or more embodiments, CT based anatomical model includes anatomical model. Computed Tomography is a medical 51 Attorney Docket No.1518-103PCT1 imaging technique that uses X-rays to capture cross-sectional images (slices) of the body. By taking a plurality of slices, a CT scan creates a detailed 3D representation of the internal structure. In an embodiment, CT-based anatomical object model may include 3D representations of the heart including chambers, valves, blood vessels, and surrounding tissues. In some cases, CT-based anatomical object model may be interactive; for instance, medical professionals may rotate, zoom, and/or explore CT-based anatomical object model from various angles. In some cases, plurality of CT-based anatomical object models may be generated prior to the training of the anatomy modeling model 152. Plurality of CT-based anatomical object models may be generated using existing techniques in the field as described above such as, without limitation, FAM, cardiac CT merging, among others. In a non-limiting example, plurality of CT-based anatomical object models may provide ground through or references models against anatomy modeling model 152 that is being trained. In a non-limiting example, generating data structure 128 of anatomical object 116 further includes training anatomy modeling model 152 using anatomy training data described herein. Anatomy modeling model 152 trained using anatomy training data 156 may be able to interpret ultrasonic images by learning relationships between ultrasonic images and corresponding CT-based anatomical object models. Processor 104 is further configured to generate data structure 128 of anatomical object 116 as a function of set of images 112 using trained anatomy modeling model 152. In some cases, data structure 128 e.g., 3D VOR may be interpreted, visualized, and analyzed by processor 104 in similar manner to CT- based anatomical object models, wherein both are 3D structures that correspond to ultrasonic images. With continued reference to FIG.1, anatomy training data may include synthetic echocardiograms. In one or more embodiments, CT scans and/or already existing 3D models may be used to generate synthetic echocardiogram in order to augment anatomy training data increase the amount of training data present. In one or more embodiments, the generation and/or addition of synthetic echocardiograms may allow anatomy modeling model to generate more accurate outputs. In one or more embodiments, diffusion transformers may be used to generate synthetic echo diagrams using synthetic CT images. In one or more embodiments, the diffusion transformer may be trained to map detailed features from CT scans to corresponding echocardiogram features. In one or more embodiments, the diffusion transformer may then generate noisy images and iteratively generate synthetic echocardiogram based on learned 52 Attorney Docket No.1518-103PCT1 features between the original CT scans and the original echocardiograms. In one or more embodiments, data collection for use in a diffusion transformer may include the collection of CT images and corresponding echocardiograms. In one or more embodiment, a machine learning model may be trained to extract relevant features between the CT images and the echocardiograms using techniques such as CNN to capture spatial details. In one or more embodiments, a diffusion model may be configured to CT images until they resemble random noise. In one or more embodiments, the diffusion model may then be trained to reverse this process until the CT images are de-noised. In one or more embodiments, a transformer network may be configured to utilize recognized features between CT images and echocardiograms in order to generate synthetic echocardiograms. In one or more embodiments, the diffusion transformer may be trained using supervised learning in order to create synthetic echocardiograms which may then be used for training data within multimodal model. With continued reference to FIG.1, in an embodiment, anatomy modeling model includes a deep neural network (DNN). As used in this disclosure, a “deep neural network” is defined as a neural network with two or more hidden layers. Neural network is described in further detail with reference to FIGS.4-5. In a non-limiting example, anatomy modeling model may include a convolutional neural network (CNN). Generating 3D data structure 128 of anatomical object 116 may include training CNN using anatomy training data and generating 3D data structure 128 as a function of set of images 112 using trained CNN. A “convolutional neural network,” for the purpose of this disclosure, is a neural network in which at least one hidden layer is a convolutional layer that convolves inputs to that layer with a subset of inputs known as a “kernel,” along with one or more additional layers such as pooling layers, fully connected layers, and the like. In some cases, CNN may include, without limitation, a deep neural network (DNN) extension. Mathematical (or convolution) operations performed in the convolutional layer may include convolution of two or more functions, where the kernel may be applied to input data e.g., set of images 112 through a sliding window approach. In some cases, convolution operations may enable processor 104 to detect local/global patterns, edges, textures, and any other spatial features 140 described herein within each ultrasonic image of set of images 112. Spatial features 140 may be passed through one or more activation functions, such as without limitation, Rectified Linear Unit (ReLU), to introduce non-linearities into the processing step of generating 3D data structure 128 of anatomical object 116. Additionally, or alternatively, CNN 53 Attorney Docket No.1518-103PCT1 may also include one or more pooling layers, wherein each pooling layer is configured to reduce the dimensionality of input data while preserving essential features within the input data. In a non-limiting example, CNN may include one or more pooling layer configured to reduce the spatial dimensions of spatial feature maps by applying downsampling, such as max-pooling or average pooling, to small, non-overlapping regions of one or more spatial features 140. Still referring to FIG.1, CNN may further include one or more fully connected layers configured to combine spatial features 140 extracted by the convolutional and pooling layers as described above. In some cases, one or more fully connected layers may allow for higher-level pattern recognition. In a non-limiting example, one or more fully connected layers may connect every neuron (i.e., node) in its input to every neuron in its output, functioning as a traditional feedforward neural network layer. In some cases, one or more fully connected layers may be used at the end of CNN to perform high-level reasoning and produce the final output such as, without limitation, a 3D data structure 128 of anatomical object 116. Further, each fully connected layer may be followed by one or more dropout layers configured to prevent overfitting, and one or more normalization layers to stabilize the learning process described herein. With continued reference to FIG.1, CNN may further include a 3D CNN, wherein the 3D CNN, unlike standard 2D CNN, may include utilization of one or more 3D convolutions which allow them to directly process 3D data, thereby enabling processor 104 to generate 3D structures such as 3D data structure 128 of anatomical object 116 using the 3D CNN. In a non- limiting example, 3D CNN may include one or more 3D filters (i.e., kernels) that move through the set of images 112 in three dimensions and capturing spatial relationships in x, y, and z axis. Similar to 3D convolutions, 3D CNN may further include one or more 3D pooling layers that may be used to reduce the dimensionality of ultrasonic images while preserving spatial features 140 as described above. Additionally, or alternatively, an encoder-decoder structure may be implemented (extended to 3D), by processor 104, in 3D CNN, wherein the encoder-decoder structure includes an encoding path that captures the context and a decoding path that enables precise localization in a same manner as U-net as described above. Such encoder-decoder structures may also include a plurality of skip connections, allowing 3D CNN to use information from multiple resolutions to improve the process of generating 3D data structure 128 of anatomical object 116. 54 Attorney Docket No.1518-103PCT1 With continued reference to FIG.1, in an embodiment, training the anatomy modeling model 152 (i.e., CNN) may include selecting a suitable loss function to guide the training process. In a non-limiting example, a loss function that measures the difference between the predicted 3D VORs and the ground truth 3D structure e.g., CT-based anatomical object models may be used, such as, without limitation, mean squared error (MSE) or a custom loss function may be designed for one or more embodiments described herein. Additionally, or alternatively, optimization algorithms, such as stochastic gradient descent (SGD), may then be used to adjust the anatomy modeling model’s parameters to minimize such loss. In a further non- limiting embodiment, instead of directly predicting 3D data structure 128, anatomy modeling model 152 may be trained as a regression model to predict presence indicators 136 and/or other embedded values described herein for each voxel of plurality of voxels 132 within a 3D grid. Additionally, CNN may be extended with additional deep learning techniques, such as recurrent neural networks (RNNs) or attention mechanism, to capture additional features and/or data relationships within input data. These extensions may further enhance the accuracy and robustness of the anatomical object modeling. With continued reference to FIG.1, alternatively, processor 104 may generate a set of shape parameters 160 based on set of images 112. As used in this disclosure, a “set of shape parameters” refers to a collection of numerical values or descriptors that quantitatively represent the geometric or morphological characteristics of a structure e.g., a heart. In a non-limiting example, set of shape parameters 160 may include information and/or metadata calculated, determined, and/or extracted from set of ultrasonic images, such as, dimensions, angles, curvatures, surface areas, texture, symmetry, and/or the like. In other embodiments, processor 104 may be configured to parameterize features (e.g., edges, textures, contours, and any other characteristics that describe the shape anatomical object 116) extracted from set of images 112 using CNN described herein. Such parameterization may involve processor 104 to derive one or more shape parameters including one or more morphological descriptors that quantitatively describe anatomical object 116 based on extracted features. In some cases, processor 104 may be configured to use principal component analysis (PCA) to reduce the dimensionality of set of shape parameters 160, allowing processor 104 to focusing on the most informative shape parameters of set of shape parameters 160 in further processing steps described below. 55 Attorney Docket No.1518-103PCT1 With continued reference to FIG.1, in a non-limiting example, set of shape parameters 160 may be generated based on set of images 112 using machine learning model such as, without limitation, a shape identification model 164. Generating set of shape parameters 160 may include receiving geometry training data 168, wherein the geometry training data 168 may include a plurality of image sets as input correlated to a plurality of shape parameter sets as output. In some cases, geometry training data may be received from Image database 124 described herein. For example, and without limitation, geometry training data 168 may be used to show each ultrasonic image may indicate a particular set of shape parameters. Shape identification model 164 may be trained, by processor 104, using geometry training data 168. Additionally, geometry training data 168 may include previously input image sets and their corresponding shape parameters output. Shape identification model 164 may be iterative such that outputs may be used as future inputs of shape identification model 164. This may allow the shape identification model 164 to evolve. Processor 104 may be further configured to generate set of shape parameters 160 as a function of set of images 112 using the trained shape identification model 164. With continued reference to FIG.1, processor 104 is configured to generate an initial 3D model 172 of anatomical object 116. As used in this disclosure, an “initial 3D model” is a foundational representation, capturing the basic geometric and spatial characteristics of the organ in 3D space. In an embodiment, initial 3D model 172 may provide a “starting point” for further refinement and customization as described in further detail below, allowing for the incorporation of more detailed and patient-specific information. In some cases, initial 3D model 172 may be generated through a direct 3D reconstruction from a series of (2D) ultrasonic images. In a non- limiting example, set of images 112 may include a plurality of ultrasonic images captured from different angles and positions within the heart. Processor 104 may be configured to apply one or more 3D reconstruction algorithms, such as without limitation, marching cubes, contour detection and segmentation, active contour models, and/or the like to create a coherent 3D representation e.g., initial model 172 of anatomical object 116. In some cases, such direct 3D reconstruction may leverage the inherent spatial information within set of images 112, providing a direct and intuitive way to model the initial model 172 of the heart's structure. In a further embodiment, generic 3D modeling techniques may be applied to create the initial 3D model. In some cases, generic 3D modeling techniques may include surface modeling, solid modeling, or 56 Attorney Docket No.1518-103PCT1 parametric modeling, among others. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various 3D reconstruction algorithms that may be used by processor 104 to generate initial 3D model 172 of anatomical object 116. In one or more embodiments, initial 3D model may include a 3D representation of anatomical object as well as electroanatomic map overlayed on the 3D representation. An “Electroanatomical map,” as described in this disclosure refers to a visualization of electrical activity in the heart. In one or more embodiments, electroanatomical map may include a visualization of electrical activity on the heart. In one or more embodiments, electroanatomical map may include a visualization on a 3D representation of a patient’s heart. In one or more embodiments, initial 3D model may aid in the placement, sizing or detection of leakages in Left Atrial Appendage Occlusion Device placement. In one or more embodiments, changes in electroanatomical map on initial 3D model may indicate issues with placement of the occlusion device, issues with leakage and the like. Additionally, or alternatively, and still referring to FIG.1, initial 3D model 172 may be generated based on a plurality of standard anatomical templates, wherein the “plurality of standard anatomical templates,” for the purpose of this disclosure, refers to predefined and commonly accepted representations of the human body’s anatomical structures. In some cases, plurality of standard anatomical templates may be selected from Image database 124 as described herein based on statistical averages or shared characteristics. In a non-limiting example, initial 3D model 172 may include a template model 176 selected from a plurality of pre-determined template models. Plurality of pre-determined template model may be generated by processor 104 based on plurality of standard anatomical templates prior to the generation of initial 3D model 172 using 3D reconstruction/modeling algorithms/techniques as listed above. In an embodiment, generating initial 3D model 172 may include selecting template model 176 from plurality of template models based on set of ultrasonic images. In some cases, template model 176 may represent a typical or average anatomical object that is most similar to anatomical object 116 pertaining to subject 120. Such similarity may be determined based on one or more similarity metrics, such as without limitation, structural similarity index (SSI), MSE, peak signal- to-noise ratio (PSNR), normalized cross-correlation (NCC), Pearson correlation coefficient, and/or the like between set of images 112 and each image sets stored in Image database 124. Template model 176 may be adjusted and customized to fit the specific patient's ultrasonic images as described below in further detail. In one or more embodiments, template models 176 57 Attorney Docket No.1518-103PCT1 may represent various anatomical objects, such as but not limited to, blood vessels, organs, the heart and the like. In one or more embodiments, each template model 176 may represent a particular anatomical object. In one or more embodiments, selecting template model 176 may include selecting template model based on the identified anatomical object 116. In one or more embodiments, processor 104 may receive an input associated with sets of images 112 indicating the particular anatomical object wherein template model may be selected based on input. In one or more embodiments, processor 104 may use one or more image classification techniques to identify anatomical object 116 in sets of images 112 and select template model based on identification. With continued reference to FIG.1, processor 104 is configured to refine generated initial 3D model 172 of anatomical object 116 as a function of 3D data structure 128 of anatomical object 116. In a non-limiting embodiment, refining initial 3D model 172 of anatomical object 116 may include utilizing a statistical shape model (SSM) 180. It should be noted that SSM may not be the only method for refining initial 3D model 172. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various methods, such as, without limitation, mesh smoothing techniques, level set method, physics- based simulation, among others may be implemented, by processor 104, to refine initial 3D model 172 described herein. In some cases, SSM may be constructed by analyzing one or more datasets of shapes and identifying, for example, mean shapes and main modes of variation within the one or more datasets. In a non-limiting example, SSM may start with calculation of at least one mean shape, which represents an average geometry of all the heart shapes in a given dataset, wherein the at least one mean shape may be served as a central reference point for processor 104 to understand different variations. In some cases, dataset may include, without limitation, anatomy training data 156, geometry training data 168, and/or any datasets within ultrasonic image databases described herein. SSM may also identify one or more principal modes of variation within given datasets described herein, wherein the “principal modes of variations,” for the purpose of this disclosure, refer to main patterns or directions along which data points vary within dataset. In a non-limiting example, identifying principal modes of variations may include applying principal component analysis (PCA) on given dataset. Additionally, or alternatively, shapes may be described directly using plurality of shape parameter sets (in geometry training data 168). In some cases, shape parameter sets may correspond to a plurality of modes of 58 Attorney Docket No.1518-103PCT1 variations. Further, one or more statistical constraints (e.g., mean, variance, correlation, boundary, proportion constraint and/or the like) may be introduced into SSM 180 based on the distribution of shape parameters within plurality of shape parameter sets. With continued reference to FIG.1, refining initial 3D model 172 of anatomical object 116 may include aligning initial 3D model 172 with 3D VOR of anatomical object 116. In an embodiment, aligning initial 3D model 172 with 3D VOR may include matching template model 176 to 3D VOR; for instance, and without limitation, this may involve adjusting the position, orientation, and scale of template model 176 to match the spatial distribution captured in 3D VOR. In some cases, matching template model 176 to 3D VOR may include matching spatial features 140, wherein matching the spatial features 140 may further include aligning the surface, boundaries and internal structures of template model 176 with corresponding features in 3D VOR. In some embodiments, processor 104 may utilize one or more optimization techniques to achieve a desired alignment; for instance, and without limitation, processor may be configured to minimizing the difference between template model 176 and 3D VOR using iterative closest point (ICP) algorithms, gradient descent, or any other optimization strategies. Additionally, alignment of template model 176 with 3D VOR may also allow incorporation of patient-specific details (e.g., patient profile) into initial 3D model 172 to form a final model as described in further detail below. In a non-limiting example, and still referring to FIG.1, refining initial 3D model 172 of anatomical object 116 may include deforming, using processor 104, template model 176 to match 3D data structure 128 of anatomical object 116. As used in this disclosure, “deforming” means altering the geometric structure of a structure e.g., template model 176 in a systematic and controlled manner to align the structure with the spatial characteristics captured in another structure e.g., 3D VOR. In some cases, processor 104 may utilize one or more mathematical deformation models such as, without limitation, B-splines, radial basis functions, or other deformation functions to control and guide the deformation process of template model 176. In some cases, one or more constraints listed above may be applied, by processor 104, based on anatomical knowledge, biomechanical properties, or other relevant factors to ensure that the deformation of template model 176 is realistic and consistent with physiological principles as would be understood and/or expected by an ordinary person skilled in the art. 59 Attorney Docket No.1518-103PCT1 Still referring to FIG.1, additionally, or alternatively, refining initial 3D model 172 of anatomical object 116 may also include validating template model or deformed template model against 3D data structure 128 or additional data such as, without limitation, expert input, adjust parameters, and/or the like. Such validation process may ensure that the refined model accurately represents the underlaying anatomical object 116. In some cases, expert input may include any user input entered via a user interface as described in further detail below. In a non- limiting example, expert input may include, without limitation, clinical assessment, anatomical knowledge, or other professional insights that guide and evaluate the refinement process inputted to apparatus 100 by one or more users including medical professionals, subjects, patients, and/or any other related individuals. In a further embodiment, validating template model or deformed template model against 3D data structure 128 may also include fine-tuning defamation controls, alignment settings, or other model characteristics or properties to achieve desired alignment with 3D VOR or additional data. In some cases, other information that is incorporated and codified within template model 176/deformed template model and/or 3D data structure 128 such as medical imaging, biomechanical simulations, patient-specific data/metadata may be validated and cross-verified. At least a machine-learning process, for example a machine-learning model described herein, may be used to validate by processor 104. Processor 104 may use any machine- learning process described in this disclosure for this or any other functions. With continued reference to FIG.1, in some embodiments, embedded values described herein may be employed in the refinement process of initial 3D model 172 of anatomical object 116. In a non-limiting example, the embedded values may contribute to SSM 180 by providing additional parameters that guide the deformation and alignment of the template to match 3D VOR. Embedded values such as, without limitation, presence indicators 136 may be used by processor 104 to guide the deformation process by providing targets for alignment; for instance, and without limitation, SSM may be configured to identify specific target areas where initial 3D model e.g., a 3D LA model that needs to be deformed. Presence indicators 136, in this case, may reveal a bulge in LA wall that is not present in initial 3D model 172. In some cases, presence indicators 136 may define the exact shape of the bulge in LA wall. Processor 104 may then deform initial 3D model 172, particularly the wall to match the bulge defined by presence indicators 136 in 3D VOR. 60 Attorney Docket No.1518-103PCT1 With continued reference to FIG.1, generating initial 3D model 172 includes determining a level of uncertainty of at least one location of a plurality of locations of the initial 3D model 172 based on the set of shape parameters 160. A location may refer to each voxel of plurality of voxels, cells, geometric marker, and all other identifying markers/data points of a model as described throughout this disclosure. A plurality of locations may refer to the surface of initial 3D model and/or heart model, such as a set of pixels or a region on a model. “Uncertainty,” as used herein, refers to the lack of confidence or precision in a model's predictions. In some cases, the level of uncertainty 160may be derived from variability within the distribution of shape parameters, image quality assessment, measurement errors and/or the like. In a non-limiting example, greater changes in heart geometry (indicated by the plurality of shape parameters) may correspond to a greater level of uncertainty at that location. This may be used to inform clinical decisions, for example, areas of high uncertainty may be avoided when planning a pathway for surgical intervention or additional imaging may be requested to reduce uncertainty in critical areas. With continued reference to FIG.1, levels of uncertainty may refer to categories of uncertainty such as epistemic uncertainty, aleatoric uncertainty, model parameter uncertainty, pixel-wise uncertainty, boundary uncertainty, uncertainty in time series data, predictive uncertainty, systematic uncertainty, model output uncertainty, and the like. Epistemic uncertainty arises from a lack of knowledge or information. For example, limited training data for certain cardiac pathologies may contribute to higher epistemic uncertainty. Aleatoric uncertainty, also known as data uncertainty, results from inherent randomness or variability in the data. For example, variability in cardiac anatomy among different patients or imaging modalities introduces aleatoric uncertainty. Model Parameter Uncertainty is uncertainty associated with the model parameters, indicating how well the model has learned the underlying patterns in the training data. For example, variations in model parameters due to the stochastic nature of the optimization process contribute to parameter uncertainty. Pixel-wise Uncertainty is associated with individual pixels in the image. It provides a confidence measure for each pixel in the segmentation mask. For example, certain regions of the heart may be more challenging to segment accurately, leading to higher pixel-wise uncertainty. Boundary Uncertainty is related to the boundaries between different structures or regions in the image. For example, the precise delineation of the endocardium or epicardium may be uncertain in regions where the boundaries 61 Attorney Docket No.1518-103PCT1 are not well-defined. Regarding uncertainty in Time Series Data, in tasks involving sequential data, such as cardiac imaging over time, uncertainty can be related to variations in the temporal dimension. For example, segmentation of dynamic structures like the beating heart involves handling uncertainty associated with different phases of the cardiac cycle. Predictive Uncertainty is uncertainty in the model's predictions for unseen data points. For example, when the model encounters a novel pathology or an atypical cardiac structure, predictive uncertainty measures its confidence in providing accurate segmentation. Systematic Uncertainty is uncertainty stemming from systematic errors or biases in the data collection process or the model architecture. For example, if the training data is biased towards a specific demographic, the model may exhibit uncertainty when applied to a more diverse patient population. Model Output Uncertainty is uncertainty associated with the actual output of the model, indicating how confident the model is in its segmentation predictions. For example, the model may output a segmentation mask with a probability or confidence score for each pixel, reflecting the uncertainty associated with that pixel's classification. With continued reference to FIG.1, a level of uncertainty may include a degree, statistical measure, percentage, or variable whether linguistic or numerical, and the like identifying a range of uncertainty. For example, processor 104 may generate probability scores/confidence scores for locations of a model, indicating the model's confidence in its predictions. Calibration plots can be used to assess how well these confidence scores align with the true accuracy. Processor 104 may perform a threshold analysis to investigate how varying decision thresholds for classification or segmentation affects the trade-off between sensitivity and specificity in uncertain regions. Threshold analysis may include task-specific metrics for clinical relevance. For example, in cardiac image segmentation, critical regions like the myocardium may have stricter uncertainty thresholds compared to less critical regions. Processor 104 may implement Bayesian Neural Networks (BNNs) to perform posterior predictive checks to evaluate the agreement between the model's predictions and the observed data, such as a data store, considering the uncertainty represented by the posterior distribution in Bayesian frameworks. In various embodiments, a level of uncertainty may be metrics determined by processor 104, such as Pixel-wise Uncertainty Metrics, Boundary Displacement Error (BDE), Uncertainty-Aware Loss Functions, Calibration Metrics, and the like. 62 Attorney Docket No.1518-103PCT1 With continued reference to FIG.1, in some embodiments, level of uncertainty may be determined using Monte Carlo dropout. Monte Carlo dropout may include running a neural network multiple times using different dropout configurations. Each dropout configuration may include turning off turning off one or more nodes of a neural network. Monte Carlo dropout may be used to, for example, determine mean and variance parameters. In some embodiments, such a variance parameter may be used as level of uncertainty. With continued reference to FIG.1, in some embodiments, level of uncertainty may be determined using deep ensembles. A deep ensemble may include a plurality of machine learning models. An input may be applied to a plurality of machine learning model, and their outputs may be combined. For example, an average and/or variance of outputs of a plurality of models may be found. Level of uncertainty may be determined based on such variance. Still referring to FIG 1, anatomy modeling model 152 may be calibrated. Calibration may include fine-tuning or adjusting anatomy modeling model 152 predictions to align more closely with the actual probabilities. A well-calibrated model is one where, for instance, if it predicts a 70% probability for a certain event, that event actually occurs about 70% of the time. Calibrating anatomy modeling model may include receiving a set of validation data. As used in the current disclosure, a “set of validation data” is a set of data used to calibrate a machine learning model, which the machine learning model has not been trained on. A set of validation data is used to assess the model's performance and, in this case, to calibrate level of uncertainty. Processor 104 may sort each datapoint of the validation set into a plurality of hyperfine bins as a function of a continuous value. As used in the current disclosure, "hyperfine bins" is a grouping of the data points into a set of very fine or detailed bins. These bins may be organized based on the values of the continuous value. Each bin corresponds to a specific range or interval of continuous values. Processor may additionally determine a bin-wise scaling factor for each of the plurality of hyperfine bins. As used in the current disclosure, "bin-wise scaling factors" refers to a factor or multiplier associated with each individual hyperfine bin. It may be used to adjust or scale the data within each bin. The scaling factor can be unique to each bin and is typically determined based on some specific criteria or algorithm. Still referring to FIG 1, calibration of level of uncertainty may include temperature scaling. Temperature scaling may include adjusting confidence scores or probabilities generated by a model to make them better reflect the true uncertainty or reliability of the model's 63 Attorney Docket No.1518-103PCT1 predictions. This technique is often used to improve the calibration of deep neural networks, especially in cases where model confidence scores do not align well with actual probabilities. Temperature scaling introduces a hyperparameter known as the "temperature" (T). The temperature is a positive scalar value that is applied to the logits (raw scores) before they are passed through a SoftMax function. By adjusting the temperature, processor 104 may control the sharpness or spread of the probability distribution. A higher temperature makes the distribution more uniform, while a lower temperature makes the distribution sharper. In an embodiment, high temperature may smooth the distribution, reducing confidence in predictions. High temperature may increase level of uncertainty. Low temperature may sharpen the distribution, reducing level of uncertainty. Temperature parameter may be adjusted based on a validation dataset. A temperature that minimizes the difference between predicted probabilities and the true probabilities observed in a calibration dataset may be determined. In one or more embodiments, calibration and uncertainty may include any calibration and/or uncertainty as described in this disclosure. Still Referring to FIG.1, processor 104 may be configured to generate a map regarding one or more levels of uncertainty. A “map,” as used herein, refers to a visualization. Map may be level(s) of uncertainty to be visualized on the initial 3d model 172. Map may include a color-coded heatmap, including other visual cues, symbols or indicators that alert a user to areas of initial 3d model 172 that may require extra caution when used for planning or guidance during an ICE procedure and/or any other procedures. For example, after obtaining the segmentation results from set of images 112, map may be generated. Map may highlight the uncertainty or confidence level associated with each pixel in the segmentation. Assigning colors to different intensity levels in map allows for an intuitive visualization. Typically, warmer colors (e.g., red, or yellow) might represent high uncertainty, while cooler colors (e.g., blue, or green) could indicate low uncertainty. The color-coding can be adjusted based on specific thresholds or clinical requirements. Still referring to FIG.1, generating map may include methods such as Class Activation Mapping (CAM). Class Activation Mapping is a technique that originated for image classification tasks and has been extended to provide visual insights into the regions of an image that are most important for a particular class. CAM allows the visualization of the spatial attention of a convolutional neural network (CNN) by generating heat maps that highlight 64 Attorney Docket No.1518-103PCT1 discriminative regions. CAM may be applied to the last convolutional layer of a CNN. The features extracted by this layer capture high-level semantic information, making it suitable for visualizing the importance of different regions in an image. CAM is typically applied to the last convolutional layer of a CNN. The features extracted by this layer capture high-level semantic information, making it suitable for visualizing the importance of different regions in an image. The output of the global average pooling is then fed into a fully connected layer with a softmax activation function. This converts the features into class scores, indicating the likelihood of the image belonging to different classes. The CAM algorithm computes a weighted sum of the original feature maps based on the weights of the fully connected layer. These weights are determined during the training process and represent the importance of each feature map for a specific class. The weighted sum is applied to the original feature maps, producing a single heat map. This heat map highlights the regions of the input image that contributed most to the prediction for the target class. The generated heat map can be overlaid on the input image, visually indicating which regions are most relevant for the predicted class. Typically, warmer colors (e.g., red, or yellow) represent higher activation or importance. Still Referring to FIG.1, generating map may include Grad-CAM (Gradient-weighted Class Activation Mapping). Grad-CAM is an extension of Class Activation Mapping (CAM) that enhances the localization capabilities by incorporating gradient information from the final convolutional layer of a neural network. Grad-CAM helps to generate heat maps that highlight discriminative regions in an image, providing more fine-grained insights into where a convolutional neural network (CNN) is focusing its attention when making predictions. In traditional CAM, the last convolutional layer's feature maps are linearly combined to obtain a weighted sum, and the resulting weights are used to create a heat map that highlights relevant regions for a specific class. Grad-CAM improves upon CAM by introducing gradient information. It computes the gradients of the predicted class score with respect to the feature maps of the last convolutional layer. Grad-CAM retains the global average pooling (GAP) operation applied after the last convolutional layer, as it is an integral part of CAM. The GAP operation condenses the spatial information into a single value per feature map. The gradients obtained in the previous step are used to calculate the importance of each feature map. These gradients represent the importance of each feature map in contributing to the final prediction. A weighted sum is computed using these gradients, and this is combined with the original feature 65 Attorney Docket No.1518-103PCT1 maps. The computed sum goes through a ReLU activation function, discarding any negative values. This step emphasizes positive contributions and suppresses negative ones. The ReLU- activated weighted sum is linearly combined with the original feature maps to produce a weighted combination. This combination retains spatial information and helps create a more accurate heat map. The resulting heat map is often normalized to enhance visualization, ensuring that the values are within a specific range (e.g., between 0 and 1). The final heat map generated by Grad-CAM is then overlaid on the input image, highlighting the regions of interest for the predicted class. The intensity of the heat map indicates the importance of different regions. Grad- CAM enhances the interpretability and explainability of deep learning models, allowing practitioners and researchers to understand which parts of an image are crucial for a particular prediction. This is particularly valuable in applications such as medical imaging or any domain where understanding the decision-making process is critical. Still Referring to FIG.1, generating map may include utilizing a “SmoothGrad technique,” a technique designed to improve the interpretability of neural network predictions by reducing the noise in the attribution maps or heat maps generated by visualizing gradients. It is particularly useful for understanding the decision-making process of deep learning models, especially in scenarios where the explanations need to be robust and less sensitive to input perturbations. The primary goal of SmoothGrad is to enhance the visual quality of attribution maps generated by visualizing gradients. Attribution maps highlight the regions in the input that contribute most to a model's prediction. SmoothGrad aims to reduce the impact of noise in these maps, providing more stable and interpretable visualizations. The key idea behind SmoothGrad is to introduce perturbations to the input data. Instead of attributing the prediction solely to the gradients calculated with respect to the original input, the gradients are averaged over multiple perturbed versions of the input. By averaging the gradients over multiple perturbed samples, SmoothGrad helps reduce the impact of noise or irrelevant features in the attribution maps. This is particularly beneficial when dealing with complex or noisy datasets. Perturbation techniques include adding Gaussian noise, random rotations, or random translations to the input data. These perturbations create variations in the input while preserving the essential features, leading to more stable and reliable attribution maps. For each perturbed input, gradients are calculated with respect to the model's output. These gradients are then averaged over all perturbed samples. This process smoothens the attribution map by reducing the influence of random noise. The averaged 66 Attorney Docket No.1518-103PCT1 gradients may undergo normalization or scaling to ensure that the values are interpretable and within a specific range. This step can enhance the consistency and comparability of the generated attribution maps. The final step involves generating a heat map using the smoothed gradients. The heat map represents the attribution of different regions in the input to the model's prediction, providing a clearer and more stable visualization. Still Referring to FIG.1, generating map may include implementing one or more Gaussian Processes. A Gaussian Process is a collection of random variables, any finite subset of which has a joint Gaussian distribution. In simpler terms, it's a distribution over functions rather than a distribution over finite-dimensional vectors. Gaussian Processes (GPs) can be applied to generate heat maps in various ways, particularly in the context of regression tasks where one would want to predict continuous values across a spatial domain. Given a set of observed data points, the GP can predict the values at unobserved locations in the spatial domain. Importantly, it also provides uncertainty estimates associated with these predictions. This uncertainty can be visualized as a heat map. The predicted values from the GP represent the main heat map, indicating the expected values across the spatial domain. The uncertainty associated with each prediction can be visualized as an uncertainty heat map. This uncertainty heat map provides insights into regions where the model is less confident about its predictions. Overlay of the main heat map and the uncertainty heat map on the original spatial data may create a composite visualization. Warmer colors in the main heat map might represent higher predicted values, while the uncertainty heat map's intensity could indicate regions where the model's predictions are less certain. Still referring to FIG.1, processor 104 may be configured to overlay map onto 3D data structure 128. In some embodiments, the overlay may be placed on initial 3D model 172 and go through a refinement process as described above. In some cases, overlaying initial 3D model 172 with map may include utilizing interactive visualization techniques, which may allow user- mediated augmentation of the set of images of cardiac anatomy. Overlaying map on a model may include implementing spatial alignment methods, texture mapping techniques wherein the color information from the heat map is mapped onto the vertices or faces of the 3D model, shader programs that define how the heat map values influence the final appearance of the 3D model, visualization software or programming libraries that support 3D rendering and overlay capabilities, interactivity visualization, quality control methods, and the like. For example, 67 Attorney Docket No.1518-103PCT1 texture mapping may include UV Mapping wherein each point on the surface of a 3D model is associated with a set of texture coordinates often denoted as U and V. These coordinates are analogous to the X and Y coordinates on a 2D image. UV mapping establishes the correspondence between points on the 3D model and pixels on the 2D texture. In another example, interactive visualization may create visual representations of data that users can interact with and manipulate. This approach allows users to explore and analyze data dynamically, gaining insights through direct engagement with the visual representation. For example, mouse interactivity may allow users to interact with visual elements using mouse actions, such as hovering over data points for additional information, clicking to drill down into details, or dragging to pan and zoom. Filtering and Selection capabilities may allow a user to filter data based on specific criteria or select subsets of data for closer examination. This is particularly useful when dealing with large datasets. Spatial Exploration may allow users to zoom in to explore details or pan to navigate across the space. Still referring to FIG, 1. in some cases, an ICE frame and/or ultrasonic image taken during a procedure or synthesized for machine learning training purposes may be also overlaid at a corresponding location or heart model. Overlaying the ICE frame may include registering the ICE frame to the generated initial 3D model 172 using the image processing model. This process and method may use a processing system, including at least a processor, image generator, and camera transformation program, as disclosed in this disclosure. For example, the processing system may include at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a heart model related to a patient’s heart, identify a region of interest within the heart model, wherein identifying the region of interest includes locating at least a point of view on the heart model and determining a view angle corresponding to the at least a view origin, wherein the at least a point of view and the corresponding view angle define at least one field of view that include at least a portion of the heart model. The at least a processor may be further configured to generate at least a medical image as a function of the region of interest using an image generator, wherein the at least a medical image captures an anatomical structure of the at least a portion of the heart model. In one or more embodiments, mapping may include any mapping processes as described in this disclosure. 68 Attorney Docket No.1518-103PCT1 With continued reference to FIG.1, processor 104 is configured to generate a subsequent 3D model 184 of anatomical object 116 as a function of the refinement. As used in this disclosure, a “subsequent 3D model” refers to a more detailed and accurate 3D representation of anatomical object 116. In an embodiment, subsequent 3D model 184 may be derived from initial 3D model 172 and/or template model 176 and adjusted based on 3D data structure 128 ad described above. In such embodiment, subsequent 3D model 184 may include a deformed initial 3D model 172 and/or template model 176. In a non-limiting example, 3D VOR may indicate a need of adjustment to initial 3D model 172 of left ventricle to match subject’s 120 unique geometry. SSM 180 may then be configured to generate subsequent 3D model 184 that accurately captures such specific anatomical object based on initial 3D model 172 and 3D VOR. In other cases, initial 3D model 172 may not need any refinement; for instance, and without limitation, if initial 3D model 172 already align perfectly with 3D data structure representing subject’s 120 right atrium (RA), no deformation or adjustment would be necessary, thereby resulting in subsequent 3D model 184 that is identical to initial 3D model 172. Still referring to FIG.1, in some cases, the refinement process may also include the incorporation of more detailed features and textures based on 3D data structure 128 and embedded values thereof, enhancing the realism and specificity of initial 3D model 172. In an embodiment, SSM 180 may be integrated with one or more additional models such as, without limitation, texture models, appearance models, or functional models to generate subsequent 3D model 184. In some cases, such integration may result in subsequent 3D model 184 that reflects not just the geometry but also the biomechanical properties or blood flow dynamics within anatomical object 116. In a non-limiting example, texture of the myocardium may be modeled, by integrating texture models with SSM 180, to represent the fibrous nature of the heart muscle. In another non-limiting example, appearance of blood vessels, including color variations and translucency, may be modeled, by integrating appearance models with SSM 180. With continued reference to FIG.1, alternatively, refining initial 3D model 172 of anatomical object 116 may include adjusting template model 176 based on set of shape parameters 160. In an embodiment, processor 104 may be configured to map set of shape parameters 160 to SSM 180. The mapping process may define how template model 176 should be adjusted to represent specific subject’s 120 anatomical object. In a non-limiting example, shape parameters may include one or more numeric values indicating a particular thick 69 Attorney Docket No.1518-103PCT1 ventricular wall, processor 104 may configure SSM 180 to adjust template model 176 to reflect such characteristic. In an embodiment, generating subsequent 3D model 184 may involve generating a 3D mesh or grid that accurately represents the shape defined by set of shape parameters; for instance, and without limitation, processor 104 may be configured to generate a 3D mesh for left ventricle with vertices and edges positioned according to specific curvature and thickness defined by set of shape parameters 160 using SSM 180. With continued reference to FIG.1, in some embodiments, processor 104 may be configured to input subsequent 3D model 184 back into anatomy modeling model 152 and/or shape identification model 164for continuous learning. In some cases, training data for these models such as, without limitation, anatomy training data 156, geometry training data 168, and/or the like may be updated, by replacing, appending or otherwise inserting subsequent 3D model 184 (and corresponding set of ultrasonic images) into the dataset. This iterative process may allow machine learning module 148 to evolve over time, adapting to new set of ultrasonic images and improving the accuracy of machine learning models generated by machine learning module 148. Incorporation of subsequent 3D models as additional training data may enable apparatus 100 to capture more variations and nuances in anatomical object modeling, enhancing its ability to generalize across different patients and conditions. Still referring to FIG.1, additionally, processor 104 may use user feedback to train the machine-learning models described above. For example, anatomy modeling model 152 and/or shape identification model 164 may be trained using past inputs and outputs of anatomy modeling model 152 and/or shape identification model 164. In some embodiments, if user feedback indicates that a subsequent 3D model outputted by SSM 180 was “bad,” then that output and the corresponding input e.g., set of ultrasonic images, corresponding CT-based anatomical object model, and/or template model, may be removed from training data used to train anatomy modeling model 152 and/or shape identification model 164, and/or may be replaced with a value entered by, e.g., another user that represents an ideal 3D model of the heart given the input the machine learning models originally received, permitting use in retraining, and adding to training data as described above; in either case, machine learning models described herein may be retrained with modified training data. In some embodiments, training data such as anatomy training data 156 and/or geometry training data 168 may include user feedback. Further, apparatus 100 may be configured to validate one or more machine learning models described 70 Attorney Docket No.1518-103PCT1 herein against real-world data, identifying areas where machine learning models may be underperforming or misaligned with clinical needs. Such feedback may also be used to guide model training, ensuring that machine learning models are not only accurate but also clinically meaningful and aligned with healthcare or medical professional’s needs and priorities. With continued reference to FIG.1, apparatus 100 may further include a display device 188. As used int his disclosure, a “display device” is an electronic device that visually presents information to a user. In an embodiment, display device may include an output interface that translates data such as, without limitation, subsequent 3D model 184 from processor 104 or other computing devices into a visual form that can be easily understood by user. In some cases, subsequent 3D model 184 and/or other data described herein such as, without limitation, ultrasonic images, 3D VOR, shape parameters initial model and/or template model may also be displayed through display device 188 using a user interface 192. User interface 192 may include a graphical user interface (GUI), wherein the GUI may include a window in which subsequent 3D model 184 and/or other data described herein may be displayed. In an embodiment, user interface 192 may include one or more graphical locator and/or cursor facilities allowing user to interact with subsequent 3D model 184 and/or any other data, or even process described herein; for instance, and without limitation, by using a touchscreen, touchpad, mouse, keyboard, and/or other manual data entry device, user may enter user input containing selecting specific regions, adding comments, adjusting parameter, and/or the like. In a non-limiting example, user interface 192 may include one or more menus and/or panels permitting selection of measurements, models, visualization of data/model to be displayed and/or used, elements of data, functions, or other aspects of data/model to be edited, added, and/or manipulated, options for importation of and/or linking to application programmer interfaces (APIs), exterior services, data source, machine-learning models, and/or algorithms, or the like. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which a visual interface and/or elements thereof may be implemented and/or used as described in this disclosure. Now referring to FIG.2, an exemplary embodiment of an ultrasonic image 200 is illustrated. In one or more embodiments, ultrasonic image 200 includes an ICE image. As described above with reference to FIG.1, set of images 112 may include a plurality of ultrasonic images, wherein each ultrasonic image of the plurality of ultrasonic images is a specialized form 71 Attorney Docket No.1518-103PCT1 of echocardiography that may provides detailed image of heart’s (i.e., anatomical object 116) interior structures. In a non-limiting example, plurality of ultrasonic images may include an ICE video (e.g., plurality of ultrasonic images arranged in a corresponding time sequence). In an embodiment, ultrasonic image 200 may be real-time, dynamic ultrasound image that provide a (detailed) view 204 of heart’s interior structures, including, without limitation, right atrium (RA) 208, anterior descending (AD) 212, pulmonary atresia (PA) 216, and right ventricular (RV) 220. With continued reference to FIG.2, in some cases, ultrasonic image 200 may include gray scaled image. It should be noted that, in some cases, ultrasonic image 200 may be configured to visualize blood flow and/or blood flow patterns within the heart via color doppler as described above with FIG.1. In some cases, resolution and/or clarity of ultrasonic image 200 as described herein may be superior to transthoracic or transesophageal echocardiography due to the ICE catheter may be positioned inside the heart, closer to the structures being imaged. Still referring to FIG.2, in a non-limiting example, heart chambers may appear as dark, anechoic (black) areas since they are filled with blood, which doesn’t reflect ultrasound waves well. Heart walls, valves, and/or other structures may appear as varying shades of gray, depending on their density and composition, in some cases, Color Doppler overlays may show blood flow in different colors, indicating the direction and speed of blood flow. For instance, and without limitation, red may indicate flow towards the probe, while blue may indicate flow away from the probe. With continued reference to FIG.2, in a non-limiting embodiment, ultrasonic image 200 may be synchronized with ECG data as described above with reference to FIG.1, allowing for precise timing of cardiac events with anatomical visualization provided by ICE. In some cases, ultrasonic image 200 may include an ECG display 224 configured to display ECG waveform as a continuous line graph at the top, bottom, or side of ultrasonic image 200. In some cases, specific parts of the cardiac cycle e.g., systole or diastole, may be correlated with visual data from ultrasonic image 200. Additionally, or alternatively, and still referring to FIG.2, ultrasonic image 200 may come with accompanying metadata 228 displayed on the side or corners of ultrasonic image 200 as described herein. In some cases, metadata 228 may provide essential contextual information about ultrasonic image 200 and/or the corresponding patient. In a non-limiting example, metadata 228 may include patient information (e.g., patient ID, name, DOB, age, gender, and the 72 Attorney Docket No.1518-103PCT1 like), image acquisition details (e.g., date and time, probe type, frequency, depth, gain, and the like), procedure-related information (e.g., procedure name, operator, location, and the like), ECG trace (e.g., ECG data as described above), measurement annotations (e.g., any measurements taken directly on the image e.g., diameter, a value of thickness of a heart wall and the like), image sequence information (e.g., image number, total number of frames, and the like), comments or notes, hospital or clinic information, and/or the like. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of ultrasonic image 200 and various components thereof may be incorporated by apparatus 100 for generating 3D model of anatomical object. Now referring to FIG.3, a flow diagram of an exemplary embodiment of an ICE example generation process 300 is described. In an embodiment, anatomy training data 156 may be generated, at least in part, via ICE example generation process 300. In some cases, processor 104 may be configured to receive a 3D model of the heart, such as, without limitation, template model 176, initial model 172, subsequent 3D model 184, and/or any 3D model of anatomical object 116 as described herein and identify an ICE view 304 (i.e., visual representation of image obtained using intracardiac echocardiography as described above e.g., ultrasonic image 200) based on the received 3D model. In some cases, 3D model received by processor 104 may be derived from CT scans as described above with reference to FIG.1. In other cases, processor may receive CT scans directly instead of 3D models. A synthetic ICE frame 308 may then be generated, by processor 104, as a function of identified ICE view 304, wherein the synthetic ICE frame 308 may be used as one or the training examples in anatomy training data 156. With continued reference to FIG.3, in some cases, processor 104 may interface with one or more 3D models (i.e., detailed representation of heart’s anatomy in a 3D space, capturing intricate structures, chambers, vessels, valves, among others) as described above, or other imaging modalities and/or databases, and equipped with algorithms e.g., CNN, gradient boosting machines, SVM, PCA, and/or the like to analyze model’s geometry and spatial relationships upon receiving the 3D models. In some cases, 3D models may be received from SSM 180 as described above with reference to FIG.1 via a communicative connection between processor 104 and SSM 180. In a non-limiting example, processor 104 may be configured to determine an optimal viewpoints or angles from which ICE view 304 would provide a desired diagnostic value or procedural guidance. 73 Attorney Docket No.1518-103PCT1 Still referring to FIG.3, in some cases, identification and selection of ICE view 304 may be automatically identified, using one or more machine learning models as described herein. In a non-limiting example, processor 104 may utilize one or more machine learning models trained on anatomical object viewpoints identification training data, wherein the anatomical object viewpoints identification training data may include a plurality of cardiac anatomies as input correlated to a plurality of ultrasonic images as output and identify at least one ICE view 304 (most informative) for a given anatomical object using the trained machine learning models. Still referring to FIG.3, in other cases, ICE view 304 may be defined by a user such as a medical professional. In a non-limiting example user interface 192 of display device 188 may allow a user (e.g., a clinician) to manually rotate, pan, and zoom displayed 3D model and/or corresponding CT scans. As user do so, processor 104 may dynamically calculate and displays potential ICE views 304 based on user’s chosen perspective. Additionally, or alternatively, depending on cardiac procedure being planned or executed, processor 104 may prioritize certain ICE views 304. For instance, and without limitation, ICE view 304 may be pre-defined. For atrial fibrillation ablation, ICE view 304 may showcase the pulmonary veins’ entrances into the LA may be emphasized. In other cases, ICE view 304 may be automatically identified, by processor 104, using one or more machine learning models as described herein, such as, without limitation, synthetic ICE data generator as described in detail below. With continued reference to FIG.3, as used in this disclosure, a “synthetic ICE frame” refers to a digitally generated or simulated image that emulates a visual representation obtained from ICE view 304. In some cases, synthetic ICE frames 308 may be produced using computational methods and/or models such as, without limitation, a synthetic ICE data generator 312 based on pre-existing data, models, or simulations e.g., identified ICE views 304. In a non- limiting example, synthetic ICE frames 308 may include a simplified version e.g., an image illustrating heart anatomy via a plurality of lines indicating contours of heart’s structure as shown in FIG.3. One or more image processing techniques and/or computer vision algorithms such as, without limitation, histogram equalization, adaptive filtering, edge detection (e.g., Canny or Sobel operators), contour extraction, and/or the like may be applied, by processor 104, on a segmented CT scan and/or 3D models based on identified ICE view 304. Synthetic ICE frame 308 may be rendered on a blank canvas or background that mimics the echogenicity of an ultrasonic image according to extracted contours, wherein the extracted contours may be 74 Attorney Docket No.1518-103PCT1 represented as a bold lines and enhanced with shading to give depth. In some cases, synthetic ICE frame 308 may be validated and verified by overlaying synthetic ICE frame 308 onto original ICE view 304, ensuring accuracy and resemblance. Still referring to FIG.3, in some cases, generating synthetic ICE frames 308 may include implementations of one or more aspects of “generative artificial intelligence (AI),” a type of AI that uses machine learning algorithms to create, establish, or otherwise generate data such as, without limitation, ultrasonic images, ICE videos, and/or the like that is similar to one or more provided training examples. In an embodiment, machine learning module described herein may generate one or more generative machine learning models that are trained on one or more set of CT scans and/or 3D models in ultrasonic image view 304 as described above. Synthetic ICE data generator 312 may include one or more generative machine learning models may be configured to generate new examples that are similar to the training data of the one or more generative machine learning models but are not exact replicas; for instance, and without limitation, data quality or attributes of the generated examples may bear a resemblance to the training data provided to one or more generative machine learning models, wherein the resemblance may pertain to underlying patterns, features, or structures found within the provided training data. Still referring to FIG.3, in some cases, generative machine learning models within synthetic ICE data generator may include one or more generative models. As described herein, “generative models” refers to statistical models of the joint probability distribution ^^^ ^^, ^^^ on a given observable variable x, representing features or data that can be directly measured or observed (e.g. CT scans and/or 3D models derived from CT scans) and target variable y, representing the outcomes or labels that one or more generative models aims to predict or generate (e.g., synthetic ICE frames 308). In some cases, generative models may rely on Bayes theorem to find joint probability; for instance, and without limitation, Naïve Bayes classifiers may be employed by computing device to categorize input data such as, without limitation, CT scans and/or 3D models derived from CT scans into different views. In a non-limiting example, and still referring to FIG.3, one or more generative machine learning models may include one or more Naïve Bayes classifiers generated, by processor 104, using a Naïve bayes classification algorithm. Naïve Bayes classification algorithm generates classifiers by assigning class labels to problem instances, represented as 75 Attorney Docket No.1518-103PCT1 vectors of element values. Class labels are drawn from a finite set. Naïve Bayes classification algorithm may include generating a family of algorithms that assume that the value of a particular element is independent of the value of any other element, given a class variable. Naïve Bayes classification algorithm may be based on Bayes Theorem expressed as P(A/B)= P(B/A) P(A)÷P(B), where P(A/B) is the probability of hypothesis A given data B also known as posterior probability; P(B/A) is the probability of data B given that the hypothesis A was true; P(A) is the probability of hypothesis A being true regardless of data also known as prior probability of A; and P(B) is the probability of the data regardless of the hypothesis. A naïve Bayes algorithm may be generated by first transforming training data into a frequency table. Processor 104 may then calculate a likelihood table by calculating probabilities of different data entries and classification labels. Processor 104 may utilize a naïve Bayes equation to calculate a posterior probability for each class. A class containing the highest posterior probability is the outcome of prediction. Still referring to FIG.3, although Naïve Bayes classifier may be primarily known as a probabilistic classification algorithm; however, it may also be considered a generative model described herein due to its capability of modeling the joint probability distribution ^^^ ^^, ^^^ over observable variables X and target variable Y. In an embodiment, Naïve Bayes classifier may be configured to make an assumption that the features X are conditionally independent given class label Y, allowing generative model to estimate the joint distribution as ^^^ ^^, ^^^ ൌ ^^^ ^^^∏ ^^ ^^^ ^^ ^^ ∣ ^^^, wherein ^^^ ^^^ may be the prior probability of the class, and ^^^ ^^_^| ^^^ is the conditional probability of each feature given the class. One or more generative machine learning models containing Naïve Bayes classifiers may be trained on labeled training data, estimating conditional probabilities ^^^ ^^_^| ^^^ and prior probabilities ^^^ ^^^ for each class; for instance, and without limitation, using techniques such as Maximum Likelihood Estimation (MLE). One or more generative machine learning models containing Naïve Bayes classifiers may select a class label ^^ according to prior distribution ^^^ ^^^, and for each feature ^^_^, sample at least a value according to conditional distribution ^^^ ^^_^| ^^^. Sampled feature values may then be combined to form one or more new data instance with selected class label ^^. In a non-limiting example, one or more generative machine learning models may include one or more Naïve Bayes classifiers to generate new examples of ultrasonic images based on CT scans and/or 3D models derived from CT scans (e.g., identified ICE views 304), wherein the models may be trained using training data 76 Attorney Docket No.1518-103PCT1 containing a plurality of features of input data as described herein and/or the like correlated to a plurality of ICE views. Still referring to FIG.3, in some cases, one or more generative machine learning models may include generative adversarial network (GAN). As used in this disclosure, a “generative adversarial network” is a type of artificial neural network with at least two sub models (e.g., neural networks), a generator, and a discriminator, that compete against each other in a process that ultimately results in the generator learning to generate new data samples, wherein the “generator” is a component of the GAN that learns to create hypothetical data by incorporating feedbacks from the “discriminator” configured to distinguish real data from the hypothetical data. In some cases, generator may learn to make discriminator classify its output as real. In an embodiment, discriminator may include a supervised machine learning model while generator may include an unsupervised machine learning model as described in further detail with reference to FIGS.5-7. With continued reference to FIG.3, in an embodiment, discriminator may include one or more discriminative models, i.e., models of conditional probability ^^^ ^^| ^^ ൌ ^^^ of target variable Y, given observed variable X. In an embodiment, discriminative models may learn boundaries between classes or labels in given training data. In a non-limiting example, discriminator may include one or more classifiers as described in further detail below with reference to FIG.5 to distinguish between different categories e.g., real vs. fake, or states e.g., TRUE vs. FALSE within the context of generated data such as, without limitations, synthetic ICE frames 308, and/or the like. In some cases, processor 104 may implement one or more classification algorithms such as, without limitation, Support Vector Machines (SVM), Logistic Regression, Decision Trees, and/or the like to define decision boundaries. In a non-limiting example, and still referring to FIG.3, generator of GAN may be responsible for creating synthetic data that resembles real ultrasonic images. In some cases, GAN may be configured to receive CT scans and/or 3D models derived from CT scans as input and generates corresponding examples of ultrasonic images containing information describing heart anatomy in different ICE views. On the other hand, discriminator of GAN may evaluate the authenticity of the generated content by comparing it to true ultrasonic images, for example, discriminator may distinguish between genuine and generated content and providing feedback to generator to improve the model performance. Additionally, or alternatively, GAN may include a 77 Attorney Docket No.1518-103PCT1 conditional GAN as an extension of the basic GAN as described herein that allows for generation of ultrasonic images using pre-existing CT scans and/or 3D models derived from CT scans based on certain conditions or labels. In standard GAN, generator may produce samples from random noise, while in a conditional GAN, generator may produce samples based on random noise and a given condition or label. With continued reference to FIG.3, in other embodiments, one or more generative models may also include a variational autoencoder (VAE). As used in this disclosure, a “variational autoencoder” is an autoencoder (i.e., an artificial neural network architecture) whose encoding distribution is regularized during the model training process in order to ensure that its latent space includes desired properties allowing new data sample generation. In an embodiment, VAE may include a prior and noise distribution respectively, trained using expectation- maximization meta-algorithms such as, without limitation, probabilistic PCA, sparse coding, among others. In a non-limiting example, VEA may use a neural network as an amortized approach to jointly optimize across input data and output a plurality of parameters for corresponding variational distribution as it maps from a known input space to a low-dimensional latent space. Additionally, or alternatively, VAE may include a second neural network, for example, and without limitation, a decoder, wherein the “decoder” is configured to map from the latent space to the input space. In a non-limiting example, and still referring to FIG.3, VAE may be used by processor 104 to model complex relationships between CT scans and/or 3D models derived from CT scans. In some cases, VAE may encode input data into a latent space, capturing example ultrasonic images. Such encoding process may include learning one or more probabilistic mappings from observed CT scans and/or 3D models derived from CT scans to a lower- dimensional latent representation. Latent representation may then be decoded back into the original data space, therefore reconstructing the 3D models representing example ultrasonic images. In some cases, such decoding process may allow VAE to generate new examples or variations that are consistent with the learned distributions. Additionally, or alternatively, and still referring to FIG.3, processor 104 may be configured to continuously monitor synthetic ICE data generator. In an embodiment, processor 104 may configure discriminator to provide ongoing feedback and further corrections as needed to subsequent input data. An iterative feedback loop may be created as processor 104 78 Attorney Docket No.1518-103PCT1 continuously receive real-time data, identify errors (e.g., distance between synthetic ICE frame 308 and real ultrasonic images) as a function of real-time data, delivering corrections based on the identified errors, and monitoring subsequent model outputs and/or user feedbacks on the delivered corrections. In an embodiment, processor 104 may be configured to retrain one or more generative machine learning models within synthetic ICE data generator based on user modified ICE frames or update training data of one or more generative machine learning models within synthetic ICE data generator by integrating validated synthetic ICE frames (i.e., subsequent model output) into the original training data. In such embodiment, iterative feedback loop may allow synthetic ICE data generator to adapt to the user’s needs and performance requirements, enabling one or more generative machine learning models described herein to learn and update based on user responses and generated feedbacks. With continued reference to FIG.3, other exemplary embodiments of generative machine learning models may include, without limitation, long short-term memory networks (LSTMs), (generative pre-trained) transformer (GPT) models, mixture density networks (MDN), and/or the like. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various generative machine learning models may be used generating synthetic ICE frames 308. Still referring to FIG.3, in a further non-limiting embodiment, synthetic ICE data generator 312 may be further configured to generate a multi-model neural network that combines various neural network architectures described herein. In a non-limiting example, multi-model neural network may combine LSTM for time-series analysis with GPT models for natural language processing. Such fusion may be applied by computing device to generate synthetic ICE frames 308. In some cases, multi-model neural network may also include a hierarchical multi- model neural network, wherein the hierarchical multi-model neural network may involve a plurality of layers of integration; for instance, and without limitation, different models may be combined at various stages of the network. Convolutional neural network (CNN) may be used for image feature extraction, followed by LSTMs for sequential pattern recognition, and a MDN at the end for probabilistic modeling. Other exemplary embodiments of multi-model neural network may include, without limitation, ensemble-based multi-model neural network, cross- modal fusion, adaptive multi-model network, among others. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various generative machine 79 Attorney Docket No.1518-103PCT1 learning models may be used to generating synthetic ICE frames 308 as described herein. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various multi-model neural network and combination thereof that may be implemented by apparatus 100 in consistent with this disclosure. Now referring to FIG.4, an exemplary embodiment of a 3D VOR 400 is illustrated. 3D VOR 400 may be used to represent 3D object 404. In an embodiment, 3D VOR 400 may divide a 3D space 408 into a grid of one or more cubic units e.g., voxels 412, wherein each voxel 412 represents a specific volume within 3D space 408. In a non-limiting example, 3D object 404 may include a anatomical object pertaining to a subject. Still referring to FIG.4, in some cases, each voxel 412 may act as a basic building block. In a non-limiting example, each voxel 412 may be configured to represent a discrete portion of 3D space 408. In an embodiment, each voxel 412 may include a presence indicator as described above with reference to FIG.1, which denotes whether the voxel is occupied or unoccupied. In such embodiment, the binary or continuous value may allow 3D VOR 400 to map the presence or absence of material within each voxel 412, creating a granular representation of 3D object 404. With continued reference to FIG.4, in some cases, the resolution of 3D VOR 400 may be determined by the size and number of voxels within the grid. In a non-limiting example, smaller voxel may provide a higher resolution, capturing finer details, while larger voxels offer a more generalized representation. Still referring to FIG.4, in an embodiment, voxels 412 may be arranged in a regular pattern along three axis 416a-b, each pointing a distinct direction. In a non-limiting example, voxels 412 may be arranged along x, y, and z axes, wherein such arrange may facilitate efficient manipulation and rendering of the 3D object 404. In some cases, spatial features 420a-c such as, without limitation, edges, surfaces, textures, and any other spatial features as described above with reference to FIG.1, may be extracted from 3D VOR 400 by analyzing the relationships and patterns between neighboring voxels. Referring now to FIG.5, an exemplary embodiment of a machine-learning module 500 that may perform one or more machine-learning processes as described in this disclosure is illustrated. Machine-learning module may perform determinations, classification, and/or analysis steps, methods, processes, or the like as described in this disclosure using machine learning 80 Attorney Docket No.1518-103PCT1 processes. A “machine learning process,” as used in this disclosure, is a process that automatedly uses training data 504 to generate an algorithm instantiated in hardware or software logic, data structures, and/or functions that will be performed by a computing device/module to produce outputs 508 given data provided as inputs 512; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. Still referring to FIG.5, “training data,” as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements. For instance, and without limitation, training data 504 may include a plurality of data entries, also known as “training examples,” each entry representing a set of data elements that were recorded, received, and/or generated together; data elements may be correlated by shared existence in a given data entry, by proximity in a given data entry, or the like. Multiple data entries in training data 504 may evince one or more trends in correlations between categories of data elements; for instance, and without limitation, a higher value of a first data element belonging to a first category of data element may tend to correlate to a higher value of a second data element belonging to a second category of data element, indicating a possible proportional or other mathematical relationship linking values belonging to the two categories. Multiple categories of data elements may be related in training data 504 according to various correlations; correlations may indicate causative and/or predictive links between categories of data elements, which may be modeled as relationships such as mathematical relationships by machine-learning processes as described in further detail below. Training data 504 may be formatted and/or organized by categories of data elements, for instance by associating data elements with one or more descriptors corresponding to categories of data elements. As a non- limiting example, training data 504 may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data 504 may be linked to descriptors of categories by tags, tokens, or other data elements; for instance, and without limitation, training data 504 may be provided in fixed-length formats, formats linking positions of data to categories such as comma-separated value (CSV) formats and/or self-describing formats such as extensible markup language (XML), JavaScript Object Notation (JSON), or the like, enabling processes or devices to detect categories of data. 81 Attorney Docket No.1518-103PCT1 Alternatively or additionally, and continuing to refer to FIG.5, training data 504 may include one or more elements that are not categorized; that is, training data 504 may not be formatted or contain descriptors for some elements of data. Machine-learning algorithms and/or other processes may sort training data 504 according to one or more categorizations using, for instance, natural language processing algorithms, tokenization, detection of correlated values in raw data and the like; categories may be generated using correlation and/or other processing algorithms. As a non-limiting example, in a corpus of text, phrases making up a number “n” of compound words, such as nouns modified by other nouns, may be identified according to a statistically significant prevalence of n-grams containing such words in a particular order; such an n-gram may be categorized as an element of language such as a “word” to be tracked similarly to single words, generating a new category as a result of statistical analysis. Similarly, in a data entry including some textual data, a person’s name may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine- learning algorithms, and/or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly may enable the same training data 504 to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data 504 used by machine-learning module 500 may correlate any input data as described in this disclosure to any output data as described in this disclosure. As a non-limiting illustrative example, image sets may be correlated with plurality of CT-based anatomical object models as training data that may be used to train anatomical object modeling machine learning model as described above with reference to FIGS.1. Further referring to FIG.5, training data may be filtered, sorted, and/or selected using one or more supervised and/or unsupervised machine-learning processes and/or models as described in further detail below; such models may include without limitation a training data classifier 516. Training data classifier 516 may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a data structure representing and/or using a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance 82 Attorney Docket No.1518-103PCT1 metric as described below, or the like. A distance metric may include any norm, such as, without limitation, a Pythagorean norm. Machine-learning module 500 may generate a classifier using a classification algorithm, defined as a processes whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data 504. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher’s linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers. As a non-limiting example, training data classifier 516 may classify elements of training data to at least one template model of plurality of template modules as described above with reference to FIG.1. With further reference to FIG.5, training examples for use as training data may be selected from a population of potential examples according to cohorts relevant to an analytical problem to be solved, a classification task, or the like. Alternatively or additionally, training data may be selected to span a set of likely circumstances or inputs for a machine-learning model and/or process to encounter when deployed. For instance, and without limitation, for each category of input data to a machine-learning process or model that may exist in a range of values in a population of phenomena such as images, user data, process data, physical data, or the like, a computing device, processor, and/or machine-learning model may select training examples representing each possible value on such a range and/or a representative sample of values on such a range. Selection of a representative sample may include selection of training examples in proportions matching a statistically determined and/or predicted distribution of such values according to relative frequency, such that, for instance, values encountered more frequently in a population of data so analyzed are represented by more training examples than values that are encountered less frequently. Alternatively or additionally, a set of training examples may be compared to a collection of representative values in a database and/or presented to a user, so that a process can detect, automatically or via user input, one or more values that are not included in the set of training examples. Computing device, processor, and/or module may automatically generate a missing training example; this may be done by receiving and/or retrieving a missing input and/or output value and correlating the missing input and/or output value with a 83 Attorney Docket No.1518-103PCT1 corresponding output and/or input value collocated in a data record with the retrieved value, provided by a user and/or other device, or the like. Still referring to FIG.5, computer, processor, and/or module may be configured to sanitize training data. “Sanitizing” training data, as used in this disclosure, is a process whereby training examples are removed that interfere with convergence of a machine-learning model and/or process to a useful result. For instance, and without limitation, a training example may include an input and/or output value that is an outlier from typically encountered values, such that a machine-learning algorithm using the training example will be adapted to an unlikely amount as an input and/or output; a value that is more than a threshold number of standard deviations away from an average, mean, or expected value, for instance, may be eliminated. Alternatively or additionally, one or more training examples may be identified as having poor quality data, where “poor quality” is defined as having a signal to noise ratio below a threshold value. As a non-limiting example, and with further reference to FIG.5, images used to train an image classifier or other machine-learning model and/or process that takes images as inputs or generates images as outputs may be rejected if image quality is below a threshold value. For instance, and without limitation, computing device, processor, and/or module may perform blur detection, and eliminate one or more Blur detection may be performed, as a non-limiting example, by taking Fourier transform, or an approximation such as a Fast Fourier Transform (FFT) of the image and analyzing a distribution of low and high frequencies in the resulting frequency-domain depiction of the image; numbers of high-frequency values below a threshold level may indicate blurriness. As a further non-limiting example, detection of blurriness may be performed by convolving an image, a channel of an image, or the like with a Laplacian kernel; this may generate a numerical score reflecting a number of rapid changes in intensity shown in the image, such that a high score indicates clarity and a low score indicates blurriness. Blurriness detection may be performed using a gradient-based operator, which measures operators based on the gradient or first derivative of an image, based on the hypothesis that rapid changes indicate sharp edges in the image, and thus are indicative of a lower degree of blurriness. Blur detection may be performed using Wavelet -based operator, which takes advantage of the capability of coefficients of the discrete wavelet transform to describe the frequency and spatial content of images. Blur detection may be performed using statistics-based operators take advantage of 84 Attorney Docket No.1518-103PCT1 several image statistics as texture descriptors in order to compute a focus level. Blur detection may be performed by using discrete cosine transform (DCT) coefficients in order to compute a focus level of an image from its frequency content. Continuing to refer to FIG.5, computing device, processor, and/or module may be configured to precondition one or more training examples. For instance, and without limitation, where a machine learning model and/or process has one or more inputs and/or outputs requiring, transmitting, or receiving a certain number of bits, samples, or other units of data, one or more training examples’ elements to be used as or compared to inputs and/or outputs may be modified to have such a number of units of data. For instance, a computing device, processor, and/or module may convert a smaller number of units, such as in a low pixel count image, into a desired number of units, for instance by upsampling and interpolating. As a non-limiting example, a low pixel count image may have 100 pixels, however a desired number of pixels may be 128. Processor may interpolate the low pixel count image to convert the 100 pixels into 128 pixels. It should also be noted that one of ordinary skill in the art, upon reading this disclosure, would know the various methods to interpolate a smaller number of data units such as samples, pixels, bits, or the like to a desired number of such units. In some instances, a set of interpolation rules may be trained by sets of highly detailed inputs and/or outputs and corresponding inputs and/or outputs downsampled to smaller numbers of units, and a neural network or other machine learning model that is trained to predict interpolated pixel values using the training data. As a non-limiting example, a sample input and/or output, such as a sample picture, with sample- expanded data units (e.g., pixels added between the original pixels) may be input to a neural network or machine-learning model and output a pseudo replica sample-picture with dummy values assigned to pixels between the original pixels based on a set of interpolation rules. As a non-limiting example, in the context of an image classifier, a machine-learning model may have a set of interpolation rules trained by sets of highly detailed images and images that have been downsampled to smaller numbers of pixels, and a neural network or other machine learning model that is trained using those examples to predict interpolated pixel values in a facial picture context. As a result, an input with sample-expanded data units (the ones added between the original data units, with dummy values) may be run through a trained neural network and/or model, which may fill in values to replace the dummy values. Alternatively or additionally, processor, computing device, and/or module may utilize sample expander methods, a low-pass 85 Attorney Docket No.1518-103PCT1 filter, or both. As used in this disclosure, a “low-pass filter” is a filter that passes signals with a frequency lower than a selected cutoff frequency and attenuates signals with frequencies higher than the cutoff frequency. The exact frequency response of the filter depends on the filter design. Computing device, processor, and/or module may use averaging, such as luma or chroma averaging in images, to fill in data units in between original data units. In some embodiments, and with continued reference to FIG.5, computing device, processor, and/or module may down-sample elements of a training example to a desired lower number of data elements. As a non-limiting example, a high pixel count image may have 256 pixels, however a desired number of pixels may be 128. Processor may down-sample the high pixel count image to convert the 256 pixels into 128 pixels. In some embodiments, processor may be configured to perform downsampling on data. Downsampling, also known as decimation, may include removing every Nth entry in a sequence of samples, all but every Nth entry, or the like, which is a process known as “compression,” and may be performed, for instance by an N-sample compressor implemented using hardware or software. Anti-aliasing and/or anti-imaging filters, and/or low-pass filters, may be used to clean up side-effects of compression. Still referring to FIG.5, machine-learning module 500 may be configured to perform a lazy-learning process 520 and/or protocol, which may alternatively be referred to as a “lazy loading” or “call-when-needed” process and/or protocol, may be a process whereby machine learning is conducted upon receipt of an input to be converted to an output, by combining the input and training set to derive the algorithm to be used to produce the output on demand. For instance, an initial set of simulations may be performed to cover an initial heuristic and/or “first guess” at an output and/or relationship. As a non-limiting example, an initial heuristic may include a ranking of associations between inputs and elements of training data 504. Heuristic may include selecting some number of highest-ranking associations and/or training data 504 elements. Lazy learning may implement any suitable lazy learning algorithm, including without limitation a K-nearest neighbors algorithm, a lazy naïve Bayes algorithm, or the like; persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various lazy- learning algorithms that may be applied to generate outputs as described in this disclosure, including without limitation lazy learning applications of machine-learning algorithms as described in further detail below. 86 Attorney Docket No.1518-103PCT1 Alternatively or additionally, and with continued reference to FIG.5, machine- learning processes as described in this disclosure may be used to generate machine-learning models 524. A “machine-learning model,” as used in this disclosure, is a data structure representing and/or instantiating a mathematical and/or algorithmic representation of a relationship between inputs and outputs, as generated using any machine-learning process including without limitation any process as described above and stored in memory; an input is submitted to a machine-learning model 524 once created, which generates an output based on the relationship that was derived. For instance, and without limitation, a linear regression model, generated using a linear regression algorithm, may compute a linear combination of input data using coefficients derived during machine-learning processes to calculate an output datum. As a further non-limiting example, a machine-learning model 524 may be generated by creating an artificial neural network, such as a convolutional neural network including an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of "training" the network, in which elements from a training data 504 set are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning. Still referring to FIG.5, machine-learning algorithms may include at least a supervised machine-learning process 528. At least a supervised machine-learning process 528, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to generate one or more data structures representing and/or instantiating one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include a plurality of image sets as described above as inputs, a plurality of shape parameter sets as outputs, and a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and/or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring 87 Attorney Docket No.1518-103PCT1 function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data 504. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various possible variations of at least a supervised machine-learning process 528 that may be used to determine relation between inputs and outputs. Supervised machine-learning processes may include classification algorithms as defined above. With further reference to FIG.5, training a supervised machine-learning process may include, without limitation, iteratively updating coefficients, biases, weights based on an error function, expected loss, and/or risk function. For instance, an output generated by a supervised machine-learning model using an input example in a training example may be compared to an output example from the training example; an error function may be generated based on the comparison, which may include any error function suitable for use with any machine-learning algorithm described in this disclosure, including a square of a difference between one or more sets of compared values or the like. Such an error function may be used in turn to update one or more weights, biases, coefficients, or other parameters of a machine-learning model through any suitable process including without limitation gradient descent processes, least-squares processes, and/or other processes described in this disclosure. This may be done iteratively and/or recursively to gradually tune such weights, biases, coefficients, or other parameters. Updating may be performed, in neural networks, using one or more back-propagation algorithms. Iterative and/or recursive updates to weights, biases, coefficients, or other parameters as described above may be performed until currently available training data is exhausted and/or until a convergence test is passed, where a “convergence test” is a test for a condition selected as indicating that a model and/or weights, biases, coefficients, or other parameters thereof has reached a degree of accuracy. A convergence test may, for instance, compare a difference between two or more successive errors or error function values, where differences below a threshold amount may be taken to indicate convergence. Alternatively or additionally, one or more errors and/or error function values evaluated in training iterations may be compared to a threshold. Still referring to FIG.5, a computing device, processor, and/or module may be configured to perform method, method step, sequence of method steps and/or algorithm described in reference to this figure, in any order and with any degree of repetition. For instance, 88 Attorney Docket No.1518-103PCT1 a computing device, processor, and/or module may be configured to perform a single step, sequence and/or algorithm repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks. A computing device, processor, and/or module may perform any step, sequence of steps, or algorithm in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing. Further referring to FIG.5, machine learning processes may include at least an unsupervised machine-learning processes 532. An unsupervised machine-learning process, as used herein, is a process that derives inferences in datasets without regard to labels; as a result, an unsupervised machine-learning process may be free to discover any structure, relationship, and/or correlation provided in the data. Unsupervised processes 532 may not require a response variable; unsupervised processes 532may be used to find interesting patterns and/or inferences between variables, to determine a degree of correlation between two or more variables, or the like. Still referring to FIG.5, machine-learning module 500 may be designed and configured to create a machine-learning model 524 using techniques for development of linear regression models. Linear regression models may include ordinary least squares regression, which aims to minimize the square of the difference between predicted outcomes and actual outcomes according to an appropriate norm for measuring such a difference (e.g. a vector-space distance norm); coefficients of the resulting linear equation may be modified to improve minimization. Linear regression models may include ridge regression methods, where the function to be minimized includes the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients. Linear regression models may include least absolute shrinkage and selection operator (LASSO) models, in which ridge 89 Attorney Docket No.1518-103PCT1 regression is combined with multiplying the least-squares term by a factor of 1 divided by double the number of samples. Linear regression models may include a multi-task lasso model wherein the norm applied in the least-squares term of the lasso model is the Frobenius norm amounting to the square root of the sum of squares of all terms. Linear regression models may include the elastic net model, a multi-task elastic net model, a least angle regression model, a LARS lasso model, an orthogonal matching pursuit model, a Bayesian regression model, a logistic regression model, a stochastic gradient descent model, a perceptron model, a passive aggressive algorithm, a robustness regression model, a Huber regression model, or any other suitable model that may occur to persons skilled in the art upon reviewing the entirety of this disclosure. Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output/actual output fit is sought; similar methods to those described above may be applied to minimize error functions, as will be apparent to persons skilled in the art upon reviewing the entirety of this disclosure. Continuing to refer to FIG.5, machine-learning algorithms may include, without limitation, linear discriminant analysis. Machine-learning algorithm may include quadratic discriminant analysis. Machine-learning algorithms may include kernel ridge regression. Machine-learning algorithms may include support vector machines, including without limitation support vector classification-based regression processes. Machine-learning algorithms may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. Machine-learning algorithms may include nearest neighbors algorithms. Machine-learning algorithms may include various forms of latent space regularization such as variational regularization. Machine-learning algorithms may include Gaussian processes such as Gaussian Process Regression. Machine-learning algorithms may include cross-decomposition algorithms, including partial least squares and/or canonical correlation analysis. Machine-learning algorithms may include naïve Bayes methods. Machine- learning algorithms may include algorithms based on decision trees, such as decision tree classification or regression algorithms. Machine-learning algorithms may include ensemble methods such as bagging meta-estimator, forest of randomized trees, AdaBoost, gradient tree boosting, and/or voting classifier methods. Machine-learning algorithms may include neural net algorithms, including convolutional neural net processes. 90 Attorney Docket No.1518-103PCT1 Still referring to FIG.5, a machine-learning model and/or process may be deployed or instantiated by incorporation into a program, apparatus, system and/or module. For instance, and without limitation, a machine-learning model, neural network, and/or some or all parameters thereof may be stored and/or deployed in any memory or circuitry. Parameters such as coefficients, weights, and/or biases may be stored as circuit-based constants, such as arrays of wires and/or binary inputs and/or outputs set at logic “1” and “0” voltage levels in a logic circuit to represent a number according to any suitable encoding system including twos complement or the like or may be stored in any volatile and/or non-volatile memory. Similarly, mathematical operations and input and/or output of data to or from models, neural network layers, or the like may be instantiated in hardware circuitry and/or in the form of instructions in firmware, machine-code such as binary operation code instructions, assembly language, or any higher- order programming language. Any technology for hardware and/or software instantiation of memory, instructions, data structures, and/or algorithms may be used to instantiate a machine- learning process and/or model, including without limitation any combination of production and/or configuration of non-reconfigurable hardware elements, circuits, and/or modules such as without limitation ASICs, production and/or configuration of reconfigurable hardware elements, circuits, and/or modules such as without limitation FPGAs, production and/or of non- reconfigurable and/or configuration non-rewritable memory elements, circuits, and/or modules such as without limitation non-rewritable ROM, production and/or configuration of reconfigurable and/or rewritable memory elements, circuits, and/or modules such as without limitation rewritable ROM or other memory technology described in this disclosure, and/or production and/or configuration of any computing device and/or component thereof as described in this disclosure. Such deployed and/or instantiated machine-learning model and/or algorithm may receive inputs from any other process, module, and/or component described in this disclosure, and produce outputs to any other process, module, and/or component described in this disclosure. Continuing to refer to FIG.5, any process of training, retraining, deployment, and/or instantiation of any machine-learning model and/or algorithm may be performed and/or repeated after an initial deployment and/or instantiation to correct, refine, and/or improve the machine- learning model and/or algorithm. Such retraining, deployment, and/or instantiation may be performed as a periodic or regular process, such as retraining, deployment, and/or instantiation at 91 Attorney Docket No.1518-103PCT1 regular elapsed time periods, after some measure of volume such as a number of bytes or other measures of data processed, a number of uses or performances of processes described in this disclosure, or the like, and/or according to a software, firmware, or other update schedule. Alternatively or additionally, retraining, deployment, and/or instantiation may be event-based, and may be triggered, without limitation, by user inputs indicating sub-optimal or otherwise problematic performance and/or by automated field testing and/or auditing processes, which may compare outputs of machine-learning models and/or algorithms, and/or errors and/or error functions thereof, to any thresholds, convergence tests, or the like, and/or may compare outputs of processes described herein to similar thresholds, convergence tests or the like. Event-based retraining, deployment, and/or instantiation may alternatively or additionally be triggered by receipt and/or generation of one or more new training examples; a number of new training examples may be compared to a preconfigured threshold, where exceeding the preconfigured threshold may trigger retraining, deployment, and/or instantiation. Still referring to FIG.5, retraining and/or additional training may be performed using any process for training described above, using any currently or previously deployed version of a machine-learning model and/or algorithm as a starting point. Training data for retraining may be collected, preconditioned, sorted, classified, sanitized or otherwise processed according to any process described in this disclosure. Training data may include, without limitation, training examples including inputs and correlated outputs used, received, and/or generated from any version of any system, module, machine-learning model or algorithm, apparatus, and/or method described in this disclosure; such examples may be modified and/or labeled according to user feedback or other processes to indicate desired results, and/or may have actual or measured results from a process being modeled and/or predicted by system, module, machine-learning model or algorithm, apparatus, and/or method as “desired” results to be compared to outputs for training processes as described above. Redeployment may be performed using any reconfiguring and/or rewriting of reconfigurable and/or rewritable circuit and/or memory elements; alternatively, redeployment may be performed by production of new hardware and/or software components, circuits, instructions, or the like, which may be added to and/or may replace existing hardware and/or software components, circuits, instructions, or the like. 92 Attorney Docket No.1518-103PCT1 Further referring to FIG.5, one or more processes or algorithms described above may be performed by at least a dedicated hardware unit 536. A “dedicated hardware unit,” for the purposes of this figure, is a hardware component, circuit, or the like, aside from a principal control circuit and/or processor performing method steps as described in this disclosure, that is specifically designated or selected to perform one or more specific tasks and/or processes described in reference to this figure, such as without limitation preconditioning and/or sanitization of training data and/or training a machine-learning algorithm and/or model. A dedicated hardware unit 536 may include, without limitation, a hardware unit that can perform iterative or massed calculations, such as matrix-based calculations to update or tune parameters, weights, coefficients, and/or biases of machine-learning models and/or neural networks, efficiently using pipelining, parallel processing, or the like; such a hardware unit may be optimized for such processes by, for instance, including dedicated circuitry for matrix and/or signal processing operations that includes, e.g., multiple arithmetic and/or logical circuit units such as multipliers and/or adders that can act simultaneously and/or in parallel or the like. Such dedicated hardware units 536 may include, without limitation, graphical processing units (GPUs), dedicated signal processing modules, FPGA or other reconfigurable hardware that has been configured to instantiate parallel processing units for one or more specific tasks, or the like, A computing device, processor, apparatus, or module may be configured to instruct one or more dedicated hardware units 536 to perform one or more operations described herein, such as evaluation of model and/or algorithm outputs, one-time or iterative updates to parameters, coefficients, weights, and/or biases, and/or any other operations such as vector and/or matrix operations as described in this disclosure. Referring now to FIG.6, an exemplary embodiment of neural network 600 is illustrated. A neural network 600 also known as an artificial neural network, is a network of “nodes,” or data structures having one or more inputs, one or more outputs, and a function determining outputs based on inputs. Such nodes may be organized in a network, such as without limitation a convolutional neural network, including an input layer of nodes 604, one or more intermediate layers 608, and an output layer of nodes 612. Connections between nodes may be created via the process of "training" the network, in which elements from a training data set are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the 93 Attorney Docket No.1518-103PCT1 connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning. Connections may run solely from input nodes toward output nodes in a “feed-forward” network or may feed outputs of one layer back to inputs of the same or a different layer in a “recurrent network.” As a further non-limiting example, a neural network may include a convolutional neural network including an input layer of nodes, one or more intermediate layers, and an output layer of nodes. A “convolutional neural network,” as used in this disclosure, is a neural network in which at least one hidden layer is a convolutional layer that convolves inputs to that layer with a subset of inputs known as a “kernel,” along with one or more additional layers such as pooling layers, fully connected layers, and the like. Referring now to FIG.7, an exemplary embodiment of a node 700 of a neural network is illustrated. A node may include, without limitation, a plurality of inputs xi that may receive numerical values from inputs to a neural network containing the node and/or from other nodes. Node may perform one or more activation functions to produce its output given one or more inputs, such as without limitation computing a binary step function comparing an input to a threshold value and outputting either a logic 1 or logic 0 output or something equivalent, a linear activation function whereby an output is directly proportional to the input, and/or a non-linear activation function, wherein the output is not proportional to the input. Non-linear activation functions may include, without limitation, a sigmoid function of the form ^^^ ^^^ ൌ ^{^} ^_ି^ష^ given ^ erbolic tangent) function, of the form _ି^ ^{ష^} input x, a tanh (hyp ^{^} ^_^ା^ష^, a tanh

such _{as ^^} ^{^} _^^ ^{^} _{ൌ tanh} ^ଶ _{^ ^^^, a rectified linear unit function such as ^^} ^{^} _^^ ^{^} _{ൌ max ^0, ^^^, a “leaky” and/or} “parametric” rectified linear unit function such as ^^^{^} ^^^{^} ൌ max ^ ^^ ^^, ^^^ for some a, an ^{exponential linear units function such as ^^^ ^^^ ൌ ^ ^^ ^^ ^^ ^^ ^^ ^ 0} ^_{^^ ^^ ^^ ^^ ^^ ^ 0} ^{for some value of ^^} (this function may be replaced and/or weighted

in some embodiments), a _{softmax function such as ^^ ^^^} ^{^} _ൌ ^{^}^ ^ _௫ where the inputs to an instant layer are ^^_^, a swish

_^ function such as ^^^ ^^^ ൌ ^^ ∗

^^^, a Gaussian error linear unit function such as f(x) = ^^൫1 ^ tanh ^^2/ ^^^{^} ^^ ^ ^^ ^^^{^^}^൯ for some values of a, b, and r, and/or a scaled exponential linear _unit

^ ^{^^^ ^^௫ െ 1^ ^^ ^^ ^^ ^^ ^} ._{Fundamentally, there is no limit to the}

94 Attorney Docket No.1518-103PCT1 nature of functions of inputs xi that may be used as activation functions. As a non-limiting and illustrative example, node may perform a weighted sum of inputs using weights wi that are multiplied by respective inputs x_i. Additionally, or alternatively, a bias b may be added to the weighted sum of the inputs such that an offset is added to each unit in the neural network layer that is independent of the input to the layer. The weighted sum may then be input into a function φ, which may generate one or more outputs y. Weight w_i applied to an input x_i may indicate whether the input is “excitatory,” indicating that it has strong influence on the one or more outputs y, for instance by the corresponding weight having a large numerical value, and/or a “inhibitory,” indicating it has a weak effect influence on the one more inputs y, for instance by the corresponding weight having a small numerical value. The values of weights w_i may be determined by training a neural network using training data, which may be performed using any suitable process as described above. Now referring to FIG.8, a schematic of an exemplary transesophageal echocardiogram (TEE) procedure 800 is shown. In some cases, TEE procedure 800 may be performed during another procedure for instance heart surgery. According to some embodiments, a patient 804 has an endoscope 808, with an ultrasonic transducer 812, inserted into his esophagus 816. As one’s esophagus 816 is proximal one’s heart 820, ultrasonic transducer 812 may generate echocardiograms. Still referring to FIG.8, in some embodiments, transesophageal echocardiography (TEE) may provide superior imaging quality than intracardiac echocardiography (ICE), as larger ultrasound transducers 812 may be placed within the esophagus 816 than within heart 820. In some cases, ultrasound transducers must be substantially miniaturized to fit within heart 820, as in ICE catheters. As esophagus 816 may be proximal to heart 820, TEE may provide a clear image of various heart structures without needing vascular access (as commonly required by ICE). Additionally, TEE may be performed without obstructing patient’s 804 ribcage and intermediary tissues (as commonly required by transthoracic echocardiography [TTE]). In some cases, TEE images may also provide information associated with angle of acquisition. Angle of acquisition may be an angle of TEE probe with respect to esophagus 816 (e.g., esophageal axis). Still referring to FIG.8, in some embodiments, TEE echocardiogram data, including images showing heart structures and, in some cases, angle of acquisition, may be used as input to any machine learning process described in this application, for instance with reference to FIGS. 95 Attorney Docket No.1518-103PCT1 1–7, 9, and 10. For instance TEE echocardiogram data may be used to reconstruct 3D heart models. In some cases, TEE echocardiogram data is input into a machine learning model that outputs a 3D heart model (e.g., 3D mesh model and/or statistical shape model). Still referring to FIG.8, in some embodiments, TEE may be a preferred imaging modality for structural heart interventions, such as without limitation left atrial appendage occlusion (LAOO) and aortic/mitral/other heart valve replacement procedures. In some cases, technology and improvements described in this disclosure permit creation and/or modification of a 3D heart mesh from TEE data to aid in planning implant size selection, as well as to guide implantation procedures. In some cases, virtual placement of a 3D model of a candidate implant (such as without limitation LAAO device and/or heart valve implants) can be simulated on a 3D heart model generated by any method described in this disclosure. This novel and improved functionality may validate appropriate size and placement of implants within heart 820, as well as other organs within body of patient 804. For example, in the context of electrophysiology procedures, TEE procedure 800 can be used to create heart anatomical models that can be used as reference for electroanatomic mapping, and guidance of ablation catheters for atrial fibrillation procedures (such as without limitation pulmonary vein isolation). Still referring to FIG.8, in some embodiments, applications described with reference to TEE procedure 800 above can be extended for use with TTE and point of care ultrasound (POCUS). In some cases, both TTE and POCUS may acquire ultrasound images of chest / surface of patient 804. In some cases, TTE and POCUS data may be used as an input (and/or training data) for any machine learning process described in this disclosure, for instance with reference to FIGS.1–7, 9, 10. In some cases, use of TTE and/or POCUS data (in machine learning processes described in this disclosure) may require adjustment in ultrasound acquisition parameters and positions to acquire a sufficient number of frames for 3D reconstruction. In some cases, TTE and POCUS offer improved accessibility (with POCUS being portable/mobile as well) and non-invasive 3D heart modeling, often without anesthesia or sedation, compared to catheterized 3D heart modeling commonly performed today for electroanatomical mapping and ablation procedures. Now referring to FIG.9, an exemplary embodiment of a method 900 for generating a three-dimensional (3D) model of anatomical object via machine-learning is illustrated. Method 900 includes a step 905 of receiving, by at least a processor, a set of images of a anatomical 96 Attorney Docket No.1518-103PCT1 object pertaining to a subject. In some embodiments, receiving the set of images includes receiving the set of images from a patient profile. This may be implemented, without limitation, as described above with reference to FIGS.1-8. With continued reference to FIG.9, method 900 includes a step 910 of generating, by the at least a processor, an 3D data structure representing the anatomical object as a function of the set of images. In some embodiments, the 3D data structure representing the anatomical object may include a 3D voxel occupancy representation (VOR) having plurality of voxels, wherein each voxel of the plurality of voxels may include a corresponding presence indicator. In other embodiments, the 3D data structure representing the anatomical object may include a 3D grid configured to map the presence indicators of the plurality of voxels, wherein the 3D grid may include one or more spatial features extracted from the set of images of the anatomical object. This may be implemented without limitation, as described above with reference to FIGS. 1-8. Still referring to FIG.9, step 910 of generating the 3D data structure further includes receiving anatomy training data, wherein the anatomy training data contains a plurality of image sets as input and a plurality of computed tomography (CT) based anatomical object models as output, training an anatomy modeling model using the anatomy training data, and generating the 3D data structure representing the anatomical object as a function of the set of images using the trained anatomy modeling model. In some embodiments, the anatomy modeling model may include a Deep Neural Network (DNN). This may be implemented without limitation, as described above with reference to FIGS.1-8. Still referring to FIG.9, alternatively, step 910 of generating the 3D VOR may include generating a set of shape parameters based on the set of images of the anatomical object, wherein generating the set of shape parameters may include training a shape identification model using geometry training data, wherein the geometry training data contains the plurality of image sets as input correlated to a plurality of shape parameter sets as output and generating the set of shape parameters as a function of the set of ultrasonic images using the trained shape identification model. This may be implemented without limitation, as described above with reference to FIGS.1-8. 97 Attorney Docket No.1518-103PCT1 With continued reference to FIG.9, method 900 includes a step 915 of generating, by the at least a processor, an initial 3D model of the anatomical object. This may be implemented without limitation, as described above with reference to FIGS.1-8. With continued reference to FIG.9, method 900 includes a step 920 of refining, by the at least a processor, the generated initial 3D model of the anatomical object as a function of the 3D data structure representing the anatomical object. In some embodiments, the initial 3D model of the anatomical object may include a template model selected from a plurality of pre- determined template models. In some embodiments, refining the initial 3D model of the anatomical object may include deforming the template model to match the generated 3D data structure representing the anatomical object. In other embodiments, refining the initial 3D model of the anatomical object may include adjusting the subsequent 3D model of the anatomical object as a function of a set of shape parameters. This may be implemented without limitation, as described above with reference to FIGS.1-8. With continued reference to FIG.9, method 900 includes a step 925 of generating, by the at least a processor, a subsequent 3D model of the anatomical object as a function of the refinement. This may be implemented without limitation, as described above with reference to FIGS.1-8. Referring now to FIG.10, an exemplary method 1000 generating a three- dimensional (3D) model of an anatomical object via machine-learning is described. At step 1005, method 1000 includes receiving, by at least a processor, a set of images of an anatomical object pertaining to a subject. This may be implemented without limitation, as described above with reference to FIGS.1-8. With continued reference to FIG.10, at step 1010 method 1000 includes generating, by the at least a processor, anatomy training data using a 3D anatomical model, wherein the anatomy training data includes a plurality of image sets as input and a plurality of anatomical object models as output. This may be implemented without limitation, as described above with reference to FIGS.1-8. With continued reference to FIG.10, at step 1015 method 1000 includes training, by the at least a processor, an anatomy modeling model using the generated anatomy training data. With continued reference to FIG.10, at step 1020 method 1000 includes generating, by the at least a processor, a three-dimensional (3D) data structure representing the anatomical 98 Attorney Docket No.1518-103PCT1 object using the trained anatomy modeling model. This may be implemented without limitation, as described above with reference to FIGS.1-9. With continued reference to FIG.10, at step 1025 method 1000 includes refining, by the at least a processor, an initial 3D model as a function of the 3D data structure representing the anatomical object. This may be implemented without limitation, as described above with reference to FIGS.1-9. With continued reference to FIG.10, in one or more embodiments, the set of images include one or more ultrasonic images. In one or more embodiments, the anatomical object includes an organ. In one or more embodiments, receiving, by the at least a processor, the set of images includes receiving the set of images from a patient profile. In one or more embodiments, receiving, the set of images from the patient profile further includes receiving (ECG) data associated with the subject form the patient profile. In one or more embodiments, the anatomy training data further includes the plurality of image sets and a plurality of ECG data as inputs and the plurality of anatomical object models as outputs. In one or more embodiments, the trained anatomy modeling model includes a multimodal machine learning model. In one or more embodiments, receiving, by the at least a processor, the set of images includes receiving the set of images from a patient profile. In one or more embodiments, generating, by the at least a processor, the anatomy training data using the 3D anatomical model includes classifying the set of images to an anatomical categorization and generating the anatomy training data using the 3D anatomical model as a function of the anatomical categorization. In one or more embodiments, the 3D anatomical model is configured to receive ongoing feedback and corrections to the 3D anatomical model and provide corrections to subsequent synthetic images. In one or more embodiments, generating the initial 3D model further includes generating a map visualizing a level of uncertainty on the 3D model. In one or more embodiments, the initial 3D model of the anatomical object includes a template model selected from a plurality of pre-determined template models. Referring now to FIG.11, an exemplary embodiment of an apparatus 1100 for generating 3D model of a cardiac anatomy via machine-learning is illustrated. System includes at least a processor 1104. Processor 1104 may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Computing 99 Attorney Docket No.1518-103PCT1 device may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. Processor 1104 may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. Processor 1104 may interface or communicate with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for connecting processor 1104 to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software etc.) may be communicated to and/or from a computer and/or a computing device. Processor 1104 may include but is not limited to, for example, a computing device or cluster of computing devices in a first location and a second computing device or cluster of computing devices in a second location. Processor 1104 may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. Processor 1104 may distribute one or more computing tasks as described below across a plurality of computing devices of computing device, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. Processor 1104 may be implemented using a “shared nothing” architecture in which data is cached at the worker, in an embodiment, this may enable scalability of apparatus 1100 and/or computing device. With continued reference to FIG.11, processor 1104 may be designed and/or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, processor 1104 may be configured to perform a single step or sequence repeatedly until a desired 100 Attorney Docket No.1518-103PCT1 or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Processor 1104 may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing. With continued reference to FIG.11, apparatus includes a memory 1108 communicatively connected to at least a processor 1104, wherein the memory 1108 contains instructions configuring at least a processor 1104 to perform any processing steps described herein. As used in this disclosure, “communicatively connected” means connected by way of a connection, attachment or linkage between two or more relata which allows for reception and/or transmittance of information therebetween. For example, and without limitation, this connection may be wired or wireless, direct or indirect, and between two or more components, circuits, devices, systems, and the like, which allows for reception and/or transmittance of data and/or signal(s) therebetween. Data and/or signals therebetween may include, without limitation, electrical, electromagnetic, magnetic, video, audio, radio and microwave data and/or signals, combinations thereof, and the like, among others. A communicative connection may be achieved, for example and without limitation, through wired or wireless electronic, digital or analog, communication, either directly or by way of one or more intervening devices or components. Further, communicative connection may include electrically coupling or connecting at least an output of one device, component, or circuit to at least an input of another device, component, or circuit. For example, and without limitation, via a bus or other facility for intercommunication between elements of a computing device. Communicative connecting may also include indirect connections via, for example and without limitation, wireless connection, 101 Attorney Docket No.1518-103PCT1 radio communication, low power wide area network, optical communication, magnetic, capacitive, or optical coupling, and the like. In some instances, the terminology “communicatively coupled” may be used in place of communicatively connected in this disclosure. With continued reference to FIG.11, processor 1104 may perform determinations, classification, and/or analysis steps, methods, processes, or the like as described in this disclosure using machine-learning processes. A “machine-learning process,” as used in this disclosure, is a process that automatedly uses a body of data known as “training data” and/or a “training set” (described further below in this disclosure) to generate an algorithm that will be performed by a processor 1104/module to produce outputs given data provided as inputs; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. Machine-learning process may utilize supervised, unsupervised, lazy-learning processes and/or neural networks, described further below. With continued reference to FIG.11, processor 1104 is configured to receive an organ model 1112 related to one of a patient’s organs. As used in this disclosure, an “organ model” refers to a digital representation of a patient’s organ, capturing its anatomy, geometry, and potentially functional properties. In some cases, organ model 1112 may include a heart model. A “heart model,” for the purposes of this disclosure, is a digital representation of a patient’s heart, capturing its anatomy, geometry, and potentially functional properties. In some embodiments, organ model may include a liver model, in some embodiments, organ model may include a kidney model, a lung model, a brain model, and/or the like. In some cases, patient may include a human or any individual organism, on whom or on which the procedure, study, or otherwise experiment, such as without limitation, atrial fibrillation (AF) ablation, is being conducted. In a non-limiting example, processor 1104 may receive organ model 1112 of a human patient with AF who is undergoing a procedure, an individual undergoing cardiac screening, a participant in a clinical trial, patient with congenital heart disease, heart transplant candidate, liver disease, patient receiving follow-up care after cardiac surgery, healthy volunteer, patient with heart failure, or the like. Additionally, or alternatively, patient may include an animal models (i.e., animal used to model AF such as a laboratory rat). 102 Attorney Docket No.1518-103PCT1 With continued reference to FIG.11, in some cases, organ model 1112 may be received from a statistical shape model 1116. as used in this disclosure, a “statistical shape model (SSM)” is a data structure representing, including, and/or utilizing a mathematical model that captures principal modes of variation in shape across a population of cardiac anatomies. SSM 1116 captures a plurality of organ models associated with a plurality of patients. In some cases, SSM 1116 may be used to capture the variability in anatomical structures among different patients; for instance, SSM 1116 of the human heart may be constructed from a plurality of heart images of a plurality of individuals. In some cases, organ model 1112 generated by SSM 1116 may capture the “average” heart shape and main ways in which heart shapes may vary among the plurality of individuals. In a non-limiting example, SSM 1116 described herein may be consistent with any SSM disclosed in this disclosure. With continued reference to FIG.11, in some embodiments, processor 1104 may receive organ model 1112 from an anatomy modeling model as described further in this disclosure. With continued reference to FIG.11, processor 1104 may use a machine learning module to implement one or more algorithms or generate one or more machine learning models, such as an anatomy modeling model to generate a 3d data structure of organ. However, the machine learning module is exemplary and may not be necessary to generate one or more machine learning models and perform any machine learning described herein. In one or more embodiments, one or more machine-learning models may be generated using training data. Training data may include inputs and corresponding predetermined outputs so that a machine- learning model may use correlations between the provided exemplary inputs and outputs to develop an algorithm and/or relationship that then allows machine-learning model to determine its own outputs for inputs. Training data may contain correlations that a machine-learning process may use to model relationships between two or more categories of data elements. Exemplary inputs and outputs may come from a database, such as any database described in this disclosure, or be provided by a user. In other embodiments, a machine-learning module may obtain a training set by querying a communicatively connected database that includes past inputs and outputs. Training data may include inputs from various types of databases, resources, and/or user inputs and outputs correlated to each of those inputs so that a machine-learning model may determine an output. Correlations may indicate causative and/or predictive links between data, 103 Attorney Docket No.1518-103PCT1 which may be modeled as relationships, such as mathematical relationships, by machine-learning models, as described in further detail below. In one or more embodiments, training data may be formatted and/or organized by categories of data elements by, for example, associating data elements with one or more descriptors corresponding to categories of data elements. As a non- limiting example, training data may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data may be linked to descriptors of categories by tags, tokens, or other data elements. In a further embodiment, training data may include previous outputs such that one or more machine learning models iteratively produces outputs. Still referring to FIG.11, machine learning module may be used to generate anatomy modeling model and/or any other machine learning models, such as, shape identification model as described in further detail below, using training data. Anatomy modeling model may be trained by correlated inputs and outputs of training data. Training data may be data sets that have already been converted from raw data whether manually, by machine, or any other method. In an embodiment, generating data structure of organ includes receiving anatomy training data, wherein the anatomy training data may include a plurality of image sets as input and a plurality of computed tomography (CT) based anatomical object models as output, and wherein each image set of plurality of image sets may include any images described in this disclosure. In some cases, anatomy training data may be received from Image database or other databases. In other cases, anatomy training data may be collected by a data acquisition unit from external sources such as one or more medical equipment’s e.g., imaging devices or diagnostic tools, wherein the data acquisition may be configured as an intermediary between the data source and machine learning module. In one or more embodiments, anatomy training data may include a plurality of set of images correlated to a plurality of anatomical models. An “anatomical model” for the purposes of this disclosure refers to a 3D representation of anatomical object. In one or more embodiments, a particular set of images 1124 within anatomy training data may be correlated to a particular anatomical model. In one or more embodiments, anatomy training data may further include a plurality of ECG data and sets of images 1124 correlated to a plurality of anatomical models. In an embodiment, a particular set of images 1124 and a particular ECG data may be 104 Attorney Docket No.1518-103PCT1 correlated to a particular anatomical model. In one or more embodiments anatomy training data may include intracardiac echo diagrams, Cardiac CTs, ECG signals and/or ultrasonic images as an input and correlated 3D representations of anatomical objects. In one or more embodiments, machine learning module and/or anatomy modeling model may include a multimodal model configured to receive multiple simultaneous inputs and produce an output. A “multimodal model” for the purposes of this disclosure is a machine learning model configured to receive combined inputs from differing modalities and provide an output. For example, and without limitation, multimodal model may receive both text and/or images as an input and generate an output. In one or more embodiments, multimodal model may include a machine learning model configured to receive inputs from differing modalities. In one or more embodiments, multimodal mode may include a machine learning model configured to receive multiple inputs from different modalities simultaneously in order to generate an output. In one or more embodiments, multimodal model may receive ECG data from patient profile as an input and/or sets of images 1124 as an input and output 3D data structure and/or anatomical model. In one or more embodiments, data fusion may be used to determine the spatial relationships between data modalities such as ECG data and set of ECG images. In one or more embodiments, data fusion may include the process of extracting features from both ECG data and sets of images 1124 during training and determining spatial relationships between ECG data and sets of images using concatenation, attention mechanisms and the like. In one or more embodiments, training of multimodal model may include the use of supervised machine learning technique in which data sets of ECG data and sets of images are fed into the multimodal and the multimodal predicts output. In one or more embedment, multimodal model may be configured to generate 3D representation of an anatomical object such as a cardiac anatomy. In one or more embodiments, a combination of ultrasonic images and mapping catheters may be used to create a more detailed 3D representation of anatomical object. In one or more embodiments, a mapping catheter may be used to receive ECG data such as intracardiac electrograms. In one or more embodiments anatomy modeling model may first be configured to generate a first 3D model of anatomical object wherein data form mapping catheter may be used to generate a final 3D model of anatomical object. In one or more embodiments, mapping catheter and/or ECG data may be used to visualize electric activity of cardiac anatomy. In one or more embodiments, ECG data may be used to visualize a patient’s heart activity on a 3D generated structure. 105 Attorney Docket No.1518-103PCT1 With continued reference to FIG.11, multimodal model may be configured to receive sets of images as an input and output 3D representation of anatomical object. In one or more embodiments, multimodal model may then be configured to receive ECG data and overlay an electroanatomic map onto 3D representation of anatomical object. In one or more embodiments, the combination of ECG data and sets of images may allow for a 3D representation of anatomical object with an overlay of electrical activity associated with the patient. In one or more embodiments, each input into multimodal model may aid in the visualization of a different aspect of 3D representation of anatomical object. In a non-limiting example, ultrasonic images may generates 3D model of heart chambers in real-time using Intracardiac Echo, even without location sensor, CT/MR imaging biomarkers may Visualize precision structures and biomarkers derived from cardiac CT/MR (fibrosis, wall thickness, epicardial fat) and ECG data may Predict electroanatomic map using multi-modal cardiac data. With continued reference to FIG.11, multimodal model may utilize longitudinal multimodal data in order to generate outputs. “Longitudinal multimodal data” for the purposes of this disclosure refers to information collected form multiple sources over an extended period of time. In one or more embodiments, longitudinal multimodal data may include text, images, audio, video, physiological changes of a patient and the like. In one or more embodiments, patient profile may include longitudinal multimodal data. In one or more embodiments, longitudinal multimodal data may include information such as but not limited to, ECG signals ultrasounds images, medical records, clinical notes, radiology scans, molecular diagnostics, pathology screenings, electrophysiologic results, lab results and the like. In one or more embodiments, longitudinal multimodal data may be used by multimodal model in order to generate more detailed 3D representation of anatomical object. For example, and without limitation, longitudinal multimodal data may be used to generate electroanatomic maps as described in further detail below. Still referring to FIG, 11, as used in this disclosure, a “computed tomography (CT) based anatomical object model” refers to a 3D representation of anatomical object and surrounding structures that is created using data from CT scans. In one or more embodiments, CT based anatomical model includes anatomical model. Computed Tomography is a medical imaging technique that uses X-rays to capture cross-sectional images (slices) of the body. By taking a plurality of slices, a CT scan creates a detailed 3D representation of the internal 106 Attorney Docket No.1518-103PCT1 structure. In an embodiment, CT-based anatomical object model may include 3D representations of the heart including chambers, valves, blood vessels, and surrounding tissues. In some cases, CT-based anatomical object model may be interactive; for instance, medical professionals may rotate, zoom, and/or explore CT-based anatomical object model from various angles. In some cases, plurality of CT-based anatomical object models may be generated prior to the training of the anatomy modeling model. Plurality of CT-based anatomical object models may be generated using existing techniques in the field as described above such as, without limitation, FAM, cardiac CT merging, among others. In a non-limiting example, plurality of CT-based anatomical object models may provide ground through or references models against anatomy modeling model that is being trained. In a non-limiting example, generating data structure of organ further includes training anatomy modeling model using anatomy training data described herein. Anatomy modeling model trained using anatomy training data may be able to interpret ultrasonic images by learning relationships between ultrasonic images and corresponding CT-based anatomical object models. Processor 1104 is further configured to generate data structure of organ as a function of set of images 1124 using trained anatomy modeling model. In some cases, data structure e.g., 3D VOR may be interpreted, visualized, and analyzed by processor 1104 in similar manner to CT-based anatomical object models, wherein both are 3D structures that correspond to ultrasonic images. With continued reference to FIG.11, anatomy training data may include synthetic echocardiograms. In one or more embodiments, CT scans and/or already existing 3D models may be used to generate synthetic echocardiogram in order to augment anatomy training data increase the amount of training data present. In one or more embodiments, the generation and/or addition of synthetic echocardiograms may allow anatomy modeling model to generate more accurate outputs. In one or more embodiments, diffusion transformers may be used to generate synthetic echo diagrams using synthetic CT images. In one or more embodiments, the diffusion transformer may be trained to map detailed features from CT scans to corresponding echocardiogram features. In one or more embodiments, the diffusion transformer may then generate noisy images and iteratively generate synthetic echocardiogram based on learned features between the original CT scans and the original echocardiograms. In one or more embodiments, data collection for use in a diffusion transformer may include the collection of CT images and corresponding echocardiograms. In one or more embodiment, a machine learning 107 Attorney Docket No.1518-103PCT1 model may be trained to extract relevant features between the CT images and the echocardiograms using techniques such as CNN to capture spatial details. In one or more embodiments, a diffusion model may be configured to CT images until they resemble random noise. In one or more embodiments, the diffusion model may then be trained to reverse this process until the CT images are de-noised. In one or more embodiments, a transformer network may be configured to utilize recognized features between CT images and echocardiograms in order to generate synthetic echocardiograms. In one or more embodiments, the diffusion transformer may be trained using supervised learning in order to create synthetic echocardiograms which may then be used for training data within multimodal model. With continued reference to FIG.11, in an embodiment, anatomy modeling model includes a deep neural network (DNN). As used in this disclosure, a “deep neural network” is defined as a neural network with two or more hidden layers. Neural network is described in further detail with reference to FIGS.5-7. In a non-limiting example, anatomy modeling model may include a convolutional neural network (CNN). Generating 3d data structure of organ may include training CNN using anatomy training data and generating 3d data structure as a function of set of images 1124 using trained CNN. A “convolutional neural network,” for the purpose of this disclosure, is a neural network in which at least one hidden layer is a convolutional layer that convolves inputs to that layer with a subset of inputs known as a “kernel,” along with one or more additional layers such as pooling layers, fully connected layers, and the like. In some cases, CNN may include, without limitation, a deep neural network (DNN) extension. Mathematical (or convolution) operations performed in the convolutional layer may include convolution of two or more functions, where the kernel may be applied to input data e.g., set of images 1124 through a sliding window approach. In some cases, convolution operations may enable processor 1104 to detect local/global patterns, edges, textures, and any other spatial features described herein within each ultrasonic image of set of images 1124. Spatial features may be passed through one or more activation functions, such as without limitation, Rectified Linear Unit (ReLU), to introduce non-linearities into the processing step of generating 3d data structure of organ. Additionally, or alternatively, CNN may also include one or more pooling layers, wherein each pooling layer is configured to reduce the dimensionality of input data while preserving essential features within the input data. In a non-limiting example, CNN may include one or more pooling layer configured to reduce the spatial dimensions of spatial feature maps by applying 108 Attorney Docket No.1518-103PCT1 downsampling, such as max-pooling or average pooling, to small, non-overlapping regions of one or more spatial features. Still referring to FIG.11, CNN may further include one or more fully connected layers configured to combine spatial features extracted by the convolutional and pooling layers as described above. In some cases, one or more fully connected layers may allow for higher-level pattern recognition. In a non-limiting example, one or more fully connected layers may connect every neuron (i.e., node) in its input to every neuron in its output, functioning as a traditional feedforward neural network layer. In some cases, one or more fully connected layers may be used at the end of CNN to perform high-level reasoning and produce the final output such as, without limitation, a 3d data structure of organ. Further, each fully connected layer may be followed by one or more dropout layers configured to prevent overfitting, and one or more normalization layers to stabilize the learning process described herein. With continued reference to FIG.11, CNN may further include a 3D CNN, wherein the 3D CNN, unlike standard 2D CNN, may include utilization of one or more 3D convolutions which allow them to directly process 3D data, thereby enabling processor 1104 to generate 3D structures such as 3d data structure of organ using the 3D CNN. In a non-limiting example, 3D CNN may include one or more 3D filters (i.e., kernels) that move through the set of images 1124 in three dimensions and capturing spatial relationships in x, y, and z axis. Similar to 3D convolutions, 3D CNN may further include one or more 3D pooling layers that may be used to reduce the dimensionality of ultrasonic images while preserving spatial features as described above. Additionally, or alternatively, an encoder-decoder structure may be implemented (extended to 3D), by processor 1104, in 3D CNN, wherein the encoder-decoder structure includes an encoding path that captures the context and a decoding path that enables precise localization in a same manner as U-net as described above. Such encoder-decoder structures may also include a plurality of skip connections, allowing 3D CNN to use information from multiple resolutions to improve the process of generating 3d data structure of organ. With continued reference to FIG.11, in an embodiment, training the anatomy modeling model (i.e., CNN) may include selecting a suitable loss function to guide the training process. In a non-limiting example, a loss function that measures the difference between the predicted 3D VORs and the ground truth 3D structure e.g., CT-based anatomical object models may be used, such as, without limitation, mean squared error (MSE) or a custom loss function 109 Attorney Docket No.1518-103PCT1 may be designed for one or more embodiments described herein. Additionally, or alternatively, optimization algorithms, such as stochastic gradient descent (SGD), may then be used to adjust the anatomy modeling model’s parameters to minimize such loss. In a further non-limiting embodiment, instead of directly predicting 3d data structure, anatomy modeling model may be trained as a regression model to predict presence indicators and/or other embedded values described herein for each voxel of plurality of voxels within a 3D grid. Additionally, CNN may be extended with additional deep learning techniques, such as recurrent neural networks (RNNs) or attention mechanism, to capture additional features and/or data relationships within input data. These extensions may further enhance the accuracy and robustness of the anatomical object modeling. With continued reference to FIG.11, processor 1104 may use a machine learning module to implement one or more algorithms or generate one or more machine learning models, such as an anatomy modeling model to generate 3d data structure of organ. However, the machine learning module is exemplary and may not be necessary to generate one or more machine learning models and perform any machine learning described herein. In one or more embodiments, one or more machine-learning models may be generated using training data. Training data may include inputs and corresponding predetermined outputs so that a machine- learning model may use correlations between the provided exemplary inputs and outputs to develop an algorithm and/or relationship that then allows machine-learning model to determine its own outputs for inputs. Training data may contain correlations that a machine-learning process may use to model relationships between two or more categories of data elements. Exemplary inputs and outputs may come from a database, such as any database described in this disclosure, or be provided by a user. In other embodiments, a machine-learning module may obtain a training set by querying a communicatively connected database that includes past inputs and outputs. Training data may include inputs from various types of databases, resources, and/or user inputs and outputs correlated to each of those inputs so that a machine-learning model may determine an output. Correlations may indicate causative and/or predictive links between data, which may be modeled as relationships, such as mathematical relationships, by machine-learning models, as described in further detail below. In one or more embodiments, training data may be formatted and/or organized by categories of data elements by, for example, associating data elements with one or more descriptors corresponding to categories of data elements. As a non- limiting example, training data may include data entered in standardized forms by persons or 110 Attorney Docket No.1518-103PCT1 processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data may be linked to descriptors of categories by tags, tokens, or other data elements. In a further embodiment, training data may include previous outputs such that one or more machine learning models iteratively produces outputs. Still referring to FIG.11, machine learning module may be used to generate anatomy modeling model and/or any other machine learning models, such as, shape identification model as described in further detail below, using training data. Anatomy modeling model may be trained by correlated inputs and outputs of training data. Training data may be data sets that have already been converted from raw data whether manually, by machine, or any other method. In an embodiment, generating data structure of organ includes receiving anatomy training data, wherein the anatomy training data may include a plurality of image sets as input and a plurality of computed tomography (CT) based anatomical object models as output, and wherein each image set of plurality of image sets may include any images described in this disclosure. In some cases, anatomy training data may be received from Image database or other databases. In other cases, anatomy training data may be collected by a data acquisition unit from external sources such as one or more medical equipment’s e.g., imaging devices or diagnostic tools, wherein the data acquisition may be configured as an intermediary between the data source and machine learning module. In one or more embodiments, anatomy training data may include a plurality of set of images correlated to a plurality of anatomical models. An “anatomical model” for the purposes of this disclosure refers to a 3D representation of anatomical object. In one or more embodiments, a particular set of images 1124 within anatomy training data may be correlated to a particular anatomical model. In one or more embodiments, anatomy training data may further include a plurality of ECG data and sets of images 1124 correlated to a plurality of anatomical models. In an embodiment, a particular set of images 1124 and a particular ECG data may be correlated to a particular anatomical model. In one or more embodiments anatomy training data may include intracardiac echo diagrams, Cardiac CTs, ECG signals and/or ultrasonic images as an input and correlated 3D representations of anatomical objects. In one or more embodiments, machine learning module and/or anatomy modeling model may include a multimodal model configured to receive multiple simultaneous inputs and produce an output. A “multimodal 111 Attorney Docket No.1518-103PCT1 model” for the purposes of this disclosure is a machine learning model configured to receive combined inputs from differing modalities and provide an output. For example, and without limitation, multimodal model may receive both text and/or images as an input and generate an output. In one or more embodiments, multimodal model may include a machine learning model configured to receive inputs from differing modalities. In one or more embodiments, multimodal mode may include a machine learning model configured to receive multiple inputs from different modalities simultaneously in order to generate an output. In one or more embodiments, multimodal model may receive ECG data from patient profile as an input and/or sets of images 1124 as an input and output 3D data structure and/or anatomical model. In one or more embodiments, data fusion may be used to determine the spatial relationships between data modalities such as ECG data and set of ECG images. In one or more embodiments, data fusion may include the process of extracting features from both ECG data and sets of images 1124 during training and determining spatial relationships between ECG data and sets of images using concatenation, attention mechanisms and the like. In one or more embodiments, training of multimodal model may include the use of supervised machine learning technique in which data sets of ECG data and sets of images are fed into the multimodal and the multimodal predicts output. In one or more embedment, multimodal model may be configured to generate 3D representation of an anatomical object such as a cardiac anatomy. In one or more embodiments, a combination of ultrasonic images and mapping catheters may be used to create a more detailed 3D representation of anatomical object. In one or more embodiments, a mapping catheter may be used to receive ECG data such as intracardiac electrograms. In one or more embodiments anatomy modeling model may first be configured to generate a first 3D model of anatomical object wherein data form mapping catheter may be used to generate a final 3D model of anatomical object. In one or more embodiments, mapping catheter and/or ECG data may be used to visualize electric activity of cardiac anatomy. In one or more embodiments, ECG data may be used to visualize a patient’s heart activity on a 3D generated structure. With continued reference to FIG.11, multimodal model may be configured to receive sets of images as an input and output 3D representation of anatomical object. In one or more embodiments, multimodal model may then be configured to receive ECG data and overlay an electroanatomic map onto 3D representation of anatomical object. In one or more embodiments, the combination of ECG data and sets of images may allow for a 3D 112 Attorney Docket No.1518-103PCT1 representation of anatomical object with an overlay of electrical activity associated with the patient. In one or more embodiments, each input into multimodal model may aid in the visualization of a different aspect of 3D representation of anatomical object. In a non-limiting example, Ultrasonic images may generates 3D model of heart chambers in real-time using Intracardiac Echo, even without location sensor, CT/MR imaging biomarkers may Visualize precision structures and biomarkers derived from cardiac CT/MR (fibrosis, wall thickness, epicardial fat) and ECG data may Predict electroanatomic map using multi-modal cardiac data. With continued reference to FIG.11, multimodal model may utilize longitudinal multimodal data in order to generate outputs. “Longitudinal multimodal data” for the purposes of this disclosure refers to information collected form multiple sources over an extended period of time. In one or more embodiments, longitudinal multimodal data may include text, images, audio, video, physiological changes of a patient and the like. In one or more embodiments, patient profile may include longitudinal multimodal data. In one or more embodiments, longitudinal multimodal data may include information such as but not limited to, ECG signals ultrasounds images, medical records, clinical notes, radiology scans, molecular diagnostics, pathology screenings, electrophysiologic results, lab results and the like. In one or more embodiments, longitudinal multimodal data may be used by multimodal model in order to generate more detailed 3D representation of anatomical object. For example, and without limitation, longitudinal multimodal data may be used to generate electroanatomic maps as described in further detail below. Still referring to FIG, 11, as used in this disclosure, a “computed tomography (CT) based anatomical object model” refers to a 3D representation of anatomical object and surrounding structures that is created using data from CT scans. In one or more embodiments, CT based anatomical model includes anatomical model. Computed Tomography is a medical imaging technique that uses X-rays to capture cross-sectional images (slices) of the body. By taking a plurality of slices, a CT scan creates a detailed 3D representation of the internal structure. In an embodiment, CT-based anatomical object model may include 3D representations of the heart including chambers, valves, blood vessels, and surrounding tissues. In some cases, CT-based anatomical object model may be interactive; for instance, medical professionals may rotate, zoom, and/or explore CT-based anatomical object model from various angles. In some cases, plurality of CT-based anatomical object models may be generated prior to the training of 113 Attorney Docket No.1518-103PCT1 the anatomy modeling model. Plurality of CT-based anatomical object models may be generated using existing techniques in the field as described above such as, without limitation, FAM, cardiac CT merging, among others. In a non-limiting example, plurality of CT-based anatomical object models may provide ground through or references models against anatomy modeling model that is being trained. In a non-limiting example, generating data structure of organ further includes training anatomy modeling model using anatomy training data described herein. Anatomy modeling model trained using anatomy training data may be able to interpret ultrasonic images by learning relationships between ultrasonic images and corresponding CT-based anatomical object models. Processor 1104 is further configured to generate data structure of organ as a function of set of images 1124 using trained anatomy modeling model. In some cases, data structure e.g., 3D VOR may be interpreted, visualized, and analyzed by processor 1104 in similar manner to CT-based anatomical object models, wherein both are 3D structures that correspond to ultrasonic images. With continued reference to FIG.11, anatomy training data may include synthetic echocardiograms. In one or more embodiments, CT scans and/or already existing 3D models may be used to generate synthetic echocardiogram in order to augment anatomy training data increase the amount of training data present. In one or more embodiments, the generation and/or addition of synthetic echocardiograms may allow anatomy modeling model to generate more accurate outputs. In one or more embodiments, diffusion transformers may be used to generate synthetic echo diagrams using synthetic CT images. In one or more embodiments, the diffusion transformer may be trained to map detailed features from CT scans to corresponding echocardiogram features. In one or more embodiments, the diffusion transformer may then generate noisy images and iteratively generate synthetic echocardiogram based on learned features between the original CT scans and the original echocardiograms. In one or more embodiments, data collection for use in a diffusion transformer may include the collection of CT images and corresponding echocardiograms. In one or more embodiment, a machine learning model may be trained to extract relevant features between the CT images and the echocardiograms using techniques such as CNN to capture spatial details. In one or more embodiments, a diffusion model may be configured to CT images until they resemble random noise. In one or more embodiments, the diffusion model may then be trained to reverse this process until the CT images are de-noised. In one or more embodiments, a transformer network 114 Attorney Docket No.1518-103PCT1 may be configured to utilize recognized features between CT images and echocardiograms in order to generate synthetic echocardiograms. In one or more embodiments, the diffusion transformer may be trained using supervised learning in order to create synthetic echocardiograms which may then be used for training data within multimodal model. With continued reference to FIG.11, in an embodiment, anatomy modeling model includes a deep neural network (DNN). As used in this disclosure, a “deep neural network” is defined as a neural network with two or more hidden layers. Neural network is described in further detail with reference to FIGS.5-7. In a non-limiting example, anatomy modeling model may include a convolutional neural network (CNN). Generating 3d data structure of organ may include training CNN using anatomy training data and generating 3d data structure as a function of set of images 1124 using trained CNN. A “convolutional neural network,” for the purpose of this disclosure, is a neural network in which at least one hidden layer is a convolutional layer that convolves inputs to that layer with a subset of inputs known as a “kernel,” along with one or more additional layers such as pooling layers, fully connected layers, and the like. In some cases, CNN may include, without limitation, a deep neural network (DNN) extension. Mathematical (or convolution) operations performed in the convolutional layer may include convolution of two or more functions, where the kernel may be applied to input data e.g., set of images 1124 through a sliding window approach. In some cases, convolution operations may enable processor 1104 to detect local/global patterns, edges, textures, and any other spatial features described herein within each ultrasonic image of set of images 1124. Spatial features may be passed through one or more activation functions, such as without limitation, Rectified Linear Unit (ReLU), to introduce non-linearities into the processing step of generating 3d data structure of organ. Additionally, or alternatively, CNN may also include one or more pooling layers, wherein each pooling layer is configured to reduce the dimensionality of input data while preserving essential features within the input data. In a non-limiting example, CNN may include one or more pooling layer configured to reduce the spatial dimensions of spatial feature maps by applying downsampling, such as max-pooling or average pooling, to small, non-overlapping regions of one or more spatial features. Still referring to FIG.11, CNN may further include one or more fully connected layers configured to combine spatial features extracted by the convolutional and pooling layers as described above. In some cases, one or more fully connected layers may allow for higher-level 115 Attorney Docket No.1518-103PCT1 pattern recognition. In a non-limiting example, one or more fully connected layers may connect every neuron (i.e., node) in its input to every neuron in its output, functioning as a traditional feedforward neural network layer. In some cases, one or more fully connected layers may be used at the end of CNN to perform high-level reasoning and produce the final output such as, without limitation, a 3d data structure of organ. Further, each fully connected layer may be followed by one or more dropout layers configured to prevent overfitting, and one or more normalization layers to stabilize the learning process described herein. With continued reference to FIG.11, CNN may further include a 3D CNN, wherein the 3D CNN, unlike standard 2D CNN, may include utilization of one or more 3D convolutions which allow them to directly process 3D data, thereby enabling processor 1104 to generate 3D structures such as 3d data structure of organ using the 3D CNN. In a non-limiting example, 3D CNN may include one or more 3D filters (i.e., kernels) that move through the set of images 1124 in three dimensions and capturing spatial relationships in x, y, and z axis. Similar to 3D convolutions, 3D CNN may further include one or more 3D pooling layers that may be used to reduce the dimensionality of ultrasonic images while preserving spatial features as described above. Additionally, or alternatively, an encoder-decoder structure may be implemented (extended to 3D), by processor 1104, in 3D CNN, wherein the encoder-decoder structure includes an encoding path that captures the context and a decoding path that enables precise localization in a same manner as U-net as described above. Such encoder-decoder structures may also include a plurality of skip connections, allowing 3D CNN to use information from multiple resolutions to improve the process of generating 3d data structure of organ. With continued reference to FIG.11, in an embodiment, training the anatomy modeling model (i.e., CNN) may include selecting a suitable loss function to guide the training process. In a non-limiting example, a loss function that measures the difference between the predicted 3D VORs and the ground truth 3D structure e.g., CT-based anatomical object models may be used, such as, without limitation, mean squared error (MSE) or a custom loss function may be designed for one or more embodiments described herein. Additionally, or alternatively, optimization algorithms, such as stochastic gradient descent (SGD), may then be used to adjust the anatomy modeling model’s parameters to minimize such loss. In a further non-limiting embodiment, instead of directly predicting 3d data structure, anatomy modeling model may be trained as a regression model to predict presence indicators and/or other embedded values 116 Attorney Docket No.1518-103PCT1 described herein for each voxel of plurality of voxels within a 3D grid. Additionally, CNN may be extended with additional deep learning techniques, such as recurrent neural networks (RNNs) or attention mechanism, to capture additional features and/or data relationships within input data. These extensions may further enhance the accuracy and robustness of the anatomical object modeling. With continued reference to FIG.11, in some cases, SSM 1116 may be generated by processor 1104 as a function of a set of labeled example shapes, each in a form of point-based representations or meshes. In some cases, example shapes may be represented in a 3D voxel occupancy representation (VOR). With continued reference to FIG.11, in some cases, organ model 1112 may include a 3D voxel occupancy representation (VOR) of the patient’s heart. With continued reference to FIG.11, in some cases, organ model 1112 may include a 3D voxel occupancy representation (VOR) of the patient’s organ. As used in this disclosure, a "3D voxel occupancy representation (VOR)" is a 3D digital representation of a spatial structure of the anatomy of an organ, wherein the representation is composed of a plurality of discrete volumetric elements known as voxels. A “voxel,” for the purpose of this disclosure, is a 3D equivalent of a pixel in 2D imaging. While a pixel represents a point in a 2D image and may include properties such as color and/or brightness, a voxel may represent a volume in a 3D space and may include additional properties such density/occupancy as described below. In an embodiment, each voxel of plurality of voxels within 3D VOR may represent a specific portion of the organ. With continued reference to FIG.11, in some cases, voxel may be a smallest distinguishable box-shaped part (i.e., 11px ^x 11px ^x 11px) of a three-dimensional image of an organ. In some cases, each voxel of plurality of voxels within 3D VOR may be represented as a cube or rectangular prism (although other shapes may be used in specialized applications). Each voxel may include a size that determines a resolution of the 3D image or model. In an embodiment, smaller voxels may provide higher resolution; however, it may require more computational resources (e.g., RAM) for processor 1104 to process. In an embodiment, each voxel may include one or more embedded values (i.e., specific numerical or categorical data associated with each voxel). In some cases, embedded values may represent various attributes or characteristics of the corresponding portion of an organ that voxel represents. In a non-limiting example, embedded values may include density values, intensity values, texture information, or 117 Attorney Docket No.1518-103PCT1 any other quantitative measures that provide insights into the underlying cardiac tissue. In another non-limiting example, each voxel may include a presence indicator i.e., a data element that indicates a presence or absence (i.e., occupancy) of cardiac tissue within that portion as described in U.S. Pat. App. Ser. No.118/376,688. Such embedded values may be derived from the corresponding labels of the example shape. With continued reference to FIG.11, in some cases, processor 1104 may be configured to align the set of labeled example shapes to a common reference frame using rigid, affine, or otherwise non-rigid registration methods to generate SSM 1116. For example, and without limitation, the rigid registration might involve translations and rotations to superimpose the shapes; affine registration could incorporate scaling, shearing, and other linear transformations; while non-rigid methods might employ B-splines, thin-plate splines, or diffeomorphic transformations to flexibly map one shape onto another. In some cases, an averaged position of each corresponding point (or voxel) across all example shapes may be calculated using formula ^̅^_^ ൌ ^{^} ே^∑ே ^_ୀ^ ^^_^^ , where ^̅^_^ is the mean position of the ^^th point (or voxel), ^^_^^ is the position of in the ^^th example shape, and N is the total number of

example shapes in the labeled set. In some cases, principle component analysis (PCA) may be applied to the aligned shapes to extract at least a primary mode of variation. As described herein, a “primary mode of variation” is a mode of variation that have the most significant variability, wherein the “mode of variation,” for the purpose of this disclosure, is a specific pattern or direction of a shape change. In some cases, such significancy may be indicated by the first principal component in PCA. In some cases, a plurality of modes of variation may be extracted, wherein each mode (or principal component) may represent a specific way the shape of organ may be deformed from the mean shape, determined by one or more eigenvectors of the covariance matrix of the aligned shapes. In a non-limiting example, eigenvector with the highest eigenvalue may represent primary mode of variation which captures the largest amount of shape variability within example shapes, while subsequent modes (eigenvectors) capture decreasing amounts of variability. With continued reference to FIG.11, in some cases, once modes of variation are extracted, processor 1104 may be configured to create a shape representation for any given organ shape within the studied class. In some cases, organ model 1112 may be constructed using SSM 1116, wherein the organ model 1112 may integrate mean shape and plurality of modes of 118 Attorney Docket No.1518-103PCT1 variation. In a non-limiting example, organ model 1112 having a shape ^^ may be mathematically _{represented as ^^ ൌ ^^} ^̅ _^ ^∑ெ ^_{ୀ^ ^^^ ൈ ^^ ^, wherein ^^} ^̅ _{denotes the mean shape derived from the set of} example shapes, ^^ is the number of modes of variation considered, ^^_^ are the coefficients or weights for each mode, and ^^_^ are the modes of variation (eigenvectors corresponding to the ^^th principal component). In some cases, coefficients ^^_^ may dictate a degree to which each mode of variation is present in shape ^^. In some cases, coefficients ^^_^ may vary from positive to negative (or negative to positive) based on the deformation of the organ model 1112 in directions described by each mode of variation. In some cases, organ model 1112 may include mean shape as described herein. In some cases, organ model 1112 may include a predictive organ shape that may not have been explicitly seen in the set of example shapes or patient’s organ observations. In some cases, organ model 1112 may be in 3D VOR as described above. With continued reference to FIG.11, in some cases, processor 1104 may be configured to construct organ model 1112 based on a patient profile 1120 using a computer vision module. As used in this disclosure, a “patient profile” is a comprehensive collection of information related to an individual patient. In some cases, patient profile may include a variety of different types of data that, when combined, provide a detailed picture of a patient's overall health. In an embodiment, patient profile may include demographic data of patient, for example, and without limitation, patient profile may include basic information about the patient such as name, age, gender, ethnicity, socioeconomic status, and/or the like. In another embodiment, each patient profile may also include a patient’s medical history, for example, and without limitation, patient profile may include a detailed record of the patient's past health conditions, medical procedures, hospitalizations, and illnesses such as surgeries, treatments, medications, and/or the like. In another embodiment, each patient profile may include lifestyle Information of patient, for example, and without limitation, patient profile may include details about the patient's diet, exercise habits, smoking and alcohol consumption, and other behaviors that could impact health. In a further embodiment, patient profile may include patient’s family history, for example, and without limitation, patient profile may include a record of hereditary diseases. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various type of data within patient profiles apparatus 1100 may receive and process in consistent with this disclosure. 119 Attorney Docket No.1518-103PCT1 With continued reference to FIG.11, in one or more embodiments, patient profile 1120 may include a set of images 1124 of patient’s organ and associated metadata. In some embodiments, patient profile 1120 may include a set of images 1124 of a patient’s heart. In some cases, set of images 1124 may include a plurality of computed tomography (CT) scans of the patient’s organ. Computed Tomography is a medical imaging technique that uses X-rays to capture cross-sectional images (slices) of the body. By taking a plurality of slices, a CT scan creates a detailed 3D representation of the internal structure. Other exemplary embodiments of set of images 1124 may include, without limitation, X-ray images, magnetic resonance imaging (MRI) scans, ultrasound images, optical images, digital photographs, or any other form of visual data. Set of images 1124 may include, without limitation, a two-dimensional image. In some embodiments, set of images 1124 may include an ultrasound image. As used herein, an “ultrasound image” is an image generated as a function of a reflection of a sound wave off of a structure. Non-limiting examples of ultrasonic images and/or imaging techniques include intracardiac echo (ICE) images, transthoracic echocardiograms (TTE), transesophageal echocardiograms (TEE), and point of care ultrasound (POCUS). In some embodiments, set of images 1124 may include a CT image correlated to an ultrasound image. A “computer vision module,” for the purpose of this disclosure, is a computation component designed to perform one or more computer vision, image processing, and/or modeling tasks. In some cases, computer vision module may receive patient profile 1120 and generate organ model 1112 as a function of set of images 1124 (and associated metadata). In one or more embodiment, computer vision module may include an image processing module. In some cases, set of images 1124 may be pre- processed using an image processing module. As used in this disclosure, an “image processing module” is a component designed to process digital images such as set of images 1124. For example, and without limitation, image processing module may be configured to compile plurality of images of a multi-layer scan to create an integrated image. In an embodiment, image processing module may include a plurality of software algorithms that can analyze, manipulate, or otherwise enhance an image, such as, without limitation, a plurality of image processing techniques as described below. In some cases, computer vision module may also include hardware components such as, without limitation, one or more graphics processing units (GPUs) that can accelerate the processing of large amount of images. In some cases, computer vision module may be implemented with one or more image processing libraries such as, without 120 Attorney Docket No.1518-103PCT1 limitation, OpenCV, PIL/Pillow, ImageMagick, and the like. In a non-limiting example, one or more image processing tasks, such as noise reduction, contrast enhancement, intensity normalization, image segmentation and/or the like may be performed by computer vision module on plurality of CT scans to isolate heart and major vascular structures from surrounding tissues. In some cases, one or more machine learning models may be used to perform CT scans segmentations, for example, and without limitation, a U-net (i.e., a convolution neural network containing a contracting path as an encoder and an expansive path as a decoder, wherein the encoder and the decoder forms a U-shaped structure). With continued reference to FIG.11, in an embodiment, such segmentation of the organ may include a plurality of pixel values e.g., 0~255, each representing the presence of heart tissue (or organ tissue) at that location. In a non-limiting example, computer vision module may be configured to generate a mesh representation of patient’s organ based on plurality of CT or ultrasound scan segmentations or other image segmentations, wherein the mesh representation may include a 3D VOR as described above, using Pix2Vox. Additionally, or alternatively, exemplary computer vision tasks may include, without limitation, object recognition, feature detection, edge/corner detection, and the like. Non-limiting examples of feature detection may include scale invariant feature transform (SIFT), Canny edge detection, Shi Tomasi corner detection, and the like. In some cases, generating mesh representation of patient’s organ may include employing, by computer vision module, one or more transformations to orient one or more images relative a 3D coordinate system; exemplary transformations include without limitation homography transforms and affine transforms. Computer vision model may implement one or more 3D modeling algorithms, such as without limitation, marching cubes, contour detection and segmentation, active contour models, and/or the like to generate a coherent 3D representation based on the mesh representation of patient’s organ e.g., organ model 1112. In other cases, generic 3D modeling techniques may be applied by computer vision module to generate organ model 1112. In some cases, generic 3D modeling techniques may include surface modeling, solid modeling, or parametric modeling, among others. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various image processing, computer vision, and modeling tasks may be performed by processor 1104 to generate organ model 1112 of patient’s organ from a set of organ images. 121 Attorney Docket No.1518-103PCT1 With continued reference to FIG.11, in some cases, processor 1104 may be configured to perform shape extraction from segmented CT or ultrasound scans, for example, and without limitation, marching cubes algorithm or similar techniques may be employed to convert the voxel-based representation from CT or ultrasound segmentation into a mesh, wherein the mesh may represent the outer surface of the patient’s heart and/or other organ. In some cases, the mesh may vary in resolutions, with more grid capturing finer details. In some cases, a consistent number of landmark points may be used to represent patient’s heart surface. In a non- limiting example, one or more landmark points may be manually annotated by professional e.g., medical expert, ensuring that the landmark points correspond to specific anatomical locations of patient’s heart or other organ. In other cases, they may be automatically derived using one or more computer vision algorithms as described herein. Landmark points may be uniformly spaced across the surface of extracted shape. In some cases, the size of the organ shape may be normalized so that the number of landmark points remain consistent between different organ shapes. In some cases, SSM 1116 may include an implementation of generalized Procrustes analysis (GPA) to find a desired rigid transformation (translation, rotation) that aligns the set of example shapes. In a non-limiting example, processor 1104 may be configured to minimize the sum of squared distance between corresponding landmark points across each organ shape. In some cases, size normalization may be reverted after alignment after such alignment. Constructing organ model 1112 may include combining the mean shape computed by averaging the positions of corresponding landmarks points and one or more modes of variations. In a non- limiting example, organ model 1112 may include a template model generated based a plurality of standard anatomical templates as described in U.S. Pat. App. Ser. No.118/376,688. With continued reference to FIG.11, in some cases, receiving heat model 1112 may include extracting set of images 1124 from patient profile (subsequent to patient identity verification and obtaining consent from subject). In some cases, patient profile may be obtained through hospital information system (HIS) or any other data acquisition platform to securely access patient’s electronic medical record (EMR) or other relevant databases. set of images 1124 may be directly or indirectly downloaded or exported. In some cases, each CT scan of set of images 1124 may be in a usable and/or computer-readable format such as, without limitation, DICOM format, and necessary metadata such as, without limitation, patient information, study information, image modality, CT scanner information, slice thickness, pixel spacing, matrix size, 122 Attorney Docket No.1518-103PCT1 and/or the like may be included. In some cases, metadata may also include acquisition parameters such as, without limitation, tube voltage (kV), tube current (mA), exposure time, total dose length product (DLP), CT dose index (CTDI), rotation time, number of acquisitions, contrast agent used (if any), contrast phase, and/or the like. In some cases, receiving organ model 1112 may include recording the access and extraction of set of images 1124 from patient profile 1120; for instance, and without limitation, this process may be documented, by processor 1104, in the patient’s medical record, databases, or other appropriate logs. With continued reference to FIG.11, in some cases, organ model 1112 may be directly imported from a dedicated database 1128 or repository containing pre-constructed anatomical models. In some cases, database 1128 may be based on historical patient scans, expert-constructed models, and/or the like. For instance, and without limitation, an organ model repository may consist of models derived from diverse population, capturing various cardiac pathologies, anomalies, or physiological states. Database 1128 may be implemented, without limitation, as a relational database, a key-value retrieval database such as a NOSQL database, or any other format or structure for use as a database that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Database 1128 may alternatively or additionally be implemented using a distributed data storage protocol and/or data structure, such as a distributed hash table or the like. Database 1128 may include a plurality of data entries and/or records as described above. Data entries in database 1128 may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in database 1128 or another relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a database may store, retrieve, organize, and/or reflect data and/or records as used herein, as well as categories and/or populations of data consistently with this disclosure. With continued reference to FIG.11, it should be noted that SSM 1116 may not be the only method for receiving organ model 1112. For example, in some cases, organ model 1112 may be directly imported from one or more external sources. In a non-limiting example, organ model 1112 may be received from a dedicated computer software e.g., specialized software solutions available for medical imaging and 3D model generation. In some cases, organ model 1112 may be exported such software which may provide model segmentation, rendering, and 123 Attorney Docket No.1518-103PCT1 generation capabilities tailored for cardiac structures. In another non-limiting example, one or more third-party platforms (for patient data management diagnostic imaging, and other healthcare functionalities) that support DICOM standards may allow for extraction and sharing organ model 1112 for synthetizing medical images as described in detail below. In a non-limiting example, organ model 1112 may be received from several medical imaging and modeling services that are available on cloud. Such organ model 1112 may be sourced from a cloud-based service (e.g., SaaS). With continued reference to FIG.11, in one or more embodiments, receiving the organ model 1112 may include transforming organ model 1112 to a second organ model as a function of a plurality of mode changers within SSM 1116, wherein each mode changer of the plurality of mode changers is associated with a model feature of organ model 1112. As used in this disclosure, a “mode changer” is an algorithmic component derived from PCA configured to encapsulate a specific mode of variation as described above (representing a distinct way in which the shape of organ model 1112 may deviate from the mean shape). A “model feature,” for the purpose of this disclosure, is a distinct, recognizable and quantifiable attribute or characteristic of the organ model 1112. For example, and without limitation, model feature may include an anatomical feature such as the size and curvature of the ventricles, the thickness of the heart wall, thickness of an organ wall, the positioning of heart valves, or the like. In a non-limiting example, a mode changer may be associated with the size variation of the left ventricle identified within organ model 1112. Such mode changer may be adjusted to modify the volume of the left ventricle, resulting in a second heart model that mimics potential biological variations or specific patient conditions that is different from original organ model 1112. In some cases, multiple mode changers of SSM 1116 may be adjusted simultaneously. With continued reference to FIG.11, additionally, or alternatively, processor 1104 may receive a plurality of shape parameter sets. In some cases, organ model 1112 may be described directly using plurality of shape parameters. In some cases, shape parameters may correspond to a plurality of modes of variations or mode changers as described above. As used in this disclosure, a “shape parameters” are numerical values or descriptors that quantitatively represent the geometric or morphological characteristics of patient’s heart or other organ. In a non-limiting example, plurality of shape parameters may include information and/or metadata calculated, determined, and/or extracted from patient profile 1120 and/or set of images 1124, 124 Attorney Docket No.1518-103PCT1 such as, dimensions, angles, curvatures, surface areas, texture, symmetry, and/or the like. In other embodiments, processor 1104 may be configured to parameterize (model) features (e.g., edges, textures, contours, and the like) using CNN described in detail below. Such parameterization may involve processor 1104 to derive one or more shape parameters including one or more morphological descriptors that quantitatively describe patient’s organ based on extracted features. In some cases, transforming organ model 1112 may include adjusting one or more shape parameters by adjusting associated mode changers. With continued reference to FIG.11, in some cases, patient profile 1120 may further include electrocardiogram (ECG) data 1132, wherein the “ECG data,” for the purpose of this disclosure, refers to data related to an electrocardiogram of the patient that corresponds to the patient profile. A “electrocardiogram,” as described herein, is a medical test that records the electrical activity of subject’s heart over a period of time. In an embodiment, ECG data 1132 may include one or more recordings captured by a plurality of electrodes placed on patient’s skin. In one or more embodiments, ECG data 1132 may include information regarding a P wave, T wave, QRS complex, PR interval, ST segment, and/or the like. In some cases, ECG data 1132 may be used to identify specific cardiac events or phases of a cardiac cycle e.g., isovolumic relaxation, ventricular filling, isovolumic contraction, and rapid ventricular ejection. In a non- limiting example, patient profile 1120 and ECG data 1132 described herein may be consistent with any patient profile and ECG data disclosed in this disclosure. With continued reference to FIG.11, processor 1104 is configured to identify a region of interest (ROI) 1136 within organ model 1112. As described in this disclosure, a “region of interest” is a specific and pre-defined spatial subset of an image or a 3D model. In some cases, ROI 1136 include a volume that has been designated for closer analysis or further processing as described in detail below due to its potential significance or relevance in synthesizing medical images. In some cases, identifying ROI 1136 within organ model 1112 may include isolating ROI 1136 with surrounding structure of organ model 1112 that may be less relevant. In some cases, ROI 1136 may be manually selected by a user. In some cases, one or more graphical tools and/or imaging software may be used to outline a particular area on organ model 1112 or an image captured from organ model 1112. In other cases, processor 1104 may be configured to automatically detect and define ROI 1136. In an embodiment, a computer vision module configured to perform one or more computer vision tasks such as, without limitation, 125 Attorney Docket No.1518-103PCT1 thresholding, edge detection, or machine learning process may be used to recognize ROI 1136 with specific features or anomalies. With continued reference to FIG.11, in some cases, ROI 1136 may also include temporal ROI. In an embodiment, ROI 1136 may not be spatial but also temporal. In some cases, a specific timeframe within a sequence may be designated as a ROI. In a non-limiting example, temporal ROI may focus on a specific time segment or interval within a dynamic dataset e.g., organ model 1112 with animation that simulating a cardiac cycle. In some cases, temporal ROI may change over time. For example, and without limitation, temporal ROI may include a time- series images capturing patient’s heart activity, or a sequence showcasing blood flow within the cardiac structure. In a non-limiting example, ROI 1136 may include a temporal ROI set to capture a specific phase of cardiac cycle such as systole or diastole. In other cases, ROI 1136 may include a hierarchical ROI. In a non-limiting example, processor may identify one or more smaller sub-ROIs within a larger ROI, each with its significance or weight. With continued reference to FIG.11, identifying ROI 1136 includes locating at least a point of view 1140 on organ model 1112 and determining a view angle 1144 corresponding to the at least a point of view 1140. As used in this disclosure, a “point of view” is a specific spatial location or origin form which an image or scene is observed or captured. In a non-limiting example, point of view 1140 may be configured to mimic the location of a camera e.g., an intracardiac echocardiography (ICE) probe within or near patient’s heart (without a magnetic field which is required in case of using a real ICE probe). In a non-limiting example, point of view 1140 may be configured to mimic the location of a camera e.g. view from an ultrasound transducer. Conventional Biosense/carto ICE catheters or other mapping catheters may use a specific magnetic location system – which requires specific equipment that creates magnetic field around the patient to generate a triangular location i.e., ROI 1136. When patient moves, the magnetic coordinates remain the same but relative location in intracardiac chambers may change, yielding undesired results. In some cases, at least a point of view 1140 may be imagined as the location of the ultrasound probe or other probe’s tip. In some cases, at least a point of view 1140 may determine from where within organ model 1112 or its vicinity the “pseudo” ultrasound waves are emitted and received. Given that ICE is a type of endoluminal ultrasound, in some cases, at least a point of view 1140 may be intracardiac which may be located inside heart chambers. Exemplary point of view may include, without limitation, ventricular point of view, 126 Attorney Docket No.1518-103PCT1 atrial point of view, near-valvular point of view, and/or the like. In a non-limiting example, ROI 1136 may be identified and at least a point of view 1140 may be located on the left ventricle’s wall, targeting its thickness and motion to assess potential cardiomyopathy. With continued reference to FIG.11, a “view angle,” for the purpose of this disclosure, is an angular orientation or direction from at least a point of view 1140. In some cases, view angle 1144 may determine the segment of the scene or image that is visible or captured. In some cases, at least a point of view 1140 and corresponding view angle 1144 defines at least one field of view (FOV) 1148 that include at least a portion of organ model 1112. In a non-limiting example, view angle 1144 may reflect the orientation of an imaging plane relative to the structure of interest within identified ROI 1136. In some cases, view angle 1144 corresponding to at least a view 1140 may define the tilt of the imaging plane, determining which structures come into FOV 1148. In some cases, FOV 1148 may indicate an area of a scene that may be captured by a camera within defined bounds (e.g., spatial boundary of ROI 1136) of organ model 1112. Exemplary view angle 1144 may include apical view (visualize patient’s organ from its apex), parasternal view (oriented laterally from the mid-sternal line), subcostal view (with angle inferiorly positioned). In some cases, view angle 1144 may be corresponding to the angle of the sector of a resultant medical image such as an ultrasound or ICE image as described in detail below (resembles a sector or-pie slice shape), wherein the ultrasound probe tip may act as the sector’s apex (i.e., point of view 1140) that delineates the ultrasound wave’s spread and hence, the captured anatomy’s width. In a non-limiting example, a narrower view angle may be chosen to focus on a specific region of patient’s organ e.g., a valve. Conversely, a broader view angle may capture more extensive organ region, offering a comprehensive overview of organ model 1112. With continued reference to FIG.11, in an embodiment, one or more machine learning models may be used to automatically identify a desired ROI 1136 that captures most clinically relevant portion of organ model 1112. In such an embodiment, desired ROI 1136 may include key anatomical structures or pathological indicators. processor 1104 may use a machine learning module to implement one or more algorithms as described herein or generate one or more machine learning models, such as a ROI identification model to identify ROI 1136 within organ model 1112. However, the machine learning module is exemplary and may not be necessary to generate one or more machine learning models and perform any machine learning 127 Attorney Docket No.1518-103PCT1 described herein. In one or more embodiments, one or more machine-learning models may be generated using training data. Training data may include inputs and corresponding predetermined outputs so that a machine-learning model may use correlations between the provided exemplary inputs and outputs to develop an algorithm and/or relationship that then allows machine-learning model to determine its own outputs for inputs. Training data may contain correlations that a machine-learning process may use to model relationships between two or more categories of data elements. Exemplary inputs and outputs may come from a database, such as any database described in this disclosure, or be provided by a user. In other embodiments, a machine-learning module may obtain a training set by querying a communicatively connected database that includes past inputs and outputs. Training data may include inputs from various types of databases, resources, and/or user inputs and outputs correlated to each of those inputs so that a machine-learning model may determine an output. Correlations may indicate causative and/or predictive links between data, which may be modeled as relationships, such as mathematical relationships, by machine-learning models, as described in further detail below. In one or more embodiments, training data may be formatted and/or organized by categories of data elements by, for example, associating data elements with one or more descriptors corresponding to categories of data elements. As a non-limiting example, training data may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data may be linked to descriptors of categories by tags, tokens, or other data elements. In a further embodiment, training data may include previous outputs such that one or more machine learning models iteratively produces outputs. With continued reference to FIG.11, in some cases, ROI identification model may be trained using training data containing a plurality of annotated organ models as input correlated to a plurality of desired ROIs as output. In some embodiments, training data for ROI identification model may also include a plurality of organ models and patient profiles including associated metadata (e.g., patient information, study information, acquisition parameters, and/or the like as described above) as input correlated to a plurality of point of views and corresponding view angles as output. In such embodiment, processor 1104 may recognize and highlight at least a point of view and a corresponding view angle for each organ model based on associated metadata, wherein the at least a point of view and the corresponding view angle may together 128 Attorney Docket No.1518-103PCT1 define a field of view that related to a desired ROI. Processor 1104 may then be configured to identify ROI 1136 using the trained ROI identification model. Automatically identification of ROI 1136 as described herein may overcome the afore mentioned limitations of magnetic location system, wherein the location and orientation of the ultrasound probe may be looked up reversely through ROI identification model. With continued reference to FIG.11, in some embodiments, location of the ROI 1136 or point of view may be done using a catheter without a magnetic sensor using a sensorless technique as disclosed in U.S. Non-provisional Application No.118/376,688. In some embodiments, ROI 1136 and/or point of view may be determined using fiducial point-based registration. In some embodiments, fiducial point-based registration may include receiving ultrasound, or another medical image. A set of points may be located on the medical image. A second set of corresponding points may be located on organ model 1112. Processor 1104 may compute a rigid transformation between the set of points and the second set of corresponding points. In some embodiments, the set of points and the second set of corresponding points (i.e. the points used to compute the rigid transformation) may be referred to as fiducials. Processor 1104 may apply the rigid transformation to other points on the medical image to map the other points of the medical image to organ model 1112. In some embodiments, fiducial registration error may be minimized using a least squares method. With continued reference to FIG.11, in some embodiments, medical image from a catheter may be overlayed onto organ model 1112 using the identified point of view. In some embodiments, this may include applying a rigid transformation to points of medical image to map it onto organ model 1112. In some embodiments, medical image may include an ultrasound. In some embodiments, medical image may include a CT scan. In some embodiments, medical image may include a TTE. In some embodiments, medical image may include a TEE. In some embodiments, medical image may include a POCUS. In some embodiments, medical image may include an EGM. In some embodiments, organ model with overlayed medical image may be displayed to a user through user interface 1172 on display device 1168. Still referring to FIG.11, in one or more embodiments, apparatus and methods described herein may involve one or more aspects of precise reconstructing of the left atrium (LA), pulmonary veins (PV), and left atrial appendage (LAA) in atrial fibrillation (AF) ablation. “Atrial fibrillation (AF),” as described herein, is a cardiac arrhythmia characterized by irregular 129 Attorney Docket No.1518-103PCT1 and often rapid heart rate. In some cases, AF may lead to various complications, including, without limitation, stroke, heart failure, and/or the like. “AF ablation,” as described herein, is a procedure that aims to isolate and eliminate the abnormal electrical pathways causing the cardiac arrhythmia. LA, PV, and LAA are key structures involved in AF. In an embodiment, precise 3D reconstruction of LA, PV, and LAA may help in understanding their geometry and relationships which are essential for planning and/or executing AF ablation. In some cases, LA, PV, and LAA may be mapped in order to enable clinicians to identify one or more specific sites responsible for AF, allowing targeted ablation that minimizes damages to surrounding tissues. Additionally, or alternatively, apparatus and methods described herein may reduce the risk of complications such as, without limitation, perforation, stenosis, collateral damage, among others to adjacent structures. Apparatus and methods described herein may ensure ablation energy is delivered to the intended locations. Still referring to FIG.11, in some embodiments, processor 1104 may determine a Left Atrial Appendage Occlusion Device placement as a function of the 3D model. In some embodiments, a computing device may determine a size of a Left Atrial Appendage Occlusion Device placement as a function of the 3D model. As a non-limiting example, processor 1104 may determine a diameter of left atrial appendage of the heart model (e.g., organ model 1112) and use the diameter to determine the desired size of a left atrial appendage occlusion device. In some embodiments, a computing device may determine whether there is leakage resulting from Left Atrial Appendage Occlusion Device placement as a function of the 3D model. In some embodiments, a determined Left Atrial Appendage Occlusion Device size, placement, and/or leakage may be displayed to a user, such as by a display device. Still referring to FIG.11, in some embodiments, an apparatus and/or method described herein may allow ultrasonic imaging to replace and/or be an alternative to MRIs and/or CT scans. This may limit radiation exposure of subjects. Additionally, this may provide an option suitable for subjects with implants. With continued reference to FIG.11, processor 1104 is configured to generate at least a medical image 1152 as a function of ROI 1136 using an image generator 1156. As used in this disclosure, a “medical image” is a visualization of the interior of a body to diagnose, monitor, or provide information about the internal structures, organs, or systems of the patient. In some cases, medical image 1152 may include, without limitation, X-ray image, echocardiogram, 130 Attorney Docket No.1518-103PCT1 magnetic resonance imaging (MRI) scan, CT scan, ultrasound image, optical image, digital photograph, and/or the like. In a non-limiting example, medical image 1152 may include an intracardiac echocardiography (ICE) image. In some embodiments, medical image 1152 may include an ultrasound image such as a TTE, TEE, ICE, or POCUS ultrasound. As used in this disclosure, an “ICE image” is an ultrasound image obtained from within the heart’s chambers or blood vessels. As used in this disclosure, an “ultrasound image” is an ultrasound image of a patient’s organ. In some cases, at least an ultrasound image may be captured using a specialized catheter equipped with an ultrasound transducer that is inserted into the body and guided to the heart of subject. In an embodiment, at least a medical image 1152 may provide a detailed and real-time visualizations of cardiac anatomy i.e., structural composition of patient’s heart and its associated blood vessels. In a non-limiting example, at least a medical image 1152 may include at least an ultrasound image captures an anatomical structure of the at least a portion of organ model 1112. In some cases, anatomical structure may include, without limitation, chambers (e.g., four chambers including left and right atria and left and right ventricles), valves (i.e., the structures that regulate blood flow between chambers and vessels, including mitral, tricuspid, aortic, and pulmonary valves), vessels (e.g., aorta, pulmonary arteries and veins, and coronary arteries), conduction system (i.e., a network of specialized cells that control the heart’s electrical activity and rhythm), muscular and connective tissues (e.g., heart’s muscular walls, septa, any other connective tissues that provide structural integrity and enable contraction), LAA and other appendages, pathological features (e.g., any abnormalities, defects, and/or the like), among others. With continued reference to FIG.11, in a non-limiting example, at least a medical image 1152 may include a particular view of patient’s heart chambers, valves, vessel, and/or the like. In some cases, processor 1104 may be configured to generate a plurality of medical images, or at least a medical image 1152 containing a plurality of views e.g., different angles and perspectives of organ model 1112. In some cases, plurality of medical images may be arranged in a temporal sequence. In a non-limiting example, plurality of medical images may include a series of ultrasound images captured over time, allowing for an observation of dynamic cardiac functions such as beating, blood flow, and/or the like as described above. In some cases, at least a medical image 1152 may include a corresponding timestamp, wherein the timestamp may include an indicator showing a date and time of when the corresponding ultrasound image was 131 Attorney Docket No.1518-103PCT1 generated. In a further non-limiting example, at least an ultrasound image may include a synthetic ultrasound frame as described in detail below with reference to FIG.3 With continued reference to FIG.11, as used in this disclosure, an “image generator” is a system, apparatus, or software module designed to produce or synthesize visual representations (images) based on certain input data. In an embodiment, image generator 1156 may be configured to generate at least a medical image 1152 based on input data such as, without limitation, organ model 1112, ROI 1136, at least a point of view 1140 and corresponding view angle 1144, among others. In some cases, generation performed by image generator 1156 may be rooted in real-world data, simulated data, or a combination of both. In some cases, image generator 1156 include a software component that processes raw data from one or more imaging device e.g., MRI, CT, or ultrasound machines, and reconstruct it into interpretable visual displays. With continued reference to FIG.11, in some cases, image generator 1156 may include implementations of one or more camera transformation programs 1160. As used in this disclosure, a “camera transformation program” is a software or algorithm that manipulate location, perspective, and orientation of a virtual camera in relation to an object or scene. In an embodiment, camera transformation program 1160 may be executed to effectively transform or alter how organ model 1112 within ROI 1136 is visualized, simulating the effects of physically moving or adjusting a real-world camera e.g., an ICE probe, ultrasound probe, or other probe. In some cases, camera transformation program 1160 may involve moving at least a virtual camera’s potion in 3D space. In some cases, virtual camera may be placed at the at least a point of view 1140. In some cases, virtual camera may be in the same object space with organ model 1112. In a non-limiting example, camera transformation program 1160 may include translation configured to shift camera left, right, up, down, forward, or backward. In some cases, camera transformation program 1160 may include one or more instructions on configuring virtual camera’s orientation based on a horizontal or vertical axes, for example, and without limitation, virtual camera may be configured to pitch (tilt up or down), yaw (turn left or right), or roll (tilt sideways). In some cases, camera transformation program 1160 may adjust virtual camera’s perspective to “zoom” in or out on organ model 1112. In a non-limiting example, generating at least a medical image 1152 may include generating at least an ICE image or ultrasound image by executing camera 132 Attorney Docket No.1518-103PCT1 transformation program 1160 to simulate at least a perspective of an ICE probe, ultrasound probe, or other probe using the image generator 1156. With continued reference to FIG.11, in one or more embodiment executing camera transformation program 1160 may include generating a projection of the anatomical structure 1164 by rendering ROI 1136 as a function of a set of imaging parameters using virtual camera positioned at the at least a point of view 1140 with the corresponding view angle 1144. As used in this disclosure, an “anatomical structure projection” is a depiction of a 3D anatomical structure such as a part of organ model 1112 onto a 2D plane or surface. In some cases, such projection of anatomical structure may capture spatial and/or morphological features of one or more anatomical structure as described herein as it would appear from at least a view 1140 or under certain imaging parameters. As used in this disclosure, a “set of imaging parameters” refers to a collection of specific variables and configurations (of virtual camera) that determined how medical image 1152 are generated, processed, and visualized. In some cases, set of imaging parameters may replicate the intricacies of real-world ultrasound imaging. In some cases, users e.g., clinicians or medical professionals may manually set or adjust set of imaging parameters through user interface as described below. In other cases, imaging parameters may be auto- detected based on an initial generation of medical image 1152 and/or preliminary data. For example, and without limitation, image parameters may include a pre-defined set of parameters configured for viewing particular organ regions or structures of the mean shape. One or more machine learning models as described herein may be implemented to adjust set of image parameters iteratively based on the quality or clarity of the initial scan until a desired medical image are achieved. With continued reference to FIG.11, in a non-limiting example, camera transformation program 1160 may be configured to simulate projection as if an ICE probe, ultrasound probe, or other probe is inserted from the apex of the patent’s organ and angled towards the mitral valve, giving a detailed view of the valve’s leaflets and adjoining heart structures. In some cases, camera transformation program 1160 may be configured to determine how 3D objects e.g., organ model 1112 are projected onto 2D visual plane. Exemplary image projections may include, without limitation, orthographic (parallel) projection, perspective (converging lines) projection, and the like. In a non-limiting example, for a close-up detailed view of ROI 1136 without depth distortions, an orthographic projection may be preferred, while 133 Attorney Docket No.1518-103PCT1 for a more holistic view of how structures related to one another in 3D space, a perspective projection may be more apt. With continued reference to FIG.11, in one or more embodiments, processor 1104 may implement one or more aspects of “generative artificial intelligence(AI),” a type of AI that uses machine learning algorithms to create, establish, or otherwise generate data such as, without limitation, medical image 1152 as described herein that is similar to one or more provided training examples. In an embodiment, machine learning module described herein may generate one or more generative machine learning models that are trained on one or more set of example medical images previously generated. One or more generative machine learning models may be configured to generate new examples that are similar to the training data of the one or more generative machine learning models but are not exact replicas; for instance, and without limitation, data quality or attributes of the generated examples may bear a resemblance to the training data provided to one or more generative machine learning models, wherein the resemblance may pertain to underlying patterns, features, or structures found within the provided training data. With continued reference to FIG.11, in some cases, image generator 1156 may include a generative machine learning model having one or more generative models. As described herein, “generative models” refers to statistical models of the joint probability distribution ^^^ ^^, ^^^ on a given observable variable x, representing features or data that can be directly measured or observed (e.g., organ model 1112, set of images 1124 and associated metadata, among others) and target variable y, representing the outcomes or labels that one or more generative models aims to predict or generate (e.g., medical image 1152). In some cases, generative models may rely on Bayes theorem to find joint probability; for instance, and without limitation, Naïve Bayes classifiers may be employed by computing device to categorize input data such as, without limitation, organ model 1112 derived from CT scans into different views. With continued reference to FIG.11, in a non-limiting example, one or more generative machine learning models may include one or more Naïve Bayes classifiers generated, by processor 1104, using a Naïve bayes classification algorithm. Naïve Bayes classification algorithm generates classifiers by assigning class labels to problem instances, represented as vectors of element values. Class labels are drawn from a finite set. Naïve Bayes classification algorithm may include generating a family of algorithms that assume that the value of a 134 Attorney Docket No.1518-103PCT1 particular element is independent of the value of any other element, given a class variable. Naïve Bayes classification algorithm may be based on Bayes Theorem expressed as P(A/B)= P(B/A) P(A)÷P(B), where P(A/B) is the probability of hypothesis A given data B also known as posterior probability; P(B/A) is the probability of data B given that the hypothesis A was true; P(A) is the probability of hypothesis A being true regardless of data also known as prior probability of A; and P(B) is the probability of the data regardless of the hypothesis. A naïve Bayes algorithm may be generated by first transforming training data into a frequency table. Processor 1104 may then calculate a likelihood table by calculating probabilities of different data entries and classification labels. Processor 1104 may utilize a naïve Bayes equation to calculate a posterior probability for each class. A class containing the highest posterior probability is the outcome of prediction. With continued reference to FIG.11, although Naïve Bayes classifier may be primarily known as a probabilistic classification algorithm; however, it may also be considered a generative model described herein due to its capability of modeling the joint probability distribution ^^^ ^^, ^^^ over observable variables X and target variable Y. In an embodiment, Naïve Bayes classifier may be configured to make an assumption that the features X are conditionally independent given class label Y, allowing generative model to estimate the joint distribution as ^^^ ^^, ^^^ ൌ ^^^ ^^^∏ ^^ ^^^ ^^ ^^ ∣ ^^^, wherein ^^^ ^^^ may be the prior probability of the class, and ^^^ ^^_^| ^^^ is the conditional probability of each feature given the class. One or more generative machine learning models containing Naïve Bayes classifiers may be trained on labeled training data, estimating conditional probabilities ^^^ ^^_^| ^^^ and prior probabilities ^^^ ^^^ for each class; for instance, and without limitation, using techniques such as Maximum Likelihood Estimation (MLE). One or more generative machine learning models containing Naïve Bayes classifiers may select a class label ^^ according to prior distribution ^^^ ^^^, and for each feature ^^_^, sample at least a value according to conditional distribution ^^^ ^^_^| ^^^. Sampled feature values may then be combined to form one or more new data instance with selected class label ^^. In a non-limiting example, one or more generative machine learning models may include one or more Naïve Bayes classifiers to generate new medical images such as ultrasound images as a function of input data such as, without limitation, at least a point of view 1140 and corresponding view angle 1144, wherein the models may be trained using training data containing a plurality of organ models and ROIs as described herein as input correlated to a plurality of ultrasound images. In 135 Attorney Docket No.1518-103PCT1 some embodiments, the models may be trained using a plurality of organ models of a particular organ (e.g., a heart, liver, brain, kidney, and the like) and ROS as described herein as input correlated to a plurality of ultrasound images. With continued reference to FIG.11, in some cases, one or more generative machine learning models may include generative adversarial network (GAN). As used in this disclosure, a “generative adversarial network” is a type of artificial neural network with at least two sub models (e.g., neural networks), a generator, and a discriminator, that compete against each other in a process that ultimately results in the generator learning to generate new data samples, wherein the “generator” is a component of the GAN that learns to create hypothetical data by incorporating feedbacks from the “discriminator” configured to distinguish real data from the hypothetical data. In some cases, generator may learn to make discriminator classify its output as real. In an embodiment, discriminator may include a supervised machine learning model while generator may include an unsupervised machine learning model as described in further detail with reference to FIGS.5-7. With continued reference to FIG.11, in an embodiment, discriminator may include one or more discriminative models, i.e., models of conditional probability ^^^ ^^| ^^ ൌ ^^^ of target variable Y, given observed variable X. In an embodiment, discriminative models may learn boundaries between classes or labels in given training data. In a non-limiting example, discriminator may include one or more classifiers as described in further detail below with reference to FIG.5 to distinguish between different categories e.g., real vs. fake, or states e.g., TRUE vs. FALSE within the context of generated data such as, without limitations, generated medical image 1152, and/or the like. In some cases, processor 1104 may implement one or more classification algorithms such as, without limitation, Support Vector Machines (SVM), Logistic Regression, Decision Trees, and/or the like to define decision boundaries. With continued reference to FIG.11, in a non-limiting example, generator of GAN may be responsible for creating synthetic data that resembles real medical images. In some cases, GAN may be configured to receive organ model 1112 and/or set of images 1124 as input and generates corresponding examples of medical images containing information describing organ anatomy in different ultrasound (e.g., ICE) views. In some cases, processor 1104 may be configured to train GAN using a plurality of anatomical structure 1164 projections as described above and synthesizing at least a medical image 1152 using the trained GAN at the at least a 136 Attorney Docket No.1518-103PCT1 point of view 1140 with corresponding view angle 1144. In some cases, during synthesizing at least a medical image 1152, discriminator of GAN may evaluate the authenticity of the generated content by comparing it to true medical images, for example, discriminator may distinguish between genuine and generated content and providing feedback to generator to improve the model performance. Additionally, or alternatively, GAN may include a conditional GAN as an extension of the basic GAN as described herein that allows for generation of medical images using organ model 1112 and/or set of images 1124 based on certain conditions or labels. In standard GAN, generator may produce samples from random noise, while in a conditional GAN, generator may produce samples based on random noise and a given condition or label. With continued reference to FIG.11, additionally, or alternatively, one or more generative models may also include a variational autoencoder (VAE). As used in this disclosure, a “variational autoencoder” is an autoencoder (i.e., an artificial neural network architecture) whose encoding distribution is regularized during the model training process in order to ensure that its latent space includes desired properties allowing new data sample generation. In an embodiment, VAE may include a prior and noise distribution respectively, trained using expectation-maximization meta-algorithms such as, without limitation, probabilistic PCA, sparse coding, among others. In a non-limiting example, VEA may use a neural network as an amortized approach to jointly optimize across input data and output a plurality of parameters for corresponding variational distribution as it maps from a known input space to a low-dimensional latent space. Additionally, or alternatively, VAE may include a second neural network, for example, and without limitation, a decoder, wherein the “decoder” is configured to map from the latent space to the input space. With continued reference to FIG.11, in some cases, processor 1104 may be configured to continuously monitor image generator 1156. In an embodiment, processor 1104 may configure discriminator to provide ongoing feedback and further corrections as needed to subsequent input data. An iterative feedback loop may be created as processor 1104 continuously receive real-time data, identify errors (e.g., distance between generated medical image 1152 and real medical images) as a function of real-time data, delivering corrections based on the identified errors, and monitoring subsequent model outputs and/or user feedbacks on the delivered corrections. In an embodiment, processor 1104 may be configured to retrain one or more generative machine learning models within image generator 1156 based on user 137 Attorney Docket No.1518-103PCT1 modified/annotated medical images or update training data of one or more generative machine learning models within image generator 1156 by integrating validated medical images (i.e., subsequent model output) into original training data. In such embodiment, iterative feedback loop may allow image generator 1156 to adapt to the user’s needs and performance requirements, enabling one or more generative machine learning models described herein to learn and update based on user responses and generated feedbacks. With continued reference to FIG.11, in some embodiments, image generator 1156, or any generative model thereof, may be trained using image training data. Image training data may include exemplary organ models correlated to exemplary medical images. In some embodiments, image training data may include exemplary organ models and FOVs correlated to exemplary medical images. With continued reference to FIG.11, other exemplary embodiments of generative machine learning models may include, without limitation, long short-term memory networks (LSTMs), (generative pre-trained) transformer (GPT) models, mixture density networks (MDN), and/or the like. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various generative machine learning models that may be used to generate medical image 1152 as described herein. In a further non-limiting embodiment, image generator 1156 may be further configured to generate a multi-model neural network that combines various neural network architectures described herein. In a non-limiting example, multi-model neural network may combine LSTM for time-series analysis with GPT models for natural language processing. Such fusion may be applied by computing device to generate medical image 1152. In some cases, multi-model neural network may also include a hierarchical multi-model neural network, wherein the hierarchical multi-model neural network may involve a plurality of layers of integration; for instance, and without limitation, different models may be combined at various stages of the network. Convolutional neural network (CNN) may be used for image feature extraction, followed by LSTMs for sequential pattern recognition, and a MDN at the end for probabilistic modeling. Other exemplary embodiments of multi-model neural network may include, without limitation, ensemble-based multi-model neural network, cross- modal fusion, adaptive multi-model network, among others. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various generative machine learning models that may be used to synthetize medical images as described herein. As an 138 Attorney Docket No.1518-103PCT1 ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various multi-model neural network and combination thereof that may be implemented by apparatus 1100 in consistent with this disclosure. With continued reference to FIG.11, apparatus 1100 may further include a display device 1168. As used int his disclosure, a “display device” is an electronic device that visually presents information to a user. In an embodiment, display device may include an output interface that translates data such as, without limitation, generated medical image 1152 from processor 1104 or other computing devices into a visual form that can be easily understood by user. In some cases, generated medical image 1152 and/or other data described herein such as, without limitation, organ model 1112, patient profile 1120, and/or the like may also be displayed through display device 1168 using a user interface 1172. User interface 1172 may include a graphical user interface (GUI), wherein the GUI may include a window in which generated medical image 1152 and/or other data described herein may be displayed. In an embodiment, user interface 1172 may include one or more graphical locator and/or cursor facilities allowing user to interact with generated medical image 1152 and/or any other data, or even process described herein; for instance, and without limitation, by using a touchscreen, touchpad, mouse, keyboard, and/or other manual data entry device, user may enter user input containing selecting specific regions, adding comments, adjusting parameter, and/or the like. In a non-limiting example, user interface 1172 may include one or more menus and/or panels permitting selection of measurements, models, visualization of data/model to be displayed and/or used, elements of data, functions, or other aspects of data/model to be edited, added, and/or manipulated, options for importation of and/or linking to application programmer interfaces (APIs), exterior services, data source, machine-learning models, and/or algorithms, or the like. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which a visual interface and/or elements thereof may be implemented and/or used as described in this disclosure. With continued reference to FIG.11, in some embodiments, display device 1168 may display medical images (including synthetic images) overlayed on organ model to a clinician during a medical procedure (for example, an ablation procedure); in some embodiments, this may be consistent with any embodiments described in this disclosure. 139 Attorney Docket No.1518-103PCT1 With continued reference to FIG.11, in some embodiments, a confidence may be displayed using display device 1168 (for example, a confidence overlayed on organ model 1112), which may be consistent with any embodiments described in this disclosure. With continued reference to FIG.11, in some embodiments, display device 1168 may be configured to display an organ model 1112 with an overlay, consistent with any embodiments described within this disclosure. With continued reference to FIG.11, in some cases, processor 1104 may be further configured to compile a plurality of medical images into a video 1176 as a function of ECG data 1132, wherein the video is synchronized with a cardiac cycle indicated by ECG data 1132. As used in this disclosure, a “video” is a sequential arrangement of plurality of images played over time. In a non-limiting embodiment, video 1176 may include an ultrasound video, for example an ICE video, wherein the ultrasound video may capture dynamic changes and movements within organ model 1112 or related structures over a certain duration by playing a plurality of ultrasound images arranged based on ECG data 1132. In some cases, processor 1104 may be configured to identify distinct phases or cardiac cycle based on ECG data 1132 including, but is not limited to P-wave, QRS complex, T-wave, and/or the like as described above. Plurality of ultrasound images may be segmented based on each ultrasound image’s timestamp or acquisition sequence, associating each ultrasound image with a specific phase or time point within the cardiac cycle. Processor 1104 may then synchronize the segmented images with the corresponding phases of the cardiac cycle derived from ECG data 1132, ensuring temporal alignment between echocardiographic visualizations and the electrophysiological events. In a non-limiting example, processor 1104 may be configured to sequentially assemble plurality of medical images in accordance with the chronological progression of the cardiac cycle, resulting in a continuous video 1176 that accurately reflects patient’s organ's dynamic movements and changes in response to treatments. In some cases, one or more interpolation or frame blending techniques may be applied by processor 1104 to ensure smooth transitions between consecutive medical images and eliminate visual discontinuities in the synthesized video 1176. In a non- limiting example, an video such as an ultrasound video 1176 synchronized with ECG data 1132 may show valve’s opening and closing in tandem with specific points on the ECG waveform e.g., P wave or T wave when observing the mitral valve’s movement during a cardiac cycle. 140 Attorney Docket No.1518-103PCT1 With continued reference to FIG.11, in some cases, plurality of generated medical images may be used as training data to train other machine learning models that requires consistent medical image input. In some cases, processor 1104 may generate medical images training data by correlating generated medical images with sourced organ model 1112 (i.e., ground truth or reference). In some cases, parameters such as organ model 1112, ROI 1136, at least a view 1140, corresponding view angle 1144 may be adjusted during medical image generation, simulating various clinical scenarios or patient populations, aiding in creating machine learning models that developed for solving problems under different clinical scenarios based on patient’s medical images. In some cases, image generator 1156 may be able to produce a vast number of unique images, much more than what may be feasible to collect in a real-world clinical settings. Such large volume of data may be beneficial for training deep learning machine learning models, which typically require extensive datasets to achieve desired performance. Additionally, or alternatively, processor 1104 may perform data augmentation techniques on generated medical images, for example, and without limitation, rotations, scaling, or even small deformations may be applied, further expanding the dataset. In a non-limiting example, a cardiac anatomy modeling model as described in U.S. Pat. App. Ser. No.118/376,688, configured for generating a 3D model of a heart based on ICE images pertaining to a patient may be trained using such generated training data. As a person skilled in the art, upon reviewing the entirety of this disclosure, will recognize the importance of using synthesized medical images in training machine learning models. With continued reference to FIG.11, it should be noted that apparatus 1100 and methods described herein are not limited to cardiac applications but are expansively applicable to other organs, for instance, and without limitation, echo sensor’s visualization capabilities when employed to compute the location and orientation of the sensor (i.e., ROI 1136) within an organ may be effectively adapted for use within liver or other anatomical structures where precision and minimally invasive diagnostics are crucial. As a person skilled in the art, upon reviewing the entirety of this disclosure, will recognize one or more embodiments described herein, the underlaying principles may be readily transposable to a broader spectrum of medical imaging and intervention applications such as, without limitation, transcatheter intervention (which is rapidly supplanting traditional open surgery) that is not currently disclosed. 141 Attorney Docket No.1518-103PCT1 Now referring to FIG.12, a flow diagram of an exemplary method 1200 for synthetizing medical images is illustrated. The method 1200 includes a step 1205 of receiving, by at least a processor, a heart model related to a patient’s heart. In some embodiments, receiving the heart model may include constructing the heart model based on a patient profile pertaining to the patient using a computer vision module, wherein the patient profile may include a plurality of computed tomography (CT) scans of the patient’s heart and associated metadata. In some cases, the patient profile further may include electrocardiogram (ECG) data. In some embodiments, receiving the heart model may include transforming the heart model to a second heart model using a statistical shape model as a function of a plurality of mode changers within the Statistical Shape Model, wherein each mode changer of the plurality of mode changers is associated with a model feature of the heart model. In other cases, the heart model may include a 3D voxel occupancy representation (VOR) of the patient’s heart. This may be implemented, without limitation, as described above with reference to FIGS.1-11. With continued reference to FIG.12, method 1200 includes a step 1210 of identifying, by the at least a processor, a region of interest within the heart model, wherein identifying the region of interest includes locating at least a point of view on the heart model and determining a view angle corresponding to the at least a point of view, wherein the at least a point of view and the corresponding view angle define at least one field of view that include at least a portion of the heart model. This may be implemented, without limitation, as described above with reference to FIGS.1-11. With continued reference to FIG.12, method 1200 includes a step 1215 of generating, by the at least a processor, at least a medical image as a function of the region of interest using an image generator, wherein the at least a medical image captures an anatomical structure of the at least a portion of the heart model. In some embodiments, generating the at least a medical image may include executing a camera transformation program configured to simulate at least a perspective of an ICE probe, ultrasound probe, or other probe using the image generator. In some cases, executing the camera transformation program may include generating a projection of the anatomical structure by rendering the ROI as a function of a set of imaging parameters using a virtual camera positioned at the at least a point of view with the corresponding view angle. In some cases, the image generator may include a generative adversarial network (GAN). In some embodiments, generating the at least a medical image may 142 Attorney Docket No.1518-103PCT1 include training the GAN using a plurality of anatomical structure projections and synthesizing at least a medical image using the trained GAN at the at least a point of view with the corresponding view angle. This may be implemented, without limitation, as described above with reference to FIGS.1-11. With continued reference to FIG.12, method 1200 may further include a step of compiling, by the at least a processor, a plurality of medical images into a video as a function of the ECG data, wherein the video is synchronized with a cardiac cycle indicated by the ECG data. This may be implemented, without limitation, as described above with reference to FIGS.1-11. FIG.13 is a flow diagram of an exemplary method 1300 for synthetizing medical images. This may be implemented, without limitation, as described above with reference to FIGS.1-12. At step 1305, method 1300 includes receiving, by at least a processor, an ultrasound image of a patient's organ. In some embodiments, the ultrasound image of the patient’s organ may include a transesophageal echocardiogram image. In some embodiments, the ultrasound image of the patient’s organ may include a transthoracic echocardiogram image. In some embodiments, the ultrasound image of the patient’s organ may include a point-of-care ultrasound image. This may be implemented, without limitation, as described above with reference to FIGS. 1-12. At step 1310, method 1300 includes generating, by at least a processor, an organ model related to the patient's organ as a function of the ultrasound image. In some embodiments, generating the organ model includes transforming the organ model to a second organ model using a Statistical Shape Model as a function of a plurality of mode changers within the Statistical Shape Model, wherein each mode changer of the plurality of mode changers is associated with a model feature of the organ model. In some embodiments, the organ model may include a heart model. In some embodiments, the heart model may include a model feature, wherein the model feature includes a thickness of a heart wall. This may be implemented, without limitation, as described above with reference to FIGS.1-12. At step 1315, method 1300 includes identifying, by the at least a processor, a region of interest within the organ model, wherein identifying the region of interest includes: locating at least a point of view on the organ model and determining a view angle corresponding to the at least a point of view, wherein the at least a point of view and the corresponding view angle 143 Attorney Docket No.1518-103PCT1 define at least one field of view that include at least a portion of the organ model. In some embodiments, identifying the region of interest within the organ model may include selecting a first set of points from a medical image. In some embodiments, identifying the region of interest within the organ model may include determining a second set of points on the organ model corresponding to the first set of points. In some embodiments, identifying the region of interest within the organ model may include mapping a plurality of points of the medical image to the organ model using a relationship between the first set of points and the second set of points. In some embodiments, mapping the plurality of points of the medical image to the organ model using the relationship between the first set of points and the second set of points may include determining a rigid transformation from the first set of points to the second set of points. This may be implemented, without limitation, as described above with reference to FIGS.1-12. At step 1320, method 1300 includes generating, by the at least a processor, at least a medical image as a function of the region of interest using an image generator, wherein the at least a medical image captures an anatomical structure of the at least a portion of the organ model. In some embodiments, generating the at least a medical image may include generating a plurality of medical images. This may be implemented, without limitation, as described above with reference to FIGS.1-12. With continued reference to FIG.13, in some embodiments, method 1300 may include compiling, by the at least a processor, the plurality of medical images into a video. This may be implemented, without limitation, as described above with reference to FIGS.1-12. In some embodiments, method 1300 may include displaying, by the at least a processor, the video on a display device. This may be implemented, without limitation, as described above with reference to FIGS.1-12. At a high level, an apparatus and method for generating a three-dimensional (3d) model of patient’s organ with an overlay is disclosed. An overlay may include determining a level of uncertainty of outputs of models used, as described below, in regard to deciphering the geometric deposition of cardiac autonomy of a subject. In some cases, the level of uncertainty may be derived from variability within the distribution of shape parameters, image quality assessment, measurement errors and/or the like. The overly may be visualized on the heart model. In some cases, level of uncertainty may be color-coded, for example, a heat map may be overlaid on top of the heart model. In other cases, other visual cues e.g., symbols or indicators 144 Attorney Docket No.1518-103PCT1 that alert user to areas of heart model that may require extra caution when used for planning or guidance during an ICE procedure. Aspects of the present disclosure can be used to simplify the ultrasound assisted anatomy reconstruction system by using an AI based algorithm to learn the positioning and 3D reconstruction directly from the ultrasound images. This is so, at least in part, because apparatus is configured to implement AI-based learning from CT datasets. In an embodiment, neural networks based estimation removes the need for complicated systems, such as manual segmentation and reconstruction methods, specialized hardware, FAM, among others. In one or more embodiments, apparatus and methods described herein may involve one or more aspects of precise reconstructing of the left atrium (LA), pulmonary veins (PV), and left atrial appendage (LAA) in atrial fibrillation (AF) ablation. “Atrial fibrillation (AF),” as described herein, is a cardiac arrhythmia characterized by irregular and often rapid heart rate. In some cases, AF may lead to various complications, including, without limitation, stroke, heart failure, and/or the like. “AF ablation,” as described herein, is a procedure that aims to isolate and eliminate the abnormal electrical pathways causing the cardiac arrhythmia. LA, PV, and LAA are key structures involved in AF. In an embodiment, precise 3D reconstruction of LA, PV, and LAA may help in understanding their geometry and relationships which are essential for planning and/or executing AF ablation. In some cases, LA, PV, and LAA may be mapped in order to enable clinicians to identify one or more specific sites responsible for AF, allowing targeted ablation that minimizes damages to surrounding tissues. Additionally, or alternatively, apparatus and methods described herein may reduce the risk of complications such as, without limitation, perforation, stenosis, collateral damage, among others to adjacent structures. Apparatus and methods described herein may ensure ablation energy is delivered to the intended locations. Referring now to FIG.14, an exemplary embodiment of an apparatus 1400 for generating 3D model of a patient’s organ via machine-learning is illustrated. System includes at least a processor 1404. Processor 1404 may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Computing device may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. Processor 1404 may include a single computing device operating 145 Attorney Docket No.1518-103PCT1 independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. Processor 1404 may interface or communicate with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for connecting processor 1404 to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus, or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software etc.) may be communicated to and/or from a computer and/or a computing device. Processor 1404 may include but is not limited to, for example, a computing device or cluster of computing devices in a first location and a second computing device or cluster of computing devices in a second location. Processor 1404 may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. Processor 1404 may distribute one or more computing tasks as described below across a plurality of computing devices of computing device, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. Processor 1404 may be implemented using a “shared nothing” architecture in which data is cached at the worker, in an embodiment, this may enable scalability of apparatus 1400 and/or computing device. With continued reference to FIG.14, processor 1404 may be designed and/or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, processor 1404 may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent 146 Attorney Docket No.1518-103PCT1 repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Processor 1404 may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing. With continued reference to FIG.14, apparatus includes a memory 1408 communicatively connected to at least a processor 1404, wherein the memory 1408 contains instructions configuring at least a processor 1404 to perform any processing steps described herein. As used in this disclosure, “communicatively connected” means connected by way of a connection, attachment, or linkage between two or more relata which allows for reception and/or transmittance of information therebetween. For example, and without limitation, this connection may be wired or wireless, direct, or indirect, and between two or more components, circuits, devices, systems, and the like, which allows for reception and/or transmittance of data and/or signal(s) therebetween. Data and/or signals therebetween may include, without limitation, electrical, electromagnetic, magnetic, video, audio, radio, and microwave data and/or signals, combinations thereof, and the like, among others. A communicative connection may be achieved, for example and without limitation, through wired or wireless electronic, digital, or analog, communication, either directly or by way of one or more intervening devices or components. Further, communicative connection may include electrically coupling or connecting at least an output of one device, component, or circuit to at least an input of another device, component, or circuit. For example, and without limitation, via a bus or other facility for intercommunication between elements of a computing device. Communicative connecting may also include indirect connections via, for example and without limitation, wireless connection, radio communication, low power wide area network, optical communication, magnetic, capacitive, or optical coupling, and the like. In some instances, the terminology 147 Attorney Docket No.1518-103PCT1 “communicatively coupled” may be used in place of communicatively connected in this disclosure. With continued reference to FIG.14, processor 1404 may perform determinations, classification, and/or analysis steps, methods, processes, or the like as described in this disclosure using machine-learning processes. A “machine-learning process,” as used in this disclosure, is a process that automatedly uses a body of data known as “training data” and/or a “training set” (described further below in this disclosure) to generate an algorithm that will be performed by a processor 1404/module to produce outputs given data provided as inputs; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. Machine-learning process may utilize supervised, unsupervised, lazy-learning processes and/or neural networks, described further below. With continued reference to FIG.14, processor is configured to receive a set of images 1412 of a patient’s organ 1416 pertaining to a subject 1420. As used in this disclosure, a “set of images” refers to a collection or group of visual representations captured using an imaging modality, imaging technique, or both described herein. Set of images 1412 may include, without limitation, two-dimensional images. In an embodiment, set of images 1412 may include a set of intracardiac echocardiography (ICE) images, wherein the "set of ICE images” is a collection of ultrasound images obtained from within the heart’s chambers or blood vessels. Processor 1404 may receive set of images 1412 from a cardiac image capture device. As used herein, a “cardiac image capture device” is a device capable of capturing a set of images of patient’s organ of a subject. A cardiac image capture device may include any device for capturing set of images 1412 as described herein. For example, a cardiac image capture device may include an ICE catheter. In some cases, ICE images may be captured using a specialized catheter equipped with an ultrasound transducer that is inserted into the body and guided to the heart of subject 1420. In an embodiment, set of images 1412 may provide a detailed and real- time visualizations of patient’s organ. As used herein, “patient’s organ” is any organ that is part of a specific individual. Patient’s organ may include any organ in question belongs to an individual who is receiving medical treatment or undergoing a medical procedure, or organ belongs to other individuals. Set of images 1412 may also include internal structures, functions, and bold flow patterns of the heart of subject 1420. Other exemplary embodiments of set of 148 Attorney Docket No.1518-103PCT1 images 1412 may include, without limitation, X-ray images, magnetic resonance imaging (MRI) scans, computed tomography (CT) scans, ultrasound images, transesophageal echocardiogram images, transthoracic echocardiogram images, point-or-care ultrasound images, optical images, digital photographs, or any other form of visual data. Additionally, images within set of images 1412 may be related in terms of content, time of capture, sequence, or any other relevant parameters described herein. In a non-limiting example, each image of set of images 1412 may represent a particular view, angle, or perspective of an object, subject, or scene, and may be in two-dimensional (2D) or 3D format. Still referring to FIG.14, images of set of images 1412 may include, without limitation, any two-dimensional or three-dimensional images of any anatomy or anatomical structure, including without limitation images of any internal organ, tissue including without limitation muscular, connective tissue, epithelial tissue, and/or nervous tissue, bone, and/or any other element that may be imaged within a human and/or animal body. In a non-limiting example, patient’s organ 1416 may include chambers (e.g., four chambers including left and right atria and left and right ventricles), valves (i.e., the structures that regulate blood flow between chambers and vessels, including mitral, tricuspid, aortic, and pulmonary valves), vessels (e.g., aorta, pulmonary arteries and veins, and coronary arteries), conduction system (i.e., a network of specialized cells that control the heart’s electrical activity and rhythm), muscular and connective tissues (e.g., heart’s muscular walls, septa, any other connective tissues that provide structural integrity and enable contraction), LAA and other appendages, pathological features (e.g., any abnormalities, defects, and/or the like), among others. Still referring to FIG.14, as used in this disclosure, a “subject” is an individual organism. In an embodiment, subject 1420 may include a human, on whom or on which the procedure, study, or otherwise experiment, such as without limitation, AF ablation described herein, is being conducted. In some cases, subject 1420 may include a provider of set of images 1412 described herein. In other cases, subject 1420 may include a recipient or a participant in a clinical trial or research study. In a non-limiting example, subject 1420 may include a human patient with AF who is undergoing a procedure, an individual undergoing cardiac screening, a participant in a clinical trial, patient with congenital heart disease, heart transplant candidate, patient receiving follow-up care after cardiac surgery, healthy volunteer, patient with heart 149 Attorney Docket No.1518-103PCT1 failure, or the like. Additionally, or alternatively, subject 1420 may include an animal models (i.e., animal used to model AF such as a laboratory rat). Still referring to FIG.14, in an embodiment, each ICE image of set of ICE images may include a particular view of subject’s 1420 heart’s chambers, valves, vessel, and/or the like. In a non-limiting example, set of images 1412 may include multiple views e.g., different angles and perspectives of subject’s 1420 heart. In another embodiment, set of images 1412 may be arranged in a temporal sequence. In a non-limiting example, set of images 1412 may include a series of images captured over time, allowing for an observation of dynamic cardiac functions such as beating, blood flow, and/or the like. In some cases, each ICE image of set of images 1412 may include a corresponding timestamp, wherein the timestamp may include an indicator showing a date and time of when the corresponding ICE image was taken. Additionally, or alternatively, and still referring to FIG.14, various imaging techniques or settings may be applied to set of images 1412 that provide specific insights into patient’s organ 1416. In some cases, patient’s organ 1416 may include a plurality of physical characteristics, spatial relationships, and function aspects of the heart’s component; for instance, and without limitation, receiving set of images 1412 may include applying a doppler imaging technique, wherein the “doppler imaging technique” is a specialized ultrasound technique used to assess the movement of blood within the body, particularly within the heart and blood vessels. Processor 1404 may configure a transducer to send high-frequency sound waves into the subject’s 1420 body, wherein the sound waves may bounce off moving blood cells and other structures. Reflected waves may then be picked up by the transducer and frequency of the reflected waves changes (Doppler shift) depending on the speed and direction of blood flow may be analyzed to determine one or more blood flow characteristics. In some cases, one or more ICE images within set of images 1412 may include visual representations translated based on one or more blood flow characteristics. Such visual representations may be further color-coded, showing the speed and direction of blood flow. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will aware other exemplary modalities of ICE imaging such as, without limitation, computed tomography (CT) scans, magnetic resonance imaging MRI, positron emission tomography (PET) scan, angiography, electrocardiogram (ECG or EKG), single-photon emission computed tomography (SPECT), optical coherence tomography (OCT), thermography, tactile imaging, and/or the like. 150 Attorney Docket No.1518-103PCT1 With continued reference to FIG.14, in one or more embodiments, receiving set of images 1412 of patient’s organ 1416 may include receiving a patient profile pertaining to subject 1420. As used in this disclosure, a “patient profile” is a comprehensive collection of information related to an individual patient. In some cases, patient profile may include a variety of different types of data that, when combined, provide a detailed picture of a patient's overall health. In an embodiment, patient profile may include demographic data of patient, for example, and without limitation, patient profile may include basic information about the patient such as name, age, gender, ethnicity, socioeconomic status, and/or the like. In another embodiment, each patient profile may also include a patient’s medical history, for example, and without limitation, patient profile may include a detailed record of the patient's past health conditions, medical procedures, hospitalizations, and illnesses such as surgeries, treatments, medications, and/or the like. In another embodiment, each patient profile may include lifestyle Information of patient, for example, and without limitation, patient profile may include details about the patient's diet, exercise habits, smoking and alcohol consumption, and other behaviors that could impact health. In a further embodiment, patient profile may include patient’s family history, for example, and without limitation, patient profile may include a record of hereditary diseases. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various type of data within patient profiles apparatus 1400 may receive and process in consistent with this disclosure. In a non-limiting example, and still referring to FIG.14, patient profile may include one or more ICE images or set of images 1412. Receiving set of images 1412 may include extracting set of images 1412 from patient profile (subsequent to patient identity verification and obtaining consent from subject 1420). In some cases, patient profile of subject 1420 may be obtained through hospital information system (HIS) or any other data acquisition platform to securely access patient’s electronic medical record (EMR) or other relevant databases. Set of images 1412 may be directly or indirectly downloaded or exported. In some cases, each ICE image of set of images 1412 may be in a usable and/or computer-readable format such as, without limitation, DICOM format, and necessary metadata (e.g., patient information described above) may be included. Further, receiving set of images 1412 may include recording the access and extraction of set of images 1412; for instance, and without limitation, this process may be 151 Attorney Docket No.1518-103PCT1 documented, by processor 1404, in the patient’s/subject’s 1420 medical record, databases, or other appropriate logs. Further, and still referring to FIG.14, in other embodiments, patient profile may include electrocardiogram (ECG) data. As used herein, “ECG data” is data related to an electrocardiogram of a patient that corresponds to the patient profile. An “electrocardiogram,” as described herein, is a medical test that records the electrical activity of subject’s heart over a period of time. In an embodiment, ECG data may include one or more recordings captured by a plurality of electrodes placed on patient’s skin. In one or more embodiments, ECG data may include information regarding a P wave, T wave, QRS complex, PR interval, ST segment, and/or the like. Processor 1404 may associate set of images 1412 with ECG data, or in other cases, receiving set of images 1412 may include receiving ECG data pertaining to subject 1420 associated with set of images 1412. Such ECG data may be collected simultaneously during ICE imaging. In some cases, set of images 1412 may be linked with ECG data by one or more unique identifiers, such as without limitations, timestamps or other metadata described herein. In a non- limiting example, ECG data may be used to identify specific cardiac events or phases of the cardiac cycle, and the corresponding ICE images may be analyzed to see how heart’s structure changes during those times. With continued reference to FIG.14, in other embodiments, receiving set of images 1412 may include receiving set of ICE images from Data store 1424. In some cases, Data store 1424 may be implemented, without limitation, as a relational database, a key-value retrieval database such as a NOSQL database, or any other format or structure for use as a database that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Data store 1424 may alternatively or additionally be implemented using a distributed data storage protocol and/or data structure, such as a distributed hash table or the like. Data store 1424 may include a plurality of data entries and/or records as described above. Data entries in Data store 1424 database may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in Data store 1424 or another relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a database may store, retrieve, organize, and/or reflect data and/or records as used herein, as well as categories and/or populations of data consistently with this disclosure. 152 Attorney Docket No.1518-103PCT1 In a further embodiment, and still referring to FIG.14, receiving set of images 1412 may involve one or more image preprocessing steps. In some cases, processor 1404 may be configured to calibrate one or more ICE images of set of images 1412 by correct for distortions and ensure accurate spatial representation of patient’s organ 1416 pertaining to subject 1420. In a non-limiting example, processor 1404 may select one or more reference objects within ICE image that needs calibration to correct spatial distortions. In some cases, processor 1404 may be configured to place a phantom with pre-determined dimensions in such ICE image and adjust ICE image until the phantom’s dimensions are accurately represented. In another non-limiting example, one or more ICE images’ brightness and contrast may be adjusted, by processor 1404 to ensure that echogenicity (reflectivity) of the tissues is accurately represented. One or more tissues with known echogenicity may be selected by processor 1404 as reference tissues to adjust corresponding portions of the one or more ICE images. In other cases, standardized correction curves may be applied in or der to correct the echogenicity of ICE images. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, may be aware of various calibration techniques, such as, without limitation, temporal calibration, geometric calibration, among others that can be used by processor 1404 to preprocess set of images 1412. Additionally, or alternatively, and still referring to FIG.14, receiving set of images 1412 may include performing image segmentation on or more ICE images of set of images 1412. In some cases, image segmentation may include separating specific structures or regions of interest (ROI) from the background or other structures in a given ICE image. In a non-limiting example, processor 1404 may be configured to use edge detection algorithms to outline the heart chambers, separating them from surrounding tissues. One or more filters may be applied to highlight the boundaries between different types of tissues during the segmentation. In another non-limiting examples, valves and vessels may also be segmented by applying thresholding techniques. Processor 1404 may be configured to set an intensity threshold based on the known echogenicity of blood and vessel walls and select pixels or regions having intensity below or above the intensity threshold from the given ICE image. In some cases, one or more machine learning models may be used to perform image segmentations, for example, and without limitation, a U-net (i.e., a convolution neural network containing a contracting path as an encoder and an expansive path as a decoder, wherein the encoder and the decoder forms a U-shaped structure). 153 Attorney Docket No.1518-103PCT1 Still referring to FIG.14, in some embodiments, a process described herein may be performed in real time. For example, a processor may continuously receive images, and may perform one or more calculations as a function of those images while receiving additional images. In some embodiments, a processor may continuously receive images, and may determine a first set of images and a second set of images as a function of such continuous image stream. Processor may continuously receive images and update 3D models as a function of those images; for example, this may occur during a medical procedure. For example, images received first chronologically may be determined to be in first set of images, a calculation may be performed as a function of the first set of images, and while such calculations are ongoing, processor may receive additional images of continuous image stream, which may be determined to be in a second set of images, which may then be used to perform additional calculations. In some embodiments, a 3D model is generated and/or displayed to a user as images are captured and/or as calculations are performed. For example, a first set of images may be captured, a first 3D model may be generated as a function of such first set of images, and first 3D model may be displayed to a user as a second set of images is captured. In some embodiments, a first 3D model is generated, and a second set of images is received by a processor simultaneously. With continued reference to FIG.14, 3D model may include a digital representation of a patient’s organ, capturing its anatomy, geometry, and potentially functional properties. In some cases, 3D model of patient’s organ may include, for example, and without limitation, a “heart model,” which is a digital representation of a patient’s heart. In some embodiments, organ model may include a liver model, in some embodiments, organ model may include a kidney model, a lung model, a brain model, and/or the like. In some cases, patient may include a human or any individual organism, on whom or on which the procedure, study, or otherwise experiment, such as without limitation, atrial fibrillation (AF) ablation, is being conducted. In a non-limiting example, processor 1404 may receive a set of images from a human patient with AF who is undergoing a procedure, an individual undergoing cardiac screening, a participant in a clinical trial, patient with congenital heart disease, heart transplant candidate, liver disease, patient receiving follow-up care after cardiac surgery, healthy volunteer, patient with heart failure, or the like. Additionally, or alternatively, patient may include an animal models (i.e., animal used to model AF such as a laboratory rat). 154 Attorney Docket No.1518-103PCT1 With continued reference to FIG.14, processor may be configured to generate a 3D data structure representing patient’s organ 1416 as a function of set of images 1412. In a non- limiting example, 3D data structure may include a 3D voxel occupancy representation (VOR). As used in this disclosure, a "3D voxel occupancy representation" of a patient’s organ is a 3D digital representation of a spatial structure of the patient’s organ, wherein the representation is composed of a plurality of discrete volumetric elements known as voxels. A “voxel,” for the purpose of this disclosure, is a 3D equivalent of a pixel in 2D imaging. While a pixel represents a point in a 2D image and may include properties such as color and/or brightness, a voxel may represent a volume in a 3D space and may include additional properties such density/occupancy as described below. In an embodiment, each voxel of plurality of voxels within 3D VOR may represent a specific portion of patient’s organ 1416. In some cases, voxel may be a smallest distinguishable box-shaped part (i.e., 14px ^14px ^14px) of a three-dimensional image. In some cases, each voxel of plurality of voxels within VOR may be represented as a cube or rectangular prism (although other shapes may be used in specialized applications). Each voxel may include a size that determines a resolution of the 3D image or model. In an embodiment, smaller voxels may provide higher resolution; however, it may require more computational resources (e.g., RAM) for processor 1404 to process. In an embodiment, and still referring to FIG.14, each voxel of plurality of voxels within VOR may include one or more embedded values. As used herein, “embedded values” refer to specific numerical or categorical data associated with each voxel. In some cases, embedded values may represent various attributes or characteristics of the corresponding portion of patient’s organ 1416 that voxel represents. In a non-limiting example, embedded values may include density values, intensity values, texture information, or any other quantitative measures that provide insights into the underlying cardiac tissue. Such embedded values may be derived from set of ICE images or other imaging modalities used to generate data structure. In some cases, embedded values may be utilized, by processor 1404, to differentiate between different types of cardiac tissues, such as myocardial tissue, blood vessels, or chambers. Embedded values may also facilitate the visualization of dynamic cardiac functions, for example, and without limitation, blood flow or heart beating by encoding temporal information such as timestamps within plurality of voxels. 155 Attorney Docket No.1518-103PCT1 Still referring to FIG.14, in an embodiment, each voxel of plurality of voxels may include a presence indicator. As used in this disclosure, a “presence indicator” refers to a data element that indicates a presence or absence of cardiac tissue within that portion. In some cases, and without limitation, presence indicator may include an occupancy status as one of the embedded values described herein. Portion may include a specific location within 3D space where data structure is generated; for instance, and without limitation, a coordinate in 3D space represented in a tuple such as (x, y, z). In an embodiment, 3D VOR may provide a spatial framework that allows for the modeling and visualization of patient’s organ 1416 in 3D space. In some cases, 3D data structure may include a plurality of layers or slices (either horizontal [e.g., xy plane] or vertical [e.g., xz or yz plane depends on the view direction]), wherein each layer or slices of the plurality of layers or slices is corresponding to a different cross-sectional view of subject’s 1420 heart, and collectively forming a comprehensive 3D depiction of the cardiac structure. In a non-limiting example, 3D VOR having plurality of voxels with presence indicators may indicate whether each voxel in 3D space may be occupied by a part of subject’s 1420 heart. A binary value such as 0 or 14 may be configured as presence indicator to show either a pixel of 3D space is occupied (e.g., 14) or empty (e.g., 0). In should be noted that other values may be used as presence indicator such as a Boolean value e.g., TRUE or FALSE. In some cases, and still reference to FIG.14, one or more embedded values, such as, without limitations, occupancy, or density, may be derived from set of images 1412 described herein by processor 1404. In a non-limiting example, determining occupancy status of each voxel of plurality of voxels may include converting set of ICE images to a set of binary images and determining occupancy status of each voxel as a function of the structure of interest’s binary value. In some cases, occupancy status may include a value representing the likelihood of occupancy of the corresponding heart tissue. In another non-limiting example, density may be calculated, by processor 1404, for each voxel as a function of the echogenicity of one or more pixels on a given ICE image, wherein, the brightness of the given ICE image may be analyzed since different tissues reflect ultrasound waves differently. With continued reference to FIG.14, generating 3D data structure of patient’s organ 1416 may include generating a 3D array. In some cases, processor 1404 may divide 3D space into a grid of plurality of voxels, each with specific x, y, and z coordinates as embedded values. Each element of 3D array may correspond to a voxel. In some cases, 3D array may allow for 156 Attorney Docket No.1518-103PCT1 easy access and manipulation of plurality of voxels, enabling various analyses, visualizations, and transformations either described or not described herein. In a non-limiting example, embedded values may include a density of the tissue at a specific location of a patient’s body derived from one or more ICE images of set of images 1412. Additionally, or alternatively, and still referring to FIG.14, a 3D data structure of patient’s organ 1416 may include a 3D grid configured to map presence indicators and/or other embedded values described herein of plurality of voxels (e.g., tissue density, blood flow velocity, echogenicity or acoustic properties, and any other biophysical properties). As used in this disclosure, a “3D grid” is a 3D data structure that divides a given volume into a plurality of discrete units. Such a volume may include, for example, a volume of a heart. Such discrete units may be referred to as cells (i.e., volume elements). In an embodiment, each cell within 3D grid may be associated with a distinct voxel. Mapping presence indicators or other embedded values may include assigning each presence indicator or embedded value to each points within 3D grid such as corners of each corresponding cell. Such values may be derived from set of images 1412 as described above. In yet another embodiment, and still referring to FIG.14, cells may be continuous, meaning that one or more cells may represent one or more continuous regions of space rather than discreate, separate units. In a non-limiting example, instead of being uniform, mapped presence indicator and/or other embedded values may vary continuously across different cells or cell’s volume. In such embodiment, processor 1404 may use interpolation to estimate other (unknown) embedded values within a range based on existing values such as known embedded values at specific points, thereby allowing for smooth transitions between cells. Exemplary interpolation methods may include, without limitation, linear interpolation, cubic interpolation, and/or the like. For example, and without limitation, if the corners of a cell have known values interpolation can be used to estimate the values at any point within the cell based on those corner values. In a non-limiting example, and still referring to FIG.14, 3D data structure of patient’s organ 1416 may include a 3D grid having a plurality of cells e.g., voxels, wherein each cell may contain a continuous range of values representing tissue density, blood flow velocity, or other properties (i.e., embedded values). Processor 1404 may be configured to apply trilinear or tricubic interpolation to estimate tissue density within each cell based on presence indicator or 157 Attorney Docket No.1518-103PCT1 other known values at the cell’s boundaries, since tissue densities change gradually; Such 3D grid may provide a smooth, continuous representation of heat’s internal structures, allowing for more nuanced analysis and visualization as described below. In a further embodiment, 3D grid with continuous cells may be additionally used in fluid dynamics simulations. With continued reference to FIG.14, in some cases, presence indicators and/or other embedded values may be mapped to 3D grid as a function of array masking, wherein specific array or grid may be selected to modify based on one or more pre-defined criteria. In a non- limiting example, processor 1404 may generate a mask e.g., a binary array that defines which voxels or cells are affected. Mask may be used to select or modify specific voxels or cells based on certain attributes; for instance, and without limitation, processor 1404 may use mask to isolate the LA within the heart focusing the analysis on that specific region. Such mask may include a criteria defined by specific density thresholds that distinguish the LA’s tissue (i.e., voxels representing LA in 3D grid) from surrounding structures (i.e., neighboring voxels). In some cases, such mask may further include a binary mask, wherein each voxel in the 3D grid may be assigned a first presence indicator such as 14 if the voxel meets the criteria for the LA and a second presence indicator such as 0 if it does not. In some embodiments, mask may be directly applied to 3D grid, selecting, or modifying voxels or cells, thereby enabling processor 1404 to highlight, exclude, or otherwise manipulate specific parts of patient’s organ 1416 within 3D grid. Processor 1404 may then perform an element-wise multiplication between 3D grid and the mask. Continuing from the previous non-limiting example, voxels corresponding to the LA (wherein the mask value is 14) may retain their original values, while other voxels (where the mask value is 0) may be set to 0 or other specific value (i.e., excluded or masked out). With continued reference to FIG.14, in some embodiments, 3D grid may include one or more spatial features extracted from set of images 1412 of patient’s organ 1416. As used in this disclosure, a “spatial feature” is a specific characteristic or attribute related to the spatial arrangement, shape, size, texture, or orientation of one or more structures within a 3D space. In some cases, spatial features may include one or more embedded values described herein and their combinations thereof. In a non-limiting example, spatial feature may be represented numerically as a vector, a metric or other mathematical constructs that capture specific spatial characteristics. In some cases, spatial features may also be visualized as contours, surfaces, or other geometric representations. In an embodiment, spatial features may be extracted using edge 158 Attorney Docket No.1518-103PCT1 detection, texture analysis, or other image processing techniques (e.g., cleaning and enhancing images, image segmentation, and/or the like). In another embodiment, one or more machine learning models, such as convolutional neural networks (CNNs) as described in further detail below, may be used to extract complex spatial features. Still referring to FIG.14, as used in this disclosure, a “vector” is a data structure that represents one or more a quantitative values, measures of one or more spatial features, or both. A vector may be represented as an n-tuple of values, where n is one or more values, as described in further detail below; a vector may alternatively or additionally be represented as an element of a vector space, defined as a set of mathematical objects that can be added together under an operation of addition following properties of associativity, commutativity, existence of an identity element, and existence of an inverse element for each vector, and can be multiplied by scalar values under an operation of scalar multiplication compatible with field multiplication, and that has an identity element is distributive with respect to vector addition, and is distributive with respect to field addition. Each value of n-tuple of values may represent a measurement or other quantitative value associated with a given category of data, or attribute, examples of which are provided in further detail below; a vector may be represented, without limitation, in n- dimensional space using an axis per category of value represented in n-tuple of values, such that a vector has a geometric direction characterizing the relative quantities of attributes in the n-tuple as compared to each other. Two vectors may be considered equivalent where their directions, and/or the relative quantities of values within each vector as compared to each other, are the same; thus, as a non-limiting example, a vector represented as [5, 140, 145] may be treated as equivalent, for purposes of this disclosure, as a vector represented as [14, 2, 3]. Vectors may be more similar where their directions are more similar, and more different where their directions are more divergent, for instance as measured using cosine similarity as computed using a dot product of two vectors; however, vector similarity may alternatively or additionally be determined using averages of similarities between like attributes, or any other measure of similarity suitable for any n-tuple of values, or aggregation of numerical similarity measures for the purposes of loss functions as described in further detail below. Any vectors as described herein may be scaled, such that each vector represents each attribute along an equivalent scale of values. Each vector may be “normalized,” or divided by a “length” attribute, such as a length attribute l as derived using a Pythagorean norm: ^^ ൌ _^∑^{^} ^_ୀ^ ^^_^ ^ଶ , where a_i is attribute number i of

159 Attorney Docket No.1518-103PCT1 the vector. Scaling and/or normalization may function to make vector comparison independent of absolute quantities of attributes, while preserving any dependency on similarity of attributes. Still referring to FIG.14, in a non-limiting example, one or more spatial features may include one or more shape features (i.e., characteristics related to the shape of specific cardiac structures), such as curvature, surface area, volume, and/or the like. In another non- limiting example, one or more spatial features may include one or more texture features (i.e., characteristics related to the texture or pattern within cardiac tissues, as seen set of images 1412), such as gray-level co-occurrence matrix (GLCM) features representing the texture of heart muscle tissue. In another non-limiting example, one or more spatial features may include one or more orientation features (i.e., characteristics related to the orientation or alignment of cardiac structures), such as the angle or alignment of the septum within the heart. In a further non- limiting example, one or more spatial features may include one or more edge and boundary features (i.e., Characteristics related to the edges or boundaries between different cardiac structures or tissues), such as edge detection features highlighting the boundary between the myocardium and the cardiac chambers. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various spatial features extracted from set of images 1412 in consistent with this disclosure. With continued reference to FIG.14, in some embodiments, apparatus 1400 may include a computer vision model 1428 configured to generate 3D data structure of patient’s organ 1416 by implementing image segmentation methods as described further below. A “computer vision model,” for the purpose of this disclosure, is a computation model designed to interpret and make determinations based on visual data. In an embodiment, computer vision model 1428 may process set of images 1412, to make a determination about a scene, space, and/or object in patient’s organ 1416. In a non-limiting example, computer vision model 1428 may be used for registration of plurality of voxels within a 3D space. In some cases, registration may include image processing described herein, such as without limitation object recognition, feature detection, edge/corner detection, and the like. Non-limiting example of feature detection may include scale invariant feature transform (SIFT), Canny edge detection, Shi Tomasi corner detection, and the like. In some cases, registration may include one or more transformations to orient an ICE image relative to a 3D coordinate system; exemplary transformations include without limitation homography transforms and affine transforms. In an embodiment, registration 160 Attorney Docket No.1518-103PCT1 of ICE image to a coordinate system may be verified and/or corrected using object identification and/or computer vision, as described above. For instance, and without limitation, an initial registration to two dimensions, represented for instance as registration to the x and y coordinates, may be performed using a two-dimensional projection of points in three dimensions onto the ICE image; however, a third dimension of registration, representing depth and/or a z axis, may be detected by utilizing depth-sensing techniques such as Doppler imaging. Alternatively, the third dimension may be inferred from the known geometry and orientation of the imaging device (e.g., ICE catheter), or through the application of one or more machine learning models trained to interpret depth from the two-dimensional projection. With continued reference to FIG.14, processor 1404 may use a machine learning module 1432 to implement one or more algorithms or generate one or more machine learning models, such as a patient’s organ modeling model to generate data structure of patient’s organ 1416. However, the machine learning module is exemplary and may not be necessary to generate one or more machine learning models and perform any machine learning described herein. In one or more embodiments, one or more machine-learning models may be generated using training data. Training data may include inputs and corresponding predetermined outputs so that a machine-learning model may use correlations between the provided exemplary inputs and outputs to develop an algorithm and/or relationship that then allows machine-learning model to determine its own outputs for inputs. Training data may contain correlations that a machine- learning process may use to model relationships between two or more categories of data elements. Exemplary inputs and outputs may come from a database, such as any database described in this disclosure, or be provided by a user. In other embodiments, a machine-learning module may obtain a training set by querying a communicatively connected database that includes past inputs and outputs. Training data may include inputs from various types of databases, resources, and/or user inputs and outputs correlated to each of those inputs so that a machine-learning model may determine an output. Correlations may indicate causative and/or predictive links between data, which may be modeled as relationships, such as mathematical relationships, by machine-learning models, as described in further detail below. In one or more embodiments, training data may be formatted and/or organized by categories of data elements by, for example, associating data elements with one or more descriptors corresponding to categories of data elements. As a non-limiting example, training data may include data entered in 161 Attorney Docket No.1518-103PCT1 standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data may be linked to descriptors of categories by tags, tokens, or other data elements. In a further embodiment, training data may include previous outputs such that one or more machine learning models iteratively produces outputs. Still referring to FIG.14, machine learning module 1432 may be used to generate patient’s organ modeling model and/or any other machine learning models, such as, shape identification model as described in further detail below, using training data. Patient’s organ modeling model may be trained by correlated inputs and outputs of training data. Training data may be data sets that have already been converted from raw data whether manually, by machine, or any other method. In an embodiment, generating data structure of patient’s organ 1416 includes receiving patient’s organ training data, wherein the patient’s organ training data may include a plurality of image sets as input and a plurality of computed tomography (CT) based patient’s organ models as output, and wherein each image set of plurality of image sets may include any images described in this disclosure. In some cases, patient’s organ training data may be received from Data store 1424 or other databases. In other cases, patient’s organ training data may be collected by a data acquisition unit from external sources such as one or more medical equipment’s e.g., imaging devices or diagnostic tools, wherein the data acquisition may be configured as an intermediary between the data source and machine learning module 1432. Still referring to FIG.14, a computed tomography (CT) scan may be used to generate a patient’s organ model. As used herein, a “computed tomography based patient’s organ model” is a 3D representation of the heart and surrounding structures that is created using data from CT scans. Computed Tomography is a medical imaging technique that uses X-rays to capture cross- sectional images (slices) of the body. By taking a plurality of slices, a CT scan creates a detailed 3D representation of the internal structure. In an embodiment, CT-based patient’s organ model may include 3D representations of the heart including chambers, valves, blood vessels, and surrounding tissues. In some cases, CT-based patient’s organ model may be interactive; for instance, medical professionals may rotate, zoom, and/or explore CT-based patient’s organ model from various angles. In some cases, plurality of CT-based patient’s organ models may be generated prior to the training of the patient’s organ modeling model. Plurality of CT-based patient’s organ models may be generated using existing techniques in the field as described 162 Attorney Docket No.1518-103PCT1 above such as, without limitation, FAM, cardiac CT merging, among others. In a non-limiting example, plurality of CT-based patient’s organ models may provide ground through or references models against patient’s organ modeling model that is being trained. In a non-limiting example, generating data structure of patient’s organ 1416 further includes training patient’s organ modeling model using patient’s organ training data described herein. Patient’s organ modeling model trained using patient’s organ training data may be able to interpret ICE images by learning relationships between ICE images and corresponding CT-based patient’s organ models. Processor 1404 is further configured to generate data structure of patient’s organ 1416 as a function of set of images 1412 using trained patient’s organ modeling model. In some cases, data structure e.g., 3D model 1456 as described below may be interpreted, visualized, and analyzed by processor 1404 in similar manner to CT-based patient’s organ models, wherein both are 3D structures that correspond to ICE images. With continued reference to FIG.14, in an embodiment, patient’s organ modeling model includes a deep neural network (DNN). As used in this disclosure, a “deep neural network” is defined as a neural network with two or more hidden layers. Neural network is described in further detail with reference to FIGS.4-5. In a non-limiting example, patient’s organ modeling model may include a convolutional neural network (CNN). Generating 3D data structure of patient’s organ 1416 may include training CNN using patient’s organ training data and generating 3D data structure as a function of set of images 1412 using trained CNN. A “convolutional neural network,” for the purpose of this disclosure, is a neural network in which at least one hidden layer is a convolutional layer that convolves inputs to that layer with a subset of inputs known as a “kernel,” along with one or more additional layers such as pooling layers, fully connected layers, and the like. In some cases, CNN may include, without limitation, a deep neural network (DNN) extension. Mathematical (or convolution) operations performed in the convolutional layer may include convolution of two or more functions, where the kernel may be applied to input data e.g., set of images 1412 through a sliding window approach. In some cases, convolution operations may enable processor 1404 to detect local/global patterns, edges, textures, and any other spatial features described herein within each ICE image of set of images 1412. Spatial features may be passed through one or more activation functions, such as without limitation, Rectified Linear Unit (ReLU), to introduce non-linearities into the processing step of generating 3D data structure of patient’s organ 1416. Additionally, or alternatively, CNN may 163 Attorney Docket No.1518-103PCT1 also include one or more pooling layers, wherein each pooling layer is configured to reduce the dimensionality of input data while preserving essential features within the input data. In a non- limiting example, CNN may include one or more pooling layer configured to reduce the spatial dimensions of spatial feature maps by applying downsampling, such as max-pooling or average pooling, to small, non-overlapping regions of one or more spatial features. Still referring to FIG.14, CNN may further include one or more fully connected layers configured to combine spatial features extracted by the convolutional and pooling layers as described above. In some cases, one or more fully connected layers may allow for higher-level pattern recognition. In a non-limiting example, one or more fully connected layers may connect every neuron (i.e., node) in its input to every neuron in its output, functioning as a traditional feedforward neural network layer. In some cases, one or more fully connected layers may be used at the end of CNN to perform high-level reasoning and produce the final output such as, without limitation, a 3D data structure of patient’s organ 1416. Further, each fully connected layer may be followed by one or more dropout layers configured to prevent overfitting, and one or more normalization layers to stabilize the learning process described herein. With continued reference to FIG.14, CNN may further include a 3D CNN, wherein the 3D CNN, unlike standard 2D CNN, may include utilization of one or more 3D convolutions which allow them to directly process 3D data, thereby enabling processor 1404 to generate 3D structures such as 3D data structure of patient’s organ 1416 using the 3D CNN. In a non-limiting example, 3D CNN may include one or more 3D filters (i.e., kernels) that move through the set of images 1412 in three dimensions and capturing spatial relationships in x, y, and z axis. Similar to 3D convolutions, 3D CNN may further include one or more 3D pooling layers that may be used to reduce the dimensionality of ICE images while preserving spatial features as described above. Additionally, or alternatively, an encoder-decoder structure may be implemented (extended to 3D), by processor 1404, in 3D CNN, wherein the encoder-decoder structure includes an encoding path that captures the context and a decoding path that enables precise localization in a same manner as U-net as described above. Such encoder-decoder structures may also include a plurality of skip connections, allowing 3D CNN to use information from multiple resolutions to improve the process of generating 3D data structure of patient’s organ 1416. With continued reference to FIG.14, in an embodiment, training the patient’s organ modeling model (i.e., CNN) may include selecting a suitable loss function to guide the training 164 Attorney Docket No.1518-103PCT1 process. In a non-limiting example, a loss function that measures the difference between the predicted 3D VORs and the ground truth 3D structure e.g., CT-based patient’s organ models may be used, such as, without limitation, mean squared error (MSE) or a custom loss function may be designed for one or more embodiments described herein. Additionally, or alternatively, optimization algorithms, such as stochastic gradient descent (SGD), may then be used to adjust the patient’s organ modeling model’s parameters to minimize such loss. In a further non-limiting embodiment, instead of directly predicting 3D data structure, patient’s organ modeling model may be trained as a regression model to predict presence indicators and/or other embedded values described herein for each voxel of plurality of voxels within a 3D grid. Additionally, CNN may be extended with additional deep learning techniques, such as recurrent neural networks (RNNs) or attention mechanism, to capture additional features and/or data relationships within input data. These extensions may further enhance the accuracy and robustness of the patient’s organ modeling. With continued reference to FIG.14, processor 1404 is configured to generate a set of shape parameters 1436 based on set of images 1412. As used in this disclosure, a “set of shape parameters” is a collection of numerical values or descriptors that quantitatively represent the geometric or morphological characteristics of a structure e.g., a heart. In a non-limiting example, set of shape parameters 1436 may include information and/or metadata calculated, determined, and/or extracted from set of ICE images, such as, dimensions, angles, curvatures, surface areas, texture, symmetry, and/or the like. In other embodiments, processor 1404 may be configured to parameterize features (e.g., edges, textures, contours, and any other characteristics that describe the shape patient’s organ 1416) extracted from set of images 1412 using CNN described herein. Such parameterization may involve processor 1404 to derive one or more shape parameters including one or more morphological descriptors that quantitatively describe patient’s organ 1416 based on extracted features. In some cases, processor 1404 may be configured to use principal component analysis (PCA) to reduce the dimensionality of set of shape parameters 1436, allowing processor 1404 to focusing on the most informative shape parameters of set of shape parameters 1436 in further processing steps described below. With continued reference to FIG.14, in a non-limiting example, set of shape parameters 1436 may be generated based on set of images 1412 using a machine learning model such as, without limitation, shape identification model 1440. Generating set of shape parameters 165 Attorney Docket No.1518-103PCT1 1436 may include receiving cardiac geometry training data 1448. Cardiac geometry training data 1448 may include a plurality of image sets as input correlated with a plurality of shape parameter sets as output. In some cases, cardiac geometry training data may be received from Data store 1424 described herein. For example, and without limitation, cardiac geometry training data 1448 may include a plurality of ICE images, correlated with shape parameter sets generated using CT scan data. In some embodiments, cardiac geometry training data 1448 may include data as to a position and/or orientation one or more ICE images were taken in within a heart. Additionally, cardiac geometry training data 1448 may include previous input image sets and their corresponding shape parameters output. Shape identification model 1440 may be iterative such that outputs may be used as future inputs of shape identification model 1440. This may allow shape identification model 1440 to evolve. Processor 1404 may be further configured to generate set of shape parameters 1436 as a function of set of images 1412 using trained shape identification model 1440. Shape identification model 1440 may include a neural network, such as a deep neural network and/or a convolutional neural network, as described herein. Still referring to FIG.14, generating set of shape parameters 1436 may include performing image processing/segmentation techniques, as described above, prior to implementation of shape identification model 1440 in order to optimize performance and runtime of processor 1404 and training of model. For example, image segmentation may include normalization and standardization methods performed by computer vision model 1428 to ensure that pixel values in images 1412 are normalized or standardized to a consistent scale thus aiding convergence during training of shape identification model 1440. Image segmentation may include data augmentation techniques such as rotation, scaling, flipping, and translation to artificially increase the size of the training dataset and improve model generalization. Image segmentation may include image enhancement preprocessing techniques like histogram equalization or contrast stretching to enhance relevant features in the images. Image segmentation may include texture and shape descriptors to extract features beyond pixel values, such as texture and shape descriptors, to capture additional information about cardiac structures. Image segmentation may include architecture selection methods, as in experiments with different architectures, such as U-Net, DeepLab, or custom architectures, depending on the complexity and characteristics of the cardiac images. Image segmentation may include grid Search or random Search processing methods to systematically explore hyperparameter combinations to 166 Attorney Docket No.1518-103PCT1 find the optimal configuration for a 3D model. As previously disclosed, image segmentation may include separating specific structures or regions of interest 1444 (ROI) from the background or other structures in a given ICE image, wherein a collection of ROIs 1444 may be also incorporated by the shape parameter training data/ cardiac geometry training data 1448. With continued reference to FIG.14, processor 1404 may use a statistical shape model (SSM) to generate and/or iteratively refine a 3D model 1456 based on a set of shape parameters. As used herein, a “heart model” is a 3D representation of patient’s organ. In some cases, 3D model 1456 may be generated through a direct 3D reconstruction from a series of (2D) ICE images. In a non-limiting example, set of images 1412 may include a plurality of ICE images captured from different angles and positions within the heart. Processor 1404 may be configured to apply one or more 3D reconstruction algorithms, such as without limitation, marching cubes, contour detection and segmentation, active contour models, and/or the like to create a coherent 3D representation e.g., 3D model 1456 of patient’s organ 1416. In some cases, such direct 3D reconstruction may leverage the inherent spatial information within set of images 1412, providing a direct and intuitive way to model the 3D model 1456 of the heart's structure. In a further embodiment, generic 3D modeling techniques may be applied to create the initial 3D model. In some cases, generic 3D modeling techniques may include surface modeling, solid modeling, or parametric modeling, among others. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various 3D reconstruction algorithms that may be used by processor 1404 to generate 3D model 1456 of patient’s organ 1416. Still referring to FIG.14, in some embodiments, a statistical shape model (SSM) may be used to generate 3D model 1456. As used herein, a “statistical shape model” is a data structure including a mathematical model of a heart shape, generated from a plurality of training data heart shapes. A SSM may take into account variation in the heart shape according to one or more characteristics of a subject. In some cases, SSM may be constructed by analyzing one or more datasets of shapes and identifying, for example, mean shapes and main modes of variation within the one or more datasets. In a non-limiting example, SSM may start with calculation of at least one mean shape, which represents an average geometry of all the heart shapes in a given dataset, wherein the at least one mean shape may serve as a central reference point for processor 1404 to understand different variations. In some cases, dataset may include, without limitation, patient’s organ training data, cardiac geometry training data 1448, CT scan data, and/or any 167 Attorney Docket No.1518-103PCT1 datasets within ICE image databases described herein. In some embodiments, historical CT scan data, such as CT scan data obtained from a database of historical subject data, may be used. Still referring to FIG.14, SSM may identify one or more principal modes of variation within a dataset. As used herein, “principal modes of variation,” are main patterns or directions along which data points vary within dataset. In a non-limiting example, identifying principal modes of variation may include applying principal component analysis (PCA) to given dataset. An SSM may be used to create, for example, an average heart shape; such average shape may include an average shape of a heart across an entire data set of scanned hearts. Changes in one or more parameters of SSM may allow 3D model 1456 to be determined according to changes from an average along a principal mode of variation. In some embodiments, such parameters may influence which subpopulations of training shapes are considered when determining 3D model 1456 and/or a relative influence of such subpopulations of training shapes in determining 3D model 1456. For example, parameters may be adjusted such that an average heart of a 35 year old subject is determined. In another example, parameters may be adjusted such that an average heart of a male subject may be determined. In some embodiments, weights may be applied to training data shapes and/or categories of training data shapes in order to generate an average shape of a subject with particular characteristics. Non-limiting examples of subject characteristics which may be customized to a subject when determining a SSM include age, gender, height, and weight. In some embodiments, one or more statistical constraints (e.g., mean, variance, correlation, boundary, proportion constraint and/or the like) may be introduced into SSM 1452 based on the distribution of shape parameters within plurality of shape parameter sets. With continued reference to FIG.14, in some cases, once modes of variation are extracted, processor 1404 may be configured to create a shape representation for any given heart shape within the studied class. In a non-limiting example, 3D model 1456 having a shape ^^ may _{be mathematically represented as ^^ ൌ ^^} ^̅ _{^ ∑} ^ெ ^_{ୀ^ ^^^ ൈ ^^ ^, wherein ^^} ^̅ _{denotes the mean shape} derived from the set of example

of modes of variation considered, ^^_^ are the coefficients or weights for each mode, and ^^_^ are the modes of variation (eigenvectors corresponding to the ^^th principal component). In some cases, coefficients ^^_^ may dictate a degree to which each mode of variation is present in shape ^^. In some cases, coefficients ^^_^ may vary from positive to negative (or negative to positive) based on the deformation of the 3D 168 Attorney Docket No.1518-103PCT1 model 1456 in directions described by each mode of variation. In some cases, 3D model 1456 may include mean shape as described herein. In some cases, 3D model 1456 may include a predictive heart shape that may not have been explicitly seen in the set of example shapes or patient’s heart observations. In some cases, 3D model 1456 may be in 3D VOR as described above. Still referring to FIG.14, generating the 3D model 1456 may include transforming 3D model 1456 to a second heart model as a function of a plurality of mode changers within SSM 1452, wherein each mode changer of the plurality of mode changers is associated with a model feature of heart model. As used in this disclosure, a “mode changer” is an algorithmic component derived from PCA configured to encapsulate a specific mode of variation. A mode changer may represent a distinct way in which the shape of 3D model 1456 may deviate from the mean shape. A “model feature,” for the purpose of this disclosure, is a distinct, recognizable and quantifiable attribute or characteristic of the heart model. For example, and without limitation, model feature may include an anatomical feature such as the size and curvature of the ventricles, the thickness of the heart wall, the positioning of heart valves or the like. In some cases, model feature may correspond to at least one shape parameter as described herein. In a non-limiting example, a mode changer may be associated with the size variation of the left ventricle identified within heart model. Such mode changer may be adjusted to modify the volume of the left ventricle, resulting in a second heart model that mimics potential biological variations or specific patient conditions that is different from original heart model. In some cases, multiple mode changers of SSM 1452 may be adjusted simultaneously. For example, and without limitation, the rigid registration might involve translations and rotations to superimpose the shapes; affine registration could incorporate scaling, shearing, and other linear transformations; while non-rigid methods might employ B-splines, thin-plate splines, or diffeomorphic transformations to flexibly map one shape onto another. In some cases, an averaged position of each corresponding point (or voxel) across all example shapes may be calculated using formula ^̅^ ^{^} ^ ൌ _ே ∑^ே ^_ୀ^ ^^_^^ , where ^̅^_^ is the mean position of the ^^th point (or voxel), ^^_^^ is the position of

in the ^^th example shape, and N is the total number of example

in the labeled set. In some cases, principle component analysis (PCA) may be applied to the aligned shapes to extract at least a primary mode of variation. As described herein, a “primary mode of variation” is a mode of variation that has the most significant variability. As used herein, a “mode of variation” is a specific pattern or 169 Attorney Docket No.1518-103PCT1 direction of a shape change. In some cases, such significancy may be indicated by the first principal component in PCA. In some cases, a plurality of modes of variation may be extracted, wherein each mode (or principal component) may represent a specific way the shape of heart may be deformed from the mean shape, determined by one or more eigenvectors of the covariance matrix of the aligned shapes. In a non-limiting example, eigenvector with the highest eigenvalue may represent primary mode of variation which captures the largest amount of shape variability within example shapes, while subsequent modes (eigenvectors) capture decreasing amounts of variability. In a non-limiting example, SSM 1452 described herein may be consistent with any SSM disclosed in this disclosure. In some embodiments, 3D model 1456 may be generated in any way similar to that of heart model as disclosed in this disclosure. In some embodiments, map 1464 may be generated as disclosed in this disclosure. In some embodiments, images may include any query images as disclosed this disclosure. Still referring to FIG.14, in some embodiments, SSM may be used to refine a mesh produced using a 3D model estimate. For example, a machine learning model such as a deep neural network may be used to produce a voxel grid estimate of patient’s organ based on ICE frames of the patient’s organ, and SSM may be used to refine this estimate to produce a 3D mesh of patient’s organ. In some embodiments, this may include deforming a template of patient’s organ to arrive at a most probable shape that matches a DNN estimate. Additionally, or alternatively, a machine learning model may be trained to directly generate parameters of a statistical shape model. Still referring to FIG.14, additionally, processor 1404 may use user feedback to train the machine-learning models described above. For example, patient’s organ modeling model and/or shape identification model 1440 may be trained using past inputs and outputs of patient’s organ modeling model and/or shape identification model 1440. In some embodiments, if user feedback indicates that a subsequent 3D model outputted by SSM 1452 was “bad,” then that output and the corresponding input e.g., set of ICE images, corresponding CT-based patient’s organ model may be removed from training data used to train patient’s organ modeling model and/or shape identification model 1440, and/or may be replaced with a value entered by, e.g., another user that represents an ideal 3D model of the heart given the input the machine learning models originally received, permitting use in retraining, and adding to training data as described above; in either case, machine learning models described herein may be retrained with modified 170 Attorney Docket No.1518-103PCT1 training data. In some embodiments, training data such as patient’s organ training data and/or cardiac geometry training data 1448 may include user feedback. Further, apparatus 1400 may be configured to validate one or more machine learning models described herein against real-world data, identifying areas where machine learning models may be underperforming or misaligned with clinical needs. Such feedback may also be used to guide model training, ensuring that machine learning models are not only accurate but also clinically meaningful and aligned with healthcare or medical professional’s needs and priorities. Stil referring to FIG.14, generating 3D model 1456 includes determining a level of uncertainty 1460 at least at one location of a plurality of locations of the 3D model 1456 based on the set of shape parameters 1436. A location may refer to each voxel of plurality of voxels, cells, geometric marker, and all other identifying markers/data points of a model as described throughout this disclosure. A plurality of locations may refer to the surface of heart model, such as a set of pixels or a region on a model. “Uncertainty,” as used herein, refers to the lack of confidence or precision in a model's predictions. In some cases, the level of uncertainty 1460 may be derived from variability within the distribution of shape parameters, image quality assessment, measurement errors and/or the like. In a non-limiting example, greater changes in heart geometry (indicated by the plurality of shape parameters) may correspond to a greater level of uncertainty at that location. This may be used to inform clinical decisions, for example, areas of high uncertainty may be avoided when planning a pathway for surgical intervention or additional imaging may be requested to reduce uncertainty in critical areas. Still referring to FIG.14, levels of uncertainty may refer to categories of uncertainty such as epistemic uncertainty, aleatoric uncertainty, model parameter uncertainty, pixel-wise uncertainty, boundary uncertainty, uncertainty in time series data, predictive uncertainty, systematic uncertainty, model output uncertainty, and the like. Epistemic uncertainty arises from a lack of knowledge or information. For example, limited training data for certain cardiac pathologies may contribute to higher epistemic uncertainty. Aleatoric uncertainty, also known as data uncertainty, results from inherent randomness or variability in the data. For example, variability in patient’s organ among different patients or imaging modalities introduces aleatoric uncertainty. Model Parameter Uncertainty is uncertainty associated with the model parameters, indicating how well the model has learned the underlying patterns in the training data. For example, variations in model parameters due to the stochastic nature of the optimization process 171 Attorney Docket No.1518-103PCT1 contribute to parameter uncertainty. Pixel-wise Uncertainty is associated with individual pixels in the image. It provides a confidence measure for each pixel in the segmentation mask. For example, certain regions of the heart may be more challenging to segment accurately, leading to higher pixel-wise uncertainty. Boundary Uncertainty is related to the boundaries between different structures or regions in the image. For example, the precise delineation of the endocardium or epicardium may be uncertain in regions where the boundaries are not well- defined. Regarding uncertainty in Time Series Data, in tasks involving sequential data, such as cardiac imaging over time, uncertainty can be related to variations in the temporal dimension. For example, segmentation of dynamic structures like the beating heart involves handling uncertainty associated with different phases of the cardiac cycle. Predictive Uncertainty is uncertainty in the model's predictions for unseen data points. For example, when the model encounters a novel pathology or an atypical cardiac structure, predictive uncertainty measures its confidence in providing accurate segmentation. Systematic Uncertainty is uncertainty stemming from systematic errors or biases in the data collection process or the model architecture. For example, if the training data is biased towards a specific demographic, the model may exhibit uncertainty when applied to a more diverse patient population. Model Output Uncertainty is uncertainty associated with the actual output of the model, indicating how confident the model is in its segmentation predictions. For example, the model may output a segmentation mask with a probability or confidence score for each pixel, reflecting the uncertainty associated with that pixel's classification. Still referring to FIG.14, a level of uncertainty 1460 may include a degree, statistical measure, percentage, or variable whether linguistic or numerical, and the like identifying a range of uncertainty. For example, processor 1404 may generate probability scores/confidence scores for locations of a model, indicating the model's confidence in its predictions. Calibration plots can be used to assess how well these confidence scores align with the true accuracy. Processor 1404 may perform a threshold analysis to investigate how varying decision thresholds for classification or segmentation affects the trade-off between sensitivity and specificity in uncertain regions. Threshold analysis may include task-specific metrics for clinical relevance. For example, in cardiac image segmentation, critical regions like the myocardium may have stricter uncertainty thresholds compared to less critical regions. Processor 1404 may implement Bayesian Neural Networks (BNNs) to perform posterior predictive checks to evaluate the 172 Attorney Docket No.1518-103PCT1 agreement between the model's predictions and the observed data, such as data store 1424, considering the uncertainty represented by the posterior distribution in Bayesian frameworks. In various embodiments, a level of uncertainty 1460 may be metrics determined by processor 1404, such as Pixel-wise Uncertainty Metrics, Boundary Displacement Error (BDE), Uncertainty- Aware Loss Functions, Calibration Metrics, and the like. Still referring to FIG.14, in some embodiments, level of uncertainty 1460 may be determined using Monte Carlo dropout. Monte Carlo dropout may include running a neural network multiple times using different dropout configurations. Each dropout configuration may include turning off turning off one or more nodes of a neural network. Monte Carlo dropout may be used to, for example, determine mean and variance parameters. In some embodiments, such a variance parameter may be used as level of uncertainty 1460. Still referring to FIG.14, in some embodiments, level of uncertainty 1460 may be determined using deep ensembles. A deep ensemble may include a plurality of machine learning models. An input may be applied to a plurality of machine learning model, and their outputs may be combined. For example, an average and/or variance of outputs of a plurality of models may be found. Level of uncertainty 1460 may be determined based on such variance. Still referring to FIG 14, shape identification model 1440 may be calibrated. Calibration may include fine-tuning or adjusting shape identification model 1440 predictions to align more closely with the actual probabilities. A well-calibrated model is one where, for instance, if it predicts a 70% probability for a certain event, that event actually occurs about 70% of the time. Calibrating shape identification model 1440 may include receiving a set of validation data. As used in the current disclosure, a “set of validation data” is a set of data used to calibrate a machine learning model, which the machine learning model has not been trained on. A set of validation data is used to assess the model's performance and, in this case, to calibrate level of uncertainty 1460. Processor 1404 may sort each datapoint of the validation set into a plurality of hyperfine bins as a function of a continuous value. As used in the current disclosure, "hyperfine bins" is a grouping of the data points into a set of very fine or detailed bins. These bins may be organized based on the values of the continuous value. Each bin corresponds to a specific range or interval of continuous values. Processor 1404 may additionally determine a bin-wise scaling factor for each of the plurality of hyperfine bins. As used in the current disclosure, "bin-wise scaling factors" refers to a factor or multiplier associated with each individual hyperfine bin. It 173 Attorney Docket No.1518-103PCT1 may be used to adjust or scale the data within each bin. The scaling factor can be unique to each bin and is typically determined based on some specific criteria or algorithm. Still referring to FIG 14, calibration of level of uncertainty 1460 may include temperature scaling. Temperature scaling may include adjusting confidence scores or probabilities generated by a model to make them better reflect the true uncertainty or reliability of the model's predictions. This technique is often used to improve the calibration of deep neural networks, especially in cases where model confidence scores do not align well with actual probabilities. Temperature scaling introduces a hyperparameter known as the "temperature" (T). The temperature is a positive scalar value that is applied to the logits (raw scores) before they are passed through a SoftMax function. By adjusting the temperature, processor 1404 may control the sharpness or spread of the probability distribution. A higher temperature makes the distribution more uniform, while a lower temperature makes the distribution sharper. In an embodiment, high temperature may smooth the distribution, reducing confidence in predictions. High temperature may increase level of uncertainty 1460. Low temperature may sharpen the distribution, reducing level of uncertainty 1460. Temperature parameter may be adjusted based on a validation dataset. A temperature that minimizes the difference between predicted probabilities and the true probabilities observed in a calibration dataset may be determined. Still referring to FIG.14, in some embodiments, processor 1404 may identify a high uncertainty location of 3D model 1456. As used herein, a “high uncertainty location” is a location of a 3D model associated with a level of uncertainty that is above the average level of uncertainty of the 3D model. A high uncertainty location may be determined using a method for determining uncertainty as described above. In a non-limiting example, a high uncertainty location may be determined using model output uncertainty. In some embodiments, a high uncertainty location may be identified on a map as described below. In some embodiments, a high uncertainty location may be determined using a method of determining a level of uncertainty used to generate a map as described below. In some embodiments, a high uncertainty location has higher level of uncertainty than 140%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 140%, or 1400% of locations of a 3D model. Still Referring to FIG.14, processor 1404 is configured to generate a map 1464 regarding one or more levels of uncertainty. A “map,” as used herein, refers to a visualization. Map 1464 may include level(s) of uncertainty to be visualized on heart model. Map 1464 may 174 Attorney Docket No.1518-103PCT1 include a color-coded heatmap, including other visual cues, symbols or indicators that alert a user to areas of 3D model 1456 that may require extra caution when used for planning or guidance during an ICE procedure. For example, after obtaining the segmentation results from heart model, map 1464 may be generated. Map 1464 may highlight the uncertainty or confidence level associated with each pixel in the segmentation. Assigning colors to different intensity levels in map 1464 allows for an intuitive visualization. Typically, warmer colors (e.g., red, or yellow) might represent high uncertainty, while cooler colors (e.g., blue, or green) could indicate low uncertainty. The color-coding can be adjusted based on specific thresholds or clinical requirements. In some embodiments, a map may include and/or identify one or more high uncertainty locations. For example, a high uncertainty location may be represented in a color such as red, orange, or yellow. In some embodiments, a mathematical formula may be used to generate colors based on uncertainty level. For example, a formula may convert high uncertainty values into hex values or RGB values associated with warmer colors and low uncertainty values into hex values or RGB values associated with cooler colors. Warm colors and cool colors need not be used to represent high and low uncertainty, respectively. For example, any first color and/or degree of opacity may represent high uncertainty, and any second color and/or degree of opacity may represent low transparency. For example, an opaque blue may be used to represent high uncertainty, and a moderately transparent yellow may be used to represent low uncertainty. In some embodiments, colors used may be those on a gradient from a first color to a second color. In some embodiments, only a single color may be used, such as when one of high or low uncertainty is represented by transparency. In some embodiments, more than two colors and/pr opacities may be used to represent uncertainty values. For example, a first gradient may be used to represent uncertainty values in a first range, and a second gradient may be used to represent uncertainty values in a second range. In some embodiments, discrete colors are used for certain uncertainty ranges. In some embodiments, uncertainty values may be represented along a continuous range of color and/or opacity. In some embodiments, non-color features may be used to represent uncertainty values. In non-limiting examples, a particular pattern may be applied to an uncertain region, brightness or darkness may be used, and line width and dot size may be used. Still referring to FIG.14, in some embodiments, visual representations used for uncertainty may be relative to one or more other points within that model. For example, the 175 Attorney Docket No.1518-103PCT1 lowest uncertainty point may always be green, and the highest uncertainty point may always be red. This may allow a user to understand which points are the most or least uncertain, even when all points are relatively certain or uncertain. In some embodiments, a first visual representation of uncertainty may be used for absolute uncertainty, and a second visual representation of uncertainty may be used for relative uncertainty. For example, opacity may be used to represent relative uncertainty within a model, and color may be used to represent absolute uncertainty. Still referring to FIG.14, in some embodiments, mathematical formula for converting the uncertainty values to colors may be recalculated and/or updated as a function of the upper and lower uncertainty values. For example, the mathematical formula may be updated such that the lowest uncertainty value represents one end of the color / opacity spectrum and the highest uncertainty value represents the other end of the color / opacity spectrum. Still referring to FIG.14, generating map 1464 may include methods such as Class Activation Mapping (CAM). Class Activation Mapping is a technique that originated for image classification tasks and has been extended to provide visual insights into the regions of an image that are most important for a particular class. CAM allows the visualization of the spatial attention of a convolutional neural network (CNN) by generating heat maps that highlight discriminative regions. CAM may be applied to the last convolutional layer of a CNN. The features extracted by this layer capture high-level semantic information, making it suitable for visualizing the importance of different regions in an image. CAM is typically applied to the last convolutional layer of a CNN. The features extracted by this layer capture high-level semantic information, making it suitable for visualizing the importance of different regions in an image. The output of the global average pooling is then fed into a fully connected layer with a softmax activation function. This converts the features into class scores, indicating the likelihood of the image belonging to different classes. The CAM algorithm computes a weighted sum of the original feature maps based on the weights of the fully connected layer. These weights are determined during the training process and represent the importance of each feature map for a specific class. The weighted sum is applied to the original feature maps, producing a single heat map. This heat map highlights the regions of the input image that contributed most to the prediction for the target class. The generated heat map can be overlaid on the input image, visually indicating which regions are most relevant for the predicted class. Typically, warmer colors (e.g., red, or yellow) represent higher activation or importance. 176 Attorney Docket No.1518-103PCT1 Still Referring to FIG.14, generating map 1464 may include Grad-CAM (Gradient- weighted Class Activation Mapping). Grad-CAM is an extension of Class Activation Mapping (CAM) that enhances the localization capabilities by incorporating gradient information from the final convolutional layer of a neural network. Grad-CAM helps to generate heat maps that highlight discriminative regions in an image, providing more fine-grained insights into where a convolutional neural network (CNN) is focusing its attention when making predictions. In traditional CAM, the last convolutional layer's feature maps are linearly combined to obtain a weighted sum, and the resulting weights are used to create a heat map that highlights relevant regions for a specific class. Grad-CAM improves upon CAM by introducing gradient information. It computes the gradients of the predicted class score with respect to the feature maps of the last convolutional layer. Grad-CAM retains the global average pooling (GAP) operation applied after the last convolutional layer, as it is an integral part of CAM. The GAP operation condenses the spatial information into a single value per feature map. The gradients obtained in the previous step are used to calculate the importance of each feature map. These gradients represent the importance of each feature map in contributing to the final prediction. A weighted sum is computed using these gradients, and this is combined with the original feature maps. The computed sum goes through a ReLU activation function, discarding any negative values. This step emphasizes positive contributions and suppresses negative ones. The ReLU- activated weighted sum is linearly combined with the original feature maps to produce a weighted combination. This combination retains spatial information and helps create a more accurate heat map. The resulting heat map is often normalized to enhance visualization, ensuring that the values are within a specific range (e.g., between 0 and 14). The final heat map generated by Grad-CAM is then overlaid on the input image, highlighting the regions of interest for the predicted class. The intensity of the heat map indicates the importance of different regions. Grad- CAM enhances the interpretability and explainability of deep learning models, allowing practitioners and researchers to understand which parts of an image are crucial for a particular prediction. This is particularly valuable in applications such as medical imaging or any domain where understanding the decision-making process is critical. Still Referring to FIG.14, generating map 1464 may include utilizing a “SmoothGrad technique,” a technique designed to improve the interpretability of neural network predictions by reducing the noise in the attribution maps or heat maps generated by visualizing 177 Attorney Docket No.1518-103PCT1 gradients. It is particularly useful for understanding the decision-making process of deep learning models, especially in scenarios where the explanations need to be robust and less sensitive to input perturbations. The primary goal of SmoothGrad is to enhance the visual quality of attribution maps generated by visualizing gradients. Attribution maps highlight the regions in the input that contribute most to a model's prediction. SmoothGrad aims to reduce the impact of noise in these maps, providing more stable and interpretable visualizations. The key idea behind SmoothGrad is to introduce perturbations to the input data. Instead of attributing the prediction solely to the gradients calculated with respect to the original input, the gradients are averaged over multiple perturbed versions of the input. By averaging the gradients over multiple perturbed samples, SmoothGrad helps reduce the impact of noise or irrelevant features in the attribution maps. This is particularly beneficial when dealing with complex or noisy datasets. Perturbation techniques include adding Gaussian noise, random rotations, or random translations to the input data. These perturbations create variations in the input while preserving the essential features, leading to more stable and reliable attribution maps. For each perturbed input, gradients are calculated with respect to the model's output. These gradients are then averaged over all perturbed samples. This process smoothens the attribution map by reducing the influence of random noise. The averaged gradients may undergo normalization or scaling to ensure that the values are interpretable and within a specific range. This step can enhance the consistency and comparability of the generated attribution maps. The final step involves generating a heat map using the smoothed gradients. The heat map represents the attribution of different regions in the input to the model's prediction, providing a clearer and more stable visualization. Still Referring to FIG.14, generating map 1464 may include implementing one or more Gaussian Processes. A Gaussian Process is a collection of random variables, any finite subset of which has a joint Gaussian distribution. In simpler terms, it's a distribution over functions rather than a distribution over finite-dimensional vectors. Gaussian Processes (GPs) can be applied to generate heat maps in various ways, particularly in the context of regression tasks where one would want to predict continuous values across a spatial domain. Given a set of observed data points, the GP can predict the values at unobserved locations in the spatial domain. Importantly, it also provides uncertainty estimates associated with these predictions. This uncertainty can be visualized as a heat map. The predicted values from the GP represent the main heat map, indicating the expected values across the spatial domain. The uncertainty associated 178 Attorney Docket No.1518-103PCT1 with each prediction can be visualized as an uncertainty heat map. This uncertainty heat map provides insights into regions where the model is less confident about its predictions. Overlay of the main heat map and the uncertainty heat map on the original spatial data may create a composite visualization. Warmer colors in the main heat map might represent higher predicted values, while the uncertainty heat map's intensity could indicate regions where the model's predictions are less certain. Still referring to FIG.14, uncertainty may be depicted in ways other than a color coded heatmap. For example, a 2-dimensional cross section of a 3D model may be taken, and a third dimension indicating certainty or uncertainty may be added. In another example, transparency may indicate certainty or uncertainty. In another example, sharpness may indicate certainty or uncertainty. In another example, size of dots or thickness of lines making up a 3D model may indicate certainty or uncertainty. In some embodiments, locations with particular certainty features may be identified. For example, high uncertainty location may be included and/or identified. Still referring to FIG.14, processor 1404 is configured to overlay map 1464 onto heart model. In some embodiments, the overlay may be placed on 3D model 1456 and go through a refinement process as described above. Overlaying map 1464 on a model may include implementing spatial alignment methods, texture mapping techniques wherein the color information from the heat map is mapped onto the vertices or faces of the 3D model, shader programs that define how the heat map values influence the final appearance of the 3D model, visualization software or programming libraries that support 3D rendering and overlay capabilities, interactivity visualization, quality control methods, and the like. For example, texture mapping may include UV Mapping wherein each point on the surface of a 3D model is associated with a set of texture coordinates often denoted as U and V. These coordinates are analogous to the X and Y coordinates on a 2D image. UV mapping establishes the correspondence between points on the 3D model and pixels on the 2D texture. In another example, interactive visualization may create visual representations of data that users can interact with and manipulate. This approach allows users to explore and analyze data dynamically, gaining insights through direct engagement with the visual representation. For example, mouse interactivity may allow users to interact with visual elements using mouse actions, such as hovering over data points for additional information, clicking to drill down into details, or 179 Attorney Docket No.1518-103PCT1 dragging to pan and zoom. Filtering and Selection capabilities may allow a user to filter data based on specific criteria or select subsets of data for closer examination. This is particularly useful when dealing with large datasets. Spatial Exploration may allow users to zoom in to explore details or pan to navigate across the space. Still referring to FIG.14, in an embodiment, a map created by a mapping catheter with map 1464 as described herein based on set of images may be combined to help with, for example, and without limitation, identification of procedural targets (e.g., ablation targets) and personalization of one or more operation parameters (e.g., ablation parameters). As a non- limiting example, set of images such as ICE images may be used to create a 3D model of a patient’s organ e.g., a heart. EGM data recorded by, for instance, and without limitation, a mapping catheter that capturing electrical activity within the patient’s organ may be used to generate an electroanatomic map using a mapping system. Processor 1404 may be configured to align (using sensorless techniques that require fiducial pint-based registration or other spatial alignment methods) 3D model and the electroanatomic map in the same coordinate space. In some cases, one or more shader programs may be employed to define how values (e.g., color information, level of uncertainty 1460, and any other values influencing the model’s final appearance are mapped to the 3D model. Additionally, or alternatively, combined map may be interactive. User may engage with the 3D model dynamically, for example, and without limitation, exploring data points, filtering and selecting specific subset of data points (e.g., at least a portion of the 3D model), zooming and panning across the 3D model, and the like. Still referring to FIG.14, further, 3D model may be used in the placement and sizing of medical devices, such as, without limitation, left atrial appendage occlusion (LAAO) device. As a non-limiting example, ICE imaging may be used to capture set of images of a patient’s organ e.g., a heart (specifically focusing on the LA and LAA).3D model created using such set of images may serve as a basis for planning and executing the placement of the LAAO device. In some cases, the 3D model may allow clinicians to visualize the exact structure and dimensions of the modeled LA and LAA in order to determine an appropriate size of the LAAO device to ensure optimal fit and function. For example, and without limitation, using the 3D model, clinicians may plan the precise placement of the LAAO device according to one or more anatomical landmarks on the 3D model. After the initial placement, another imaging session may be conducted to verify the positioning and fit of the LAAO device. In some cases, the two 180 Attorney Docket No.1518-103PCT1 imaging session may be performed via different imaging techniques; for instance, and without limitation, second set of images may include one or more CT images of the heart. In some cases, 3D model may be used in detecting any potential leakage around the device. Accurate detection may ensure that the device effectively prevents blood flow into the LAA thereby reducing the risk of complications. Still referring to FIG.14, in some cases, an ICE frame taken during an ICE procedure or synthesized for machine learning training purposes may be also overlaid at a corresponding location or heart model. Overlaying the ICE frame may include registering the ICE frame to the generated 3D model 1456 using the image processing model. This process and method may use a processing system, including at least a processor, image generator, and camera transformation program, as disclosed in this disclosure. For example, the processing system may include at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a heart model related to a patient’s heart, identify a region of interest within the heart model, wherein identifying the region of interest includes locating at least a point of view on the heart model and determining a view angle corresponding to the at least a view origin, wherein the at least a point of view and the corresponding view angle define at least one field of view that include at least a portion of the heart model. The at least a processor may be further configured to generate at least a medical image as a function of the region of interest using an image generator, wherein the at least a medical image captures an anatomical structure of the at least a portion of the heart model. Still referring to FIG.14, in some embodiments, processor 1404 may receive more than one set of images of patient’s organ. For example, processor 1404 may receive a first set of images of patient’s organ, and such first image may be used to perform one or more functions described herein. As examples, a 3D model, a level of uncertainty, and/or a map may be generated based on such first set of images. A second set of images may be captured as a function of such a 3D model, level of uncertainty, and/or map. In some embodiments, a second set of images may be captured of one or more high uncertainty locations. Still referring to FIG.14, image sets beyond the first may be captured and/or received as described above with respect to set of images 1412. A second set of images may be captured as a function of a high uncertainty location. For example, a first set of images may be 181 Attorney Docket No.1518-103PCT1 used to determine a first 3D model, and a map including a high uncertainty location may be overlaid on the first 3D model. Such first 3D model and map may be displayed to a user, such as through a display device. A user, such as a medical professional, may use a cardiac image capture device to capture a second set of images based on the 3D model and map. For example, a user may use a cardiac image capture device to capture a second set of images of a high uncertainty location. Still referring to FIG.14, image sets beyond the first may be used to create a 3D model and/or a map. In some embodiments, a second set of images may be captured, and the second set of images may be used to generate a 3D model and/or a map as described herein. In some embodiments, a second set of images may be combined with an earlier captured first set of images to generate a 3D model and/or a map. For example, a first set of images may provide data as to a majority of a patient’s organ, but a 3D model based on a first set of images may include one or more high uncertainty locations; a second set of images providing additional data on one or more high uncertainty locations may be captured by a user using a cardiac image capture device, and the first set of images and second set of images may be combined to provide more complete patient’s organ data. A second 3D model and/or a second map may be generated based on the first set of images and the second set of images. In this case, level of uncertainty at each location of the plurality of locations may be re-calculated. In some embodiments, a second 3D model may be generated using a filtered set of first and second images, as disclosed in this disclosure. In some embodiments, processor may continuously update second 3D model as further images are collected. Updating second 3D model may include any of the methods for generating a 3D model as disclosed in this disclosure. Processor may be configured to update a 3D after every set time period. Set time period may include 14 second, 5 seconds, 140 seconds, 14 minute, and the like. Set time period may range from 140 ms to 5 minutes. Processor may be configured to update a 3D model after a certain number of additional images have been collected. For example processor may update 3D model after 20 additional images have been collected. For example processor may update 3D model after 140 additional images have been collected. For example processor may update 3D model after 50 additional images have been collected. For example processor may update 3D model after 1400 or more additional images have been collected. 182 Attorney Docket No.1518-103PCT1 Still referring to FIG.14, in some embodiments, a second set of images may be captured based on first set of images. For example, if levels of uncertainty of a 3D model are sufficiently high, then processor 1404 may configure a display device to display a notification to a user indicating that a second set of images is to be captured. In another example, if levels of uncertainty of a 3D model are sufficiently high, then a user may capture a second set of images. In some embodiments, processor 1404 may automatically trigger capture of a second set of images and/or repositioning of a cardiac image capture device. In some embodiments, this may occur based on a level of uncertainty of a 3D model. For example, processor 1404 may automatically reposition a cardiac image capture device into a location and orientation for capturing an image of a part of patient’s organ 1416 associated with a high uncertainty location of 3D model 1456. This may occur, for example, if a level of uncertainty exceeds a threshold. Non-limiting metrics which may be used to determine information displayed to a user and/or automatically capture a second set of images include a maximum uncertainty, an average uncertainty, a median uncertainty, a level of uncertainty at a certain uncertainty percentile, and the like. Such metrics may be compared to thresholds to determine information displayed to a user and/or automatically capture a second set of images. Still referring to FIG.14, in some embodiments, processor 1404 may remove and/or filter out one or more frames of one of more sets of images such that a 3D model is generated based on a subset of the sets of images. In some embodiments, one or more images may be filtered out such that a subset of a set of images contains a desired number of images. For example, desired number of images may be equal to a bandwidth of a neural network. In some embodiments, images may be selected to be filtered out such that images remaining in a subset of a set of images are sufficiently diverse. For example, a set of images may include many frames captured depicting a first feature, and only a single frame (or fewer frames) of additional features. In this example, a frame selected to be filtered out may include a frame depicting the first feature. In some embodiments, one or more frames which are identical or nearly identical to one or more additional frames may be filtered out. In some embodiments, processor 1404 may remove an image of first set of images from the first set of images. In some embodiments, processor 1404 may add one or more additional frames to a set of images such that a 3D model is generated based on a larger set of images. In some embodiments, one or more images of a set of images may be duplicated in order to arrive at a desired number of images in a set of images. In 183 Attorney Docket No.1518-103PCT1 some embodiments, processor 1404 may duplicate an image of first set of images and add the duplicate to the first set of images. In some embodiments, a first set of images and a second set of images may have been received by processor 1404, and processor 1404 may generate a 3D model as a function of one or more images of the first set of images and one or more images of the second set of images. For example, processor 1404 may determine a combined set of images, may perform an image filtration step and/or an image duplication step as described above on the combined set of images, and may generate a 3D model as a function of a resulting set of images. In some embodiments, image filtration may use a machine vision module. In some embodiments, a desired number of images may include a number of images which satisfies input requirements of a machine learning model or other algorithm. In some embodiments, a machine learning model or other algorithm may accept as an input a limited number of frames in order to, for example, avoid excessive use of processing power. In some embodiment, processor may filter first and/or second set of images to include 1428 images. In some embodiments, this filtering step may be performed on any available images (such as a combination of the first and second set of images, along with any set collected thereafter). With continued reference to FIG.14, apparatus 1400 may further include a display device 1468. As used in this disclosure, a “display device” is an electronic device that visually presents information to a user. In an embodiment, display device may include an output interface that translates data such as, without limitation, subsequent 3D heart model from processor 1404 or other computing devices into a visual form that can be easily understood by user. In some cases, subsequent 3D heart model/or other data described herein such as, without limitation, ICE images, 3D VOR, shape parameters initial model and/or template model may also be displayed through display device 1468 using a user interface 1472. User interface 1472 may include a graphical user interface (GUI), wherein the GUI may include a window in which subsequent 3D heart model and/or other data described herein may be displayed. In an embodiment, user interface 1472 may include one or more graphical locator and/or cursor facilities allowing user to interact with subsequent 3D heart model and/or any other data, or even process described herein; for instance, and without limitation, by using a touchscreen, touchpad, mouse, keyboard, and/or other manual data entry device, user may enter user input containing selecting specific regions, adding comments, adjusting parameter, and/or the like. In a non-limiting example, user interface 1472 may include one or more menus and/or panels permitting selection of measurements, 184 Attorney Docket No.1518-103PCT1 models, visualization of data/model to be displayed and/or used, elements of data, functions, or other aspects of data/model to be edited, added, and/or manipulated, options for importation of and/or linking to application programmer interfaces (APIs), exterior services, data source, machine-learning models, and/or algorithms, or the like. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which a visual interface and/or elements thereof may be implemented and/or used as described in this disclosure. One or more systems, methods, or devices herein may be consistent with any system, method, or device disclosed in this disclosure. Now referring to FIG.15, an exemplary embodiment of an overlaid heat map 1500 is illustrated. Heat map 1500 may include elements described with reference to other figures and/or may be generated as described with reference to other figures. Heat map 1500 may illustrate one or more of levels of uncertainty differentiated by color, shading, texture, and the like as described above. For example, heat map 1500 may depict a first level of uncertainty 1504. In some embodiments, first level of uncertainty 1504 may be determined as a function of a level of uncertainty associated with one or more points within a region associated with first level of uncertainty, such as point 1508. In some embodiments, heat map 1500 may depict one or more additional levels of uncertainty, such as second level of uncertainty 1512, third level of uncertainty 1516, and/or fourth level of uncertainty 1520. In some embodiments, levels of uncertainty may be displayed as discrete regions and/or discrete levels of uncertainty. This may make heat map 1500 more readable for a user than an alternative in which continuous levels of uncertainty are displayed. In some embodiments, levels of uncertainty may be displayed on a continuous scale. For example, each point on heat map 1500 may have a color associated with its level of uncertainty. This may improve accuracy of depictions of uncertainty at specific locations. In some embodiments, one or more levels of uncertainty may represent a certain percentage of certainty and/or accuracy, in the depiction of a shape parameter, location, geometric identifier and the like. In some embodiments, map 1500 may depict levels of uncertainty relative to other levels of uncertainty of map 1500. For example, if first level of uncertainty 1504 is the highest level of uncertainty of map 1500, and second level of uncertainty 1512 is the lowest level of uncertainty of map 1500, then third level of uncertainty 1516 and/or fourth level of uncertainty 1520 may have values, colors, patterns, levels of transparencies, and 185 Attorney Docket No.1518-103PCT1 the like associated with how close to the minimum and maximum levels of uncertainty they are. In some embodiments, each level of uncertainty may be measured on an absolute scale. For example, first level of uncertainty 1504 may have a value, color, pattern, level of transparency, and the like associated with a percent likelihood that a 3D model at first level of uncertainty 1504 is correct. In some embodiments, each level of uncertainty may be scaled based on color code/texture code based scales as described above. For example, first level of uncertainty 1504 may include a light shading of an area of the heart model, wherein as the level of uncertainty progress, shading darkens in second level of uncertainty 1512. Referring now to FIG.16, an exemplary system 1600 for generating a three- dimensional (3D) model of patient’s organ is illustrated. System 1600 may include cardiac image capture device 1604. Cardiac image capture device 1604 may include a cardiac image capture device described with reference to another figure herein. For example, cardiac image capture device 1604 may include an ICE catheter. System 1600 may further include computing device 1608. Computing device may receive a first set of images, such as a first set of ICE images, from cardiac image capture device 1604. Computing device 1608 may perform one or more processing steps described herein, such as application of a shape identification model to generate a set of shape parameters, application of a statistical shape model to generate a 3D model, determination of a level of uncertainty and/or a map, overlay of a map onto a 3D model, and/or display of a map and/or a 3D model to a user using user interface 192 and/or user device. User 1612 may receive information, such as information as to a level of uncertainty at a particular location of a 3D model and/or patient’s organ or anatomical object 116 and may position cardiac image capture device 1604 within patient’s organ 1416 in order to capture a second set of images, such as a second set of ICE images. User 1612 may perform this through, for example, interaction with user interface 1472. Second set of images may be used to generate an updated 3D model, an updated map, and/or an updated level of uncertainty, which may be displayed to user 1612 through user interface 1472. Referring now to FIG.17, a flow diagram illustrating an exemplary embodiment of a method 1700 for generating a three-dimensional (3D) model of patient’s organ with an overlay is illustrated. At step 1705, method 1700 includes receiving, by a processor, a set of images of a patient’s organ pertaining to a subject. This may be implemented as disclosed above and with reference to FIGS.1-17. At step 1710, method 1700 includes generating, by the processor, a set 186 Attorney Docket No.1518-103PCT1 of shape parameters based on the set of images, wherein generating the set of shape parameters includes receiving cardiac geometry training data including a plurality of image sets as input correlated to a plurality of shape parameter sets as output, training a shape identification model using the cardiac geometry training data, and generating the set of chape parameters using the shape identification model. This may be implemented as disclosed above and with reference to FIGS.1-17. At step 1715, method 1700 includes generating, by the processor, a 3D model of the patient’s organ based on the set of shape parameters. This may be implemented as disclosed above and with reference to FIGS.1-10. At step 1720, method 1700 includes generating, by the processor, a map by determining a level of uncertainty at each location of a plurality of locations on the generated 3D model. This may be implemented as disclosed above and with reference to FIGS.1-17. At step 1725, method 1700 includes overlaying, by the processor, the 3D model with the map. This may be implemented as disclosed above and with reference to FIGS.1-17. Referring now to FIG.18, an exemplary embodiment of a method 1800 of generating a three-dimensional (3D) model of patient’s organ is illustrated. One or more steps if method 1800 may be implemented, without limitation, as described with reference to other figures. One or more steps of method 1800 may be implemented, without limitation, using at least a processor. Still referring to FIG.18, in some embodiments, method 1800 may include receiving a first set of images of patient’s organ 1805. In some embodiments, capturing a second set of images may include using a display device, displaying the first 3D model of the patient’s organ to a user; and by the user, positioning the cardiac image capture device for capturing an image of the low confidence region. In some embodiments, displaying the first 3D model of the patient’s organ to the user may include generating a first map by determining a level of uncertainty at each location of a plurality of locations on the generated first 3D model; and overlaying the first map onto the first 3D model. In some embodiments, the first map identifies the high uncertainty region of the first 3D model. In some embodiments, the first map includes a color-coded heat map configured to visualize one or more areas of uncertainty on the first 3D model. In some embodiments, the cardiac image capture device includes an intracardiac echocardiography catheter. Still referring to FIG.18, in some embodiments, method 1800 may include generating a first 3D model of the patient’s organ as a function of the first set of images 1810. In 187 Attorney Docket No.1518-103PCT1 some embodiments, generating the first 3D model includes generating a set of shape parameters based on the first set of images; generating the set of shape parameters includes receiving cardiac geometry training data including a plurality of image sets as inputs correlated to a plurality of shape parameter sets as outputs; training a shape identification model using the cardiac geometry training data; and generating the set of shape parameters using the shape identification model; and the first 3D model is generated based on the set of shape parameters. In some embodiments, the high uncertainty region is determined using model output uncertainty. In some embodiments, the plurality of shape parameter sets of the cardiac geometry training data is generated using computed tomography. In some embodiments, the shape identification model includes a deep neural network. In some embodiments, the shape identification model includes a convolutional neural network. In some embodiments, the second 3D model is generated using the shape identification model and the second set of images. In some embodiments, the second 3D model is generated using the first set of images. In some embodiments, generating the first 3D model further includes using a statistical shape model to generate the first 3D model as a function of the set of shape parameters. In some embodiments, the set of shape parameters includes a plurality of numerical descriptors, wherein each numerical descriptor of the plurality of numerical descriptors represents a geometric characteristic of the patient’s organ. In some embodiments, each shape parameter within the set of shape parameters includes a corresponding parameter range. Still referring to FIG.18, in some embodiments, method 1800 may include calculating a level of uncertainty at each location of a plurality of locations on the first 3D model 1815. Still referring to FIG.18, in some embodiments, method 1800 may include receiving a second set of images of the patient’s organ corresponding to a high uncertainty region of the first 3D model 1820. Still referring to FIG.18, in some embodiments, method 1800 may include generating a second 3D model as a function of the second set of images 1825. Still referring to FIG.18, in some embodiments, method 1800 may further include displaying the second 3D model to a user. In some embodiments, displaying the second 3D model of the patient’s organ to the user includes generating a second map by determining a level of uncertainty at each location of a plurality of locations on the generated second 3D model; and 188 Attorney Docket No.1518-103PCT1 overlaying the second map onto the second 3D model. In some embodiments, the second map includes a color-coded heat map configured to visualize one or more areas of uncertainty on the second 3D model. Still referring to FIG.18, in some embodiments, method 1800 may be performed using a plurality of cardiac image capture devices. For example, a first set of images may be captured using a first cardiac image capture device and a second set of images may be captured using a second cardiac image capture device. Now referring to FIG.19, a flow diagram of an exemplary embodiment of a method 1900 of generating a three-dimensional (3D) model of a patient’s organ is illustrated. Method 1900 include, using at least a processor, receiving a first set of images of a patient’s organ 1905. Still referring to FIG.19, in some embodiments, method 1900 may include, using the at least a processor, determining, at a trained neural network, a first set of shape parameters as a function of the first set of images 1910. Still referring to FIG.19, in some embodiments, method 1900 may include, using the at least a processor, generating a first 3D model of the patient’s organ as a function of the first set of shape parameters 1915. In some cases, generating the first 3D model may include generating, as a function of the first set of shape parameters, the first 3D model using a statistical shape model. Still referring to FIG.19, in some embodiments, method 1900 may include, using the at least a processor, calculating a level of uncertainty at each location of a plurality of locations on the first 3D model of the patient’s organ 1920. In some cases, calculating the level of uncertainty at each location of the plurality of locations of the first 3D model of the patient’s organ may include generating a first map including the level of uncertainty at each location of the plurality of locations on the first 3D model of the patient’s organ, overlaying the first map onto the first 3D model, and displaying, using a display device, the first 3D model of the patient’s organ to a user. Still referring to FIG.19, in some embodiments, method 1900 may include, using the at least a processor, receiving a second set of images of the patient’s organ corresponding to a high uncertainty region of the first 3D model 1925. In some cases, the first set of images and the second set of images of the patient’s organ may include a plurality of ultrasound images, and wherein the plurality of ultrasound images containing one or more of a transesophageal 189 Attorney Docket No.1518-103PCT1 echocardiogram image, transthoracic echocardiogram image, and point-of-care ultrasound image. In some cases, receiving the second set of images of the patient’s organ may include identifying, on the first map, the high uncertainty region of the first second 3D model of patient’s organ by comparing the level of uncertainty at each location of the plurality of locations to a pre- determined uncertainty threshold. Still referring to FIG.19, in some embodiments, method 1900 may include, using the at least a processor, determining, at the trained neural network, a second set of shape parameters as a function of the first set of images and the second set of images 1930. In some cases, determining the second set of shape parameters may include combining the second set of images with the first set of images by replacing one or more images corresponding to the high uncertainty region of the first 3D model within the first set of images with the second set of images. Determining the second set of shape parameters may further include calibrating the trained neural network by fine-tuning the trained neural network using the combined sets of images and determining the second set of shape parameters as a function of the second set of images using the trained neural network. Still referring to FIG.19, in some embodiments, method 1900 may include, using the at least a processor, generating a second 3D model of the patient’s organ as a function of the second set of shape parameters 1935. In some cases, generating the second 3D model may include adjusting, at the statistical shape model, the first 3D model as a function of the second set of shape parameters. In some cases, generating the second 3D model of the patient’s organ may include generating a second map by re-calculating the level of uncertainty at each location of the plurality of locations on the second 3D model of the patient’s organ, overlaying the second map onto the second 3D model of the patient’s organ, and displaying, using the display device, the second 3D model of the patient’s organ to the user. In some cases, each one of the first map and the second map may include a color-coded heat map configured to visualize one or more areas of uncertainty on the first 3D model and second 3D model of the patient’s organ respectively. At a high level, an apparatus and method for generating a three-dimensional (3d) model of a structure with an overlay is disclosed. An overlay may include determining a level of uncertainty of outputs of models used, as described below, in regard to deciphering the geometric deposition of a structure of a subject. In some cases, the level of uncertainty may be derived from variability within the distribution of shape parameters, image quality assessment, measurement 190 Attorney Docket No.1518-103PCT1 errors and/or the like. The overlay may be visualized on a 3D model. In some cases, level of uncertainty may be color-coded, for example, a heat map may be overlaid on top of a 3D model. In other cases, other visual cues e.g., symbols or indicators that alert user to areas of a 3D model that may require extra caution when used for planning or guidance during an ICE procedure. Aspects of the present disclosure can be used to simplify the ultrasound assisted anatomy reconstruction system by using an AI based algorithm to learn the positioning and 3D reconstruction directly from the ultrasound images. This is so, at least in part, because apparatus is configured to implement AI-based learning from CT datasets. In an embodiment, neural networks based estimation removes the need for complicated systems, such as manual segmentation and reconstruction methods, specialized hardware, FAM, among others. Referring now to FIG.20, an exemplary embodiment of an apparatus 2000 for generating 3D model of a structure via machine-learning is illustrated. Apparatus 2000 includes at least a processor 2004. Processor 2004 may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Computing device may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. Processor 2004 may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. Processor 2004 may interface or communicate with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for connecting processor 2004 to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus, or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network may employ a wired and/or a wireless mode of communication. In general, any network 191 Attorney Docket No.1518-103PCT1 topology may be used. Information (e.g., data, software etc.) may be communicated to and/or from a computer and/or a computing device. Processor 2004 may include but is not limited to, for example, a computing device or cluster of computing devices in a first location and a second computing device or cluster of computing devices in a second location. Processor 2004 may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. Processor 2004 may distribute one or more computing tasks as described below across a plurality of computing devices of computing device, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. Processor 2004 may be implemented using a “shared nothing” architecture in which data is cached at the worker, in an embodiment, this may enable scalability of apparatus 2000 and/or computing device. With continued reference to FIG.20, processor 2004 may be designed and/or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, processor 2004 may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Processor 2004 may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing. With continued reference to FIG.20, apparatus includes a memory 2008 communicatively connected to at least a processor 2004, wherein the memory 2008 contains instructions configuring at least a processor 2004 to perform any processing steps described 192 Attorney Docket No.1518-103PCT1 herein. As used in this disclosure, “communicatively connected” means connected by way of a connection, attachment, or linkage between two or more relata which allows for reception and/or transmittance of information therebetween. For example, and without limitation, this connection may be wired or wireless, direct, or indirect, and between two or more components, circuits, devices, systems, and the like, which allows for reception and/or transmittance of data and/or signal(s) therebetween. Data and/or signals therebetween may include, without limitation, electrical, electromagnetic, magnetic, video, audio, radio, and microwave data and/or signals, combinations thereof, and the like, among others. A communicative connection may be achieved, for example and without limitation, through wired or wireless electronic, digital, or analog, communication, either directly or by way of one or more intervening devices or components. Further, communicative connection may include electrically coupling or connecting at least an output of one device, component, or circuit to at least an input of another device, component, or circuit. For example, and without limitation, via a bus or other facility for intercommunication between elements of a computing device. Communicative connecting may also include indirect connections via, for example and without limitation, wireless connection, radio communication, low power wide area network, optical communication, magnetic, capacitive, or optical coupling, and the like. In some instances, the terminology “communicatively coupled” may be used in place of communicatively connected in this disclosure. With continued reference to FIG.20, processor is configured to receive a set of images 2012 of a structure 2016 of a subject 2020. As used in this disclosure, a “set of images” refers to a group of one or more visual representations. Set of images 2012 may include, without limitation, a two-dimensional image. In some embodiments, set of images 2012 may include an ultrasonic image. As used herein, an “ultrasonic image” is an image generated as a function of a reflection of a sound wave off of a structure. Non-limiting examples of ultrasonic images and/or imaging techniques include intracardiac echo (ICE) images, transthoracic echocardiograms (TTE), transesophageal echocardiograms (TEE), and point of care ultrasound (POCUS). In some embodiments, a set of ultrasonic images of the patient’s organ may include an image selected from the list consisting of a transesophageal echocardiogram image, a transthoracic echocardiogram image, and a point-of-care ultrasound image. As used herein, a “structure” is a component of a subject. Non-limiting examples of structures include organs and tissues. In some 193 Attorney Docket No.1518-103PCT1 embodiments, a structure includes an organ of a subject. In non-limiting examples, a structure may include a heart, lung, spleen, liver, kidney, muscle, skeleton, intestine, stomach, vein, and/or artery. In additional non-limiting examples, a structure may include a left atrium, left atrial appendage, left ventricle, right ventricle, and/or a right atrium. In an embodiment, set of images 2012 may include a set of intracardiac echocardiography (ICE) images. As used herein, a “set of ICE images” is a collection of ultrasound images obtained from within the heart’s chambers or blood vessels. In some cases, ICE images may be captured using a specialized catheter equipped with an ultrasound transducer that is inserted into the body and guided to the heart of subject 2020. In an embodiment, set of images 2012 may provide a detailed and real-time visualizations of cardiac anatomy. As used herein, “cardiac anatomy” is the structural composition of the heart and its associated blood vessels. Set of images 2012 may also include internal structures, functions, and blood flow patterns of the heart of subject 2020. Other exemplary embodiments of set of images 2012 may include, without limitation, X-ray images, magnetic resonance imaging (MRI) scans, computed tomography (CT) scans, ultrasound images, optical images, digital photographs, or any other form of visual data. Additionally, images within set of images 2012 may be related in terms of content, time of capture, sequence, or any other relevant parameters described herein. In a non- limiting example, each image of set of images 2012 may represent a particular view, angle, or perspective of an object, subject, or scene, and may be in two-dimensional (2D) or 3D format. Images of set of images 2012 may include, without limitation, any two-dimensional or three- dimensional images of any anatomy or anatomical structure, including without limitation images of any internal organ, tissue including without limitation muscular, connective tissue, epithelial tissue, and/or nervous tissue, bone, and/or any other element that may be imaged within a human and/or animal body. Still referring to FIG.20, in a non-limiting example, structure 2016 may include chambers (e.g., four chambers including left and right atria and left and right ventricles), valves (i.e., the structures that regulate blood flow between chambers and vessels, including mitral, tricuspid, aortic, and pulmonary valves), vessels (e.g., aorta, pulmonary arteries and veins, and coronary arteries), conduction system (i.e., a network of specialized cells that control the heart’s electrical activity and rhythm), muscular and connective tissues (e.g., heart’s muscular walls, septa, any other connective tissues that provide structural integrity and enable contraction), LAA 194 Attorney Docket No.1518-103PCT1 and other appendages, pathological features (e.g., any abnormalities, defects, and/or the like), and/or other components of a heart. Still referring to FIG.20, as used in this disclosure, a “subject” refers to an individual organism. In an embodiment, subject 2020 may include a human, such as a human undergoing a medical procedure such as atrial fibrillation (AF) ablation. In some cases, subject 2020 may include a provider of set of images 2012 described herein. In other cases, subject 2020 may include a recipient or a participant in a clinical trial or research study. In a non-limiting example, subject 2020 may include a human patient with AF who is undergoing a procedure, an individual undergoing cardiac screening, a participant in a clinical trial, patient with congenital heart disease, heart transplant candidate, patient receiving follow-up care after cardiac surgery, healthy volunteer, patient with heart failure, or the like. Additionally, or alternatively, subject 2020 may include an animal models (i.e., animal used to model AF such as a laboratory rat). Still referring to FIG.20, in an embodiment, each ultrasonic image of set of ultrasonic images may include a particular view of subject’s 2020 heart’s chambers, valves, vessel, and/or the like. In a non-limiting example, set of images 2012 may include multiple views e.g., different angles and perspectives of subject’s 2020 heart. In another embodiment, set of images 2012 may be arranged in a temporal sequence. In a non-limiting example, set of images 2012 may include a series of images captured over time, allowing for an observation of dynamic cardiac functions such as beating, blood flow, and/or the like. In some cases, each ultrasonic image of set of images 2012 may include a corresponding timestamp, wherein the timestamp may include an indicator showing a date and time of when the corresponding ultrasonic image was taken. Additionally, or alternatively, and still referring to FIG.20, various imaging techniques or settings may be applied to set of images 2012 that provide specific insights into structure 2016. In some cases, structure 2016 may include a plurality of physical characteristics, spatial relationships, and function aspects of the heart’s component; for instance, and without limitation, receiving set of images 2012 may include applying a doppler imaging technique, wherein the “doppler imaging technique” is a specialized ultrasound technique used to assess the movement of blood within the body, particularly within the heart and blood vessels. Processor 2004 may configure a transducer to send high-frequency sound waves into the subject’s 2020 body, wherein the sound waves may bounce off moving blood cells and other structures. 195 Attorney Docket No.1518-103PCT1 Reflected waves may then be picked up by the transducer and frequency of the reflected waves changes (Doppler shift) depending on the speed and direction of blood flow may be analyzed to determine one or more blood flow characteristics. In some cases, one or more ultrasonic images within set of images 2012 may include visual representations translated based on one or more blood flow characteristics. Such visual representations may be further color-coded, showing the speed and direction of blood flow. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will aware other exemplary modalities of imaging such as, without limitation, computed tomography (CT) scans, magnetic resonance imaging MRI, positron emission tomography (PET) scan, angiography, electrocardiogram (ECG or EKG), single-photon emission computed tomography (SPECT), optical coherence tomography (OCT), thermography, tactile imaging, and/or the like. With continued reference to FIG.20, in one or more embodiments, receiving set of images 2012 of structure 2016 may include receiving a patient profile pertaining to subject 2020. As used in this disclosure, a “patient profile” is a comprehensive collection of information related to an individual patient. In some cases, patient profile may include a variety of different types of data that, when combined, provide a detailed picture of a patient's overall health. In an embodiment, patient profile may include demographic data of patient, for example, and without limitation, patient profile may include basic information about the patient such as name, age, gender, ethnicity, socioeconomic status, and/or the like. In another embodiment, each patient profile may also include a patient’s medical history, for example, and without limitation, patient profile may include a detailed record of the patient's past health conditions, medical procedures, hospitalizations, and illnesses such as surgeries, treatments, medications, and/or the like. In another embodiment, each patient profile may include lifestyle Information of patient, for example, and without limitation, patient profile may include details about the patient's diet, exercise habits, smoking and alcohol consumption, and other behaviors that could impact health. In a further embodiment, patient profile may include patient’s family history, for example, and without limitation, patient profile may include a record of hereditary diseases. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various type of data within patient profiles apparatus 2000 may receive and process in consistent with this disclosure. 196 Attorney Docket No.1518-103PCT1 In a non-limiting example, and still referring to FIG.20, patient profile may include one or more ultrasonic images or set of images 2012. Receiving set of images 2012 may include extracting set of images 2012 from patient profile (subsequent to patient identity verification and obtaining consent from subject 2020). In some cases, patient profile of subject 2020 may be obtained through hospital information system (HIS) or any other data acquisition platform to securely access patient’s electronic medical record (EMR) or other relevant databases. Set of images 2012 may be directly or indirectly downloaded or exported. In some cases, each ultrasonic image of set of images 2012 may be in a usable and/or computer-readable format such as, without limitation, DICOM format, and necessary metadata (e.g., patient information described above) may be included. Further, receiving set of images 2012 may include recording the access and extraction of set of images 2012; for instance, and without limitation, this process may be documented, by processor 2004, in the patient’s/subject’s 2020 medical record, databases, or other appropriate logs. Further, and still referring to FIG.20, in other embodiments, patient profile may include electrocardiogram (ECG) data, wherein the “ECG data,” for the purpose of this disclosure, refers to data related to an electrocardiogram of the patient that corresponds to the patient profile. An “electrocardiogram,” as used herein, is a medical test that records the electrical activity of subject’s heart over a period of time. In an embodiment, ECG data may include one or more recordings captured by a plurality of electrodes placed on patient’s skin. In one or more embodiments, ECG data may include information regarding a P wave, T wave, QRS complex, PR interval, ST segment, and/or the like. Processor 2004 may associate set of images 2012 with ECG data, or in other cases, receiving set of images 2012 may include receiving ECG data pertaining to subject 2020 associated with set of images 2012. Such ECG data may be collected simultaneously during ultrasonic imaging. In some cases, set of images 2012 may be linked with ECG data by one or more unique identifiers, such as without limitations, timestamps or other metadata described herein. In a non-limiting example, ECG data may be used to identify specific cardiac events or phases of the cardiac cycle, and the corresponding ultrasonic images may be analyzed to see how heart’s structure changes during those times. With continued reference to FIG.20, in other embodiments, receiving set of images 2012 may include receiving set of ultrasonic images from Data store 2024. In some cases, Data store 2024 may be implemented, without limitation, as a relational database, a key-value retrieval 197 Attorney Docket No.1518-103PCT1 database such as a NOSQL database, or any other format or structure for use as a database that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Data store 2024 may alternatively or additionally be implemented using a distributed data storage protocol and/or data structure, such as a distributed hash table or the like. Data store 2024 may include a plurality of data entries and/or records as described above. Data entries in Data store 2024 database may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in Data store 2024 or another relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a database may store, retrieve, organize, and/or reflect data and/or records as used herein, as well as categories and/or populations of data consistently with this disclosure. In a further embodiment, and still referring to FIG.20, receiving set of images 2012 may involve one or more image preprocessing steps. In some cases, processor 2004 may be configured to calibrate one or more ultrasonic images of set of images 2012 by correct for distortions and ensure accurate spatial representation of structure 2016 pertaining to subject 2020. In a non-limiting example, processor 2004 may select one or more reference objects within ultrasonic image that needs calibration to correct spatial distortions. In some cases, processor 2004 may be configured to place a phantom with pre-determine dimensions in such ultrasonic image and adjust ultrasonic image until the phantom’s dimensions are accurately represented. In another non-limiting example, one or more ultrasonic images’ brightness and contrast may be adjusted, by processor 2004 to ensure that echogenicity (reflectivity) of the tissues is accurately represented. One or more tissues with known echogenicity may be selected by processor 2004 as reference tissues to adjust corresponding portions of the one or more ultrasonic images. In other cases, standardized correction curves may be applied in order to correct the echogenicity of ultrasonic images. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, may be aware of various calibration techniques, such as, without limitation, temporal calibration, geometric calibration, among others that can be used by processor 2004 to preprocess set of images 2012. Additionally, or alternatively, and still referring to FIG.20, receiving set of images 2012 may include perform image segmentation on or more ultrasonic images of set of images 2012. In some cases, image segmentation may include separating specific structures or regions of 198 Attorney Docket No.1518-103PCT1 interest (ROI) from the background or other structures in a given ultrasonic image. In a non- limiting example, processor 2004 may be configured to use edge detection algorithms to outline the heart chambers, separating them from surrounding tissues. One or more filters may be applied to highlight the boundaries between different types of tissues during the segmentation. In another non-limiting examples, valves and vessels may also be segmented by applying thresholding techniques. Processor 2004 may be configured to set an intensity threshold based on the known echogenicity of blood and vessel walls and select pixels or regions having intensity below or above the intensity threshold from the given ultrasonic image. In some cases, one or more machine learning models may be used to perform image segmentations, for example, and without limitation, a U-net (i.e., a convolution neural network containing a contracting path as an encoder and an expansive path as a decoder, wherein the encoder and the decoder forms a U- shaped structure). With continued reference to FIG.20, processor may be configured to generate a 3D data structure representing structure 2016 as a function of set of images 2012. In a non-limiting example, 3D data structure may include a 3D voxel occupancy representation (VOR). As used in this disclosure, a “3D voxel occupancy representation (VOR)” of anatomy is a 3D digital representation of a spatial structure of the anatomy, wherein the representation is composed of a plurality of discrete volumetric elements known as voxels. A “voxel,” for the purpose of this disclosure, is a 3D equivalent of a pixel in 2D imaging. While a pixel represents a point in a 2D image and may include properties such as color and/or brightness, a voxel may represent a volume in a 3D space and may include additional properties such density/occupancy as described below. In an embodiment, each voxel of plurality of voxels within 3D VOR may represent a specific portion of structure 2016. In some cases, voxel may be a smallest distinguishable box- shaped part (i.e., 20px ^20px ^20px) of a three-dimensional image. In some cases, each voxel of plurality of voxels within VOR may be represented as a cube or rectangular prism (although other shapes may be used in specialized applications). Each voxel may include a size that determines a resolution of the 3D image or model. In an embodiment, smaller voxels may provide higher resolution; however, it may require more computational resources (e.g., RAM) for processor 2004 to process. In an embodiment, and still referring to FIG.20, each voxel of plurality of voxels within VOR may include one or more embedded values. As used herein, “embedded values” 199 Attorney Docket No.1518-103PCT1 refers to specific numerical or categorical data associated with each voxel. In some cases, embedded values may represent various attributes or characteristics of the corresponding portion of structure 2016 that voxel represents. In a non-limiting example, embedded values may include density values, intensity values, texture information, or any other quantitative measures that provide insights into the underlying tissue. Such embedded values may be derived from set of ultrasonic images or other imaging modalities used to generate data structure. In some cases, embedded values may be utilized, by processor 2004, to differentiate between different types of tissues, such as myocardial tissue, blood vessels, or chambers. Embedded values may also facilitate the visualization of dynamic cardiac functions, for example, and without limitation, blood flow or heart beating by encoding temporal information such as timestamps within plurality of voxels. Still referring to FIG.20, in an embodiment, each voxel of plurality of voxels may include a presence indicator. As used in this disclosure, a “presence indicator” refers to a data element that indicates a presence or absence (i.e., occupancy) of tissue within that portion. In some cases, and without limitation, presence indicator may include an occupancy status as one of the embedded values described herein. Portion may include a specific location within 3D space where data structure is generated; for instance, and without limitation, a coordinate in 3D space represented in a tuple such as (x, y, z). In an embodiment, 3D VOR may provide a spatial framework that allows for the modeling and visualization of structure 2016 in 3D space. In some cases, 3D data structure may include a plurality of layers or slices (either horizontal [e.g., xy plane] or vertical [e.g., xz or yz plane depends on the view direction]), wherein each layer or slices of the plurality of layers or slices is corresponding to a different cross-sectional view of a structure of subject 2020, and collectively forming a comprehensive 3D depiction of the structure. In a non-limiting example, 3D VOR having plurality of voxels with presence indicators may indicate whether each voxel in 3D space may be occupied by a part of a structure of subject 2020. A binary value such as 0 or 20 may be configured as presence indicator to show ether a pixel of 3D space is occupied (e.g., 20) or empty (e.g., 0). In should be noted that other values may be used as presence indicator such as a Boolean value e.g., TRUE or FALSE. In some cases, and still reference to FIG.20, one or more embedded values, such as, without limitations, occupancy, or density, may be derived from set of images 2012 described herein by processor 2004. In a non-limiting example, determining occupancy status of each 200 Attorney Docket No.1518-103PCT1 voxel of plurality of voxels may include converting set of ultrasonic images to a set of binary images and determining occupancy status of each voxel as a function of the structure of interest’s binary value. In some cases, occupancy status may include a value representing the likelihood of occupancy of the corresponding tissue. In another non-limiting example, density may be calculated, by processor 2004, for each voxel as a function of the echogenicity of one or more pixels on a given ultrasonic image, wherein, the brightness of the given ultrasonic image may be analyzed since different tissues reflect ultrasound waves differently. With continued reference to FIG.20, generating 3D data structure of structure 2016 may include generating a 3D array. In some cases, processor 2004 may divide 3D space into a grid of plurality of voxels, each with specific x, y, and z coordinates as embedded values. Each element of 3D array may correspond to a voxel. In some cases, 3D array may allow for easy access and manipulation of plurality of voxels, enabling various analyses, visualizations, and transformations either described or not described herein. In a non-limiting example, embedded values may include a density of the tissue at a specific location of a patient’s body derived from one or more ultrasonic images of set of images 2012. Additionally, or alternatively, and still referring to FIG.20, 3D data structure of structure 2016 may include a 3D grid configured to map presence indicators and/or other embedded values described herein of plurality of voxels (e.g., tissue density, blood flow velocity, echogenicity or acoustic properties, and any other biophysical properties). As used in this disclosure, a “3D grid” refers to a 3D data structure that divides a given volume (e.g., volume of a structure) into a plurality of discrete units called cells (i.e., volume elements). In an embodiment, each cell within 3D grid may be associated with a distinct voxel. Mapping presence indicators or other embedded values may include assigning each presence indicator or embedded value to each point within 3D grid such as corners of each corresponding cell. Such values may be derived from set of images 2012 as described above. In yet another embodiment, and still referring to FIG.20, cells may be continuous, meaning that one or more cells may represent one or more continuous regions of space rather than discreate, separate units. In a non-limiting example, instead of being uniform, mapped presence indicator and/or other embedded values may vary continuously across different cells or cell’s volume. In such embodiment, processor 2004 may use interpolation to estimate other (unknown) embedded values within a range based on existing values such as known embedded 201 Attorney Docket No.1518-103PCT1 values at specific points, thereby allowing for smooth transitions between cells. Exemplary interpolation methods may include, without limitation, linear interpolation, cubic interpolation, and/or the like. For example, and without limitation, if the corners of a cell have known values interpolation can be used to estimate the values at any point within the cell based on those corner values. In a non-limiting example, and still referring to FIG.20, 3D data structure of structure 2016 may include a 3D grid having a plurality of cells e.g., voxels, wherein each cell may contain a continuous range of values representing tissue density, blood flow velocity, or other properties (i.e., embedded values). Processor 2004 may be configured to apply trilinear or tricubic interpolation to estimate tissue density within each cell based on presence indicator or other known values at the cell’s boundaries, since tissue densities change gradually; Such 3D grid may provide a smooth, continuous representation of heat’s internal structures, allowing for more nuanced analysis and visualization as described below. In a further embodiment, 3D grid with continuous cells may be additionally used in fluid dynamics simulations. With continued reference to FIG.20, in some case, presence indicators and/or other embedded values may be mapped to 3D grid as a function of array masking, wherein specific array or grid may be selected to modify based on one or more pre-defined criteria. In a non- limiting example, processor 2004 may generate a mask e.g., a binary array that defines which voxels or cells are affected. Mask may be used to select or modify specific voxels or cells based on certain attributes; for instance, and without limitation, processor 2004 may use mask to isolate the LA within the heart focusing the analysis on that specific region. Such mask may include criteria defined by specific density thresholds that distinguish the LA’s tissue (i.e., voxels representing LA in 3D grid) from surrounding structures (i.e., neighboring voxels). In some cases, such mask may further include a binary mask, wherein each voxel in the 3D grid may be assigned a first presence indicator such as 20 if the voxel meets the criteria for the LA and a second presence indicator such as 0 if it does not. In some embodiments, mask may be directly applied to 3D grid, selecting, or modifying voxels or cells, thereby enabling processor 2004 to highlight, exclude, or otherwise manipulate specific parts of structure 2016 within 3D grid. Processor 2004 may then perform an element-wise multiplication between 3D grid and the mask. Continuing from the previous non-limiting example, voxels corresponding to the LA (wherein 202 Attorney Docket No.1518-103PCT1 the mask value is 20) may retain their original values, while other voxels (where the mask value is 0) may be set to 0 or other specific value (i.e., excluded or masked out). With continued reference to FIG.20, in some embodiments, 3D grid may include one or more spatial features extracted from set of images 2012 of structure 2016. As used in this disclosure, “spatial features” are specific characteristics or attributes related to the spatial arrangement, shape, size, texture, or orientation of structures within a 3D space. In some cases, spatial features may include one or more embedded values described herein and their combinations thereof. In a non-limiting example, spatial feature may be represented numerically as a vector, a metric or other mathematical constructs that capture specific spatial characteristics. In some cases, spatial features may also be visualized as contours, surfaces, or other geometric representations. In an embodiment, spatial features may be extracted using edge detection, texture analysis, or other image processing techniques (e.g., cleaning and enhancing images, image segmentation, and/or the like). In another embodiment, one or more machine learning models, such as convolutional neural networks (CNNs) as described in further detail below, may be used to extract complex spatial features. Still referring to FIG.20, as used in this disclosure, a “vector” is a data structure that represents one or more a quantitative values and/or measures of one or more spatial features. A vector may be represented as an n-tuple of values, where n is one or more values, as described in further detail below; a vector may alternatively or additionally be represented as an element of a vector space, defined as a set of mathematical objects that can be added together under an operation of addition following properties of associativity, commutativity, existence of an identity element, and existence of an inverse element for each vector, and can be multiplied by scalar values under an operation of scalar multiplication compatible with field multiplication, and that has an identity element is distributive with respect to vector addition, and is distributive with respect to field addition. Each value of n-tuple of values may represent a measurement or other quantitative value associated with a given category of data, or attribute, examples of which are provided in further detail below; a vector may be represented, without limitation, in n- dimensional space using an axis per category of value represented in n-tuple of values, such that a vector has a geometric direction characterizing the relative quantities of attributes in the n-tuple as compared to each other. Two vectors may be considered equivalent where their directions, and/or the relative quantities of values within each vector as compared to each other, are the 203 Attorney Docket No.1518-103PCT1 same; thus, as a non-limiting example, a vector represented as [5, 200, 205] may be treated as equivalent, for purposes of this disclosure, as a vector represented as [20, 2, 3]. Vectors may be more similar where their directions are more similar, and more different where their directions are more divergent, for instance as measured using cosine similarity as computed using a dot product of two vectors; however, vector similarity may alternatively or additionally be determined using averages of similarities between like attributes, or any other measure of similarity suitable for any n-tuple of values, or aggregation of numerical similarity measures for the purposes of loss functions as described in further detail below. Any vectors as described herein may be scaled, such that each vector represents each attribute along an equivalent scale of values. Each vector may be “normalized,” or divided by a “length” attribute, such as a length attribute l as derived using a Pythagorean norm: ^^ ൌ _^∑^{^} ^_ୀ^ ^^_^ ^ଶ , where a_i is attribute number i of the vector. Scaling and/or normalization may vector comparison independent of

absolute quantities of attributes, while preserving any on similarity of attributes. Still referring to FIG.20, in a non-limiting example, one or more spatial features may include one or more shape features (i.e., characteristics related to the shape of specific structures), such as curvature, surface area, volume, and/or the like. In another non-limiting example, one or more spatial features may include one or more texture features (i.e., characteristics related to the texture or pattern within tissues, as seen in set of images 2012), such as gray-level co-occurrence matrix (GLCM) features representing the texture of heart muscle tissue. In another non-limiting example, one or more spatial features may include one or more orientation features (i.e., characteristics related to the orientation or alignment of structures), such as the angle or alignment of the septum within the heart. In a further non-limiting example, one or more spatial features may include one or more edge and boundary features (i.e., Characteristics related to the edges or boundaries between different structures), such as edge detection features highlighting the boundary between the myocardium and the cardiac chambers. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various spatial features extracted from set of images 2012 in consistent with this disclosure. With continued reference to FIG.20, in some embodiments, apparatus 2000 may include a computer vision model 2028 configured to generate 3D data structure of structure 2016 by implementing image segmentation methods as described further below. A “computer vision 204 Attorney Docket No.1518-103PCT1 model,” for the purpose of this disclosure, is a computation model designed to interpret and make determinations based on visual data. In an embodiment, computer vision model 2028 may process set of images 2012, to make a determination about a scene, space, and/or object in structure 2016. In a non-limiting example, computer vision model 2028 may be used for registration of plurality of voxels within a 3D space. In some cases, registration may include image processing described herein, such as without limitation object recognition, feature detection, edge/corner detection, and the like. Non-limiting example of feature detection may include scale invariant feature transform (SIFT), Canny edge detection, Shi Tomasi corner detection, and the like. In some cases, registration may include one or more transformations to orient an ultrasonic image relative to a 3D coordinate system; exemplary transformations include without limitation homography transforms and affine transforms. In an embodiment, registration of ultrasonic image to a coordinate system may be verified and/or corrected using object identification and/or computer vision, as described above. For instance, and without limitation, an initial registration to two dimensions, represented for instance as registration to the x and y coordinates, may be performed using a two-dimensional projection of points in three dimensions onto the ultrasonic image; however, a third dimension of registration, representing depth and/or a z axis, may be detected by utilizing depth-sensing techniques such as Doppler imaging. Alternatively, the third dimension may be inferred from the known geometry and orientation of the imaging device (e.g., ICE catheter), or through the application of one or more machine learning models trained to interpret depth from the two-dimensional projection. With continued reference to FIG.20, processor 2004 may use a machine learning module 2032 to implement one or more algorithms or generate one or more machine learning models, such as a structure modeling model to generate data structure of structure 2016. However, the machine learning module is exemplary and may not be necessary to generate one or more machine learning models and perform any machine learning described herein. In one or more embodiments, one or more machine-learning models may be generated using training data. Training data may include inputs and corresponding predetermined outputs so that a machine- learning model may use correlations between the provided exemplary inputs and outputs to develop an algorithm and/or relationship that then allows machine-learning model to determine its own outputs for inputs. Training data may contain correlations that a machine-learning process may use to model relationships between two or more categories of data elements. 205 Attorney Docket No.1518-103PCT1 Exemplary inputs and outputs may come from a database, such as any database described in this disclosure, or be provided by a user. In other embodiments, a machine-learning module may obtain a training set by querying a communicatively connected database that includes past inputs and outputs. Training data may include inputs from various types of databases, resources, and/or user inputs and outputs correlated to each of those inputs so that a machine-learning model may determine an output. Correlations may indicate causative and/or predictive links between data, which may be modeled as relationships, such as mathematical relationships, by machine-learning models, as described in further detail below. In one or more embodiments, training data may be formatted and/or organized by categories of data elements by, for example, associating data elements with one or more descriptors corresponding to categories of data elements. As a non- limiting example, training data may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data may be linked to descriptors of categories by tags, tokens, or other data elements. In a further embodiment, training data may include previous outputs such that one or more machine learning models iteratively produces outputs. Still referring to FIG.20, machine learning module 2032 may be used to generate structure modeling model and/or any other machine learning models, such as, shape identification model as described in further detail below, using training data. Structure modeling model may be trained by correlated inputs and outputs of training data. Training data may be data sets that have already been converted from raw data whether manually, by machine, or any other method. In an embodiment, generating data structure of structure 2016 includes receiving structure training data, wherein the structure training data may include a plurality of image sets as input and a plurality of computed tomography (CT) based 3D models as output, and wherein each image set of plurality of image sets may include any images described in this disclosure. In some cases, structure training data may be received from Data store 2024 or other databases. In other cases, structure training data may be collected by a data acquisition unit from external sources such as one or more medical equipment’s e.g., imaging devices or diagnostic tools, wherein the data acquisition may be configured as an intermediary between the data source and machine learning module 2032. 206 Attorney Docket No.1518-103PCT1 Still referring to FIG.20, in some embodiments, a training dataset may be identified by correlating an instance of computed tomography scan data with a historical ultrasonic image as a function of a medical record and a language model. For example, a language model may be used to interpret a medical record and/or determine whether an instance of computed tomography scan data should be associated with a historical ultrasonic image in a training dataset. For example, a language model may be used to interpret language of a medical record, and the output of the language model may be used to identify whether a medical event has taken place between when the historical ultrasonic image was taken and when the historical computed tomography scan data was recorded, such that they are not to be associated in a training dataset. In another example, a language model may be used to interpret language of a medical record, and the output of the language model may be used to identify whether historical ultrasonic image and historical computed tomography scan data were recorded in a sufficiently short time, such that they are associated in a training dataset. In some embodiments, a training dataset may be identified by generating a synthetic ultrasonic image as a function of an instance of computed tomography scan data. Still referring to FIG, 20, as used in this disclosure, a “computed tomography (CT) based 3D model” refers to a 3D representation of a structure that is created using data from CT scans. In some embodiments, a computed tomography (CT) based 3D model may include a 3D representation of a structure and surrounding structures that is created using data from CT scans. Computed Tomography is a medical imaging technique that uses X-rays to capture cross- sectional images (slices) of the body. By taking a plurality of slices, a CT scan creates a detailed 3D representation of the internal structure. In an embodiment, CT-based 3D model may include 3D representations of a structure such as the heart including chambers, valves, blood vessels, and surrounding tissues. In some cases, CT-based 3D model may be interactive; for instance, medical professionals may rotate, zoom, and/or explore CT-based 3D model from various angles. In some cases, plurality of CT-based 3D models may be generated prior to the training of the structure modeling model. Plurality of CT-based 3D models may be generated using existing techniques in the field as described above such as, without limitation, FAM, cardiac CT merging, among others. In a non-limiting example, plurality of CT-based 3D models may provide ground through or references models against structure modeling model that is being trained. In a non- limiting example, generating data structure of structure 2016 further includes training structure 207 Attorney Docket No.1518-103PCT1 modeling model using structure training data described herein. Structure modeling model trained using structure training data may be able to interpret ultrasonic images by learning relationships between ultrasonic images and corresponding CT-based 3D models. Processor 2004 is further configured to generate data structure of structure 2016 as a function of set of images 2012 using trained structure modeling model. In some cases, data structure e.g., 3D model 2056 as described below may be interpreted, visualized, and analyzed by processor 2004 in similar manner to CT- based 3D models, wherein both are 3D structures that correspond to ultrasonic images. With continued reference to FIG.20, in an embodiment, structure modeling model includes a deep neural network (DNN). As used in this disclosure, a “deep neural network” is defined as a neural network with two or more hidden layers. Neural network is described in further detail below with reference to FIGS.4-5. In a non-limiting example, structure modeling model may include a convolutional neural network (CNN). Generating 3D data structure of structure 2016 may include training CNN using structure training data and generating 3D data structure as a function of set of images 2012 using trained CNN. A “convolutional neural network,” for the purpose of this disclosure, is a neural network in which at least one hidden layer is a convolutional layer that convolves inputs to that layer with a subset of inputs known as a “kernel,” along with one or more additional layers such as pooling layers, fully connected layers, and the like. In some cases, CNN may include, without limitation, a deep neural network (DNN) extension. Mathematical (or convolution) operations performed in the convolutional layer may include convolution of two or more functions, where the kernel may be applied to input data e.g., set of images 2012 through a sliding window approach. In some cases, convolution operations may enable processor 2004 to detect local/global patterns, edges, textures, and any other spatial features described herein within each ultrasonic image of set of images 2012. Spatial features may be passed through one or more activation functions, such as without limitation, Rectified Linear Unit (ReLU), to introduce non-linearities into the processing step of generating 3D data structure of structure 2016. Additionally, or alternatively, CNN may also include one or more pooling layers, wherein each pooling layer is configured to reduce the dimensionality of input data while preserving essential features within the input data. In a non- limiting example, CNN may include one or more pooling layer configured to reduce the spatial dimensions of spatial feature maps by applying downsampling, such as max-pooling or average pooling, to small, non-overlapping regions of one or more spatial features. 208 Attorney Docket No.1518-103PCT1 Still referring to FIG.20, CNN may further include one or more fully connected layers configured to combine spatial features extracted by the convolutional and pooling layers as described above. In some cases, one or more fully connected layers may allow for higher-level pattern recognition. In a non-limiting example, one or more fully connected layers may connect every neuron (i.e., node) in its input to every neuron in its output, functioning as a traditional feedforward neural network layer. In some cases, one or more fully connected layers may be used at the end of CNN to perform high-level reasoning and produce the final output such as, without limitation, a 3D data structure of structure 2016. Further, each fully connected layer may be followed by one or more dropout layers configured to prevent overfitting, and one or more normalization layers to stabilize the learning process described herein. With continued reference to FIG.20, CNN may further include a 3D CNN, wherein the 3D CNN, unlike standard 2D CNN, may include utilization of one or more 3D convolutions which allow them to directly process 3D data, thereby enabling processor 2004 to generate 3D structures such as 3D data structure of structure 2016 using the 3D CNN. In a non-limiting example, 3D CNN may include one or more 3D filters (i.e., kernels) that move through the set of images 2012 in three dimensions and capturing spatial relationships in x, y, and z axis. Similar to 3D convolutions, 3D CNN may further include one or more 3D pooling layers that may be used to reduce the dimensionality of ultrasonic images while preserving spatial features as described above. Additionally, or alternatively, an encoder-decoder structure may be implemented (extended to 3D), by processor 2004, in 3D CNN, wherein the encoder-decoder structure includes an encoding path that captures the context and a decoding path that enables precise localization in a same manner as U-net as described above. Such encoder-decoder structures may also include a plurality of skip connections, allowing 3D CNN to use information from multiple resolutions to improve the process of generating 3D data structure of structure 2016. With continued reference to FIG.20, in an embodiment, training the structure modeling model (i.e., CNN) may include selecting a suitable loss function to guide the training process. In a non-limiting example, a loss function that measures the difference between the predicted 3D VORs and the ground truth 3D structure e.g., CT-based 3D models may be used, such as, without limitation, mean squared error (MSE) or a custom loss function may be designed for one or more embodiments described herein. Additionally, or alternatively, optimization algorithms, such as stochastic gradient descent (SGD), may then be used to adjust 209 Attorney Docket No.1518-103PCT1 the structure modeling model’s parameters to minimize such loss. In a further non-limiting embodiment, instead of directly predicting 3D data structure, structure modeling model may be trained as a regression model to predict presence indicators and/or other embedded values described herein for each voxel of plurality of voxels within a 3D grid. Additionally, CNN may be extended with additional deep learning techniques, such as recurrent neural networks (RNNs) or attention mechanism, to capture additional features and/or data relationships within input data. These extensions may further enhance the accuracy and robustness of the 3D modeling. With continued reference to FIG.20, processor 2004 is configured to generate a set of shape parameters 2036 based on set of images 2012. As used in this disclosure, a “set of shape parameters” refers to a collection of numerical values or descriptors that quantitatively represent the geometric or morphological characteristics of a structure. In some embodiments, a set of shape parameters may represent a shape of a structure. In a non-limiting example, set of shape parameters 2036 may include information and/or metadata calculated, determined, and/or extracted from set of ultrasonic images, such as, dimensions, angles, curvatures, surface areas, texture, symmetry, and/or the like. In other embodiments, processor 2004 may be configured to parameterize features (e.g., edges, textures, contours, and any other characteristics that describe the shape structure 2016) extracted from set of images 2012 using CNN described herein. Such parameterization may involve processor 2004 to derive one or more shape parameters including one or more morphological descriptors that quantitatively describe structure 2016 based on extracted features. In some cases, processor 2004 may be configured to use principal component analysis (PCA) to reduce the dimensionality of set of shape parameters 2036, allowing processor 2004 to focusing on the most informative shape parameters of set of shape parameters 2036 in further processing steps described below. With continued reference to FIG.20, in a non-limiting example, set of shape parameters 2036 may be generated based on set of images 2012 using machine learning model such as, without limitation, a shape identification model 2040. Generating set of shape parameters 2036 may include receiving structure training data 2048, wherein the structure training data 2048 may include a plurality of image sets as inputs correlated to a plurality of shape parameter sets as outputs. In some cases, structure training data may be received from Data store 2024. For example, and without limitation, structure training data 2048 may be used to show each ultrasonic image may indicate a particular set of shape parameters. In some 210 Attorney Docket No.1518-103PCT1 embodiments, structure training data may include historical ultrasonic images correlated with historical computed tomography scan data. Such a training dataset may be used to train shape identification model to generate a set of shape parameters representing a structure’s shape as a function of a set of ultrasonic images, which may be input into the model in order to receive, as an output, a set of shape parameters. Shape identification model 2040 may be trained, by processor 2004, using structure training data 2048. Additionally, structure training data 2048 may include previously input image sets and their corresponding shape parameter outputs. Shape identification model 2040 may be iterative such that outputs may be used as future inputs of shape identification model 2040. This may allow the shape identification model 2040 to evolve. Processor 2004 may be further configured to generate set of shape parameters 2036 as a function of set of images 2012 using the trained shape identification model 2040. Still referring to FIG, 20, generating set of shape parameters 2036 may include performing image processing/segmentation techniques, as described above, prior to implementation of shape identification model 2040 in order to optimize performance and runtime of processor 2004 and training of model. For example, image segmentation may include normalization and standardization methods performed by computer vision model 2028 to ensure that pixel values in images 2012 are normalized or standardized to a consistent scale thus aiding convergence during training of shape identification model 2040. Image segmentation may include data augmentation techniques such as rotation, scaling, flipping, and translation to artificially increase the size of the training dataset and improve model generalization. Image segmentation may include image enhancement preprocessing techniques like histogram equalization or contrast stretching to enhance relevant features in the images. Image segmentation may include texture and shape descriptors to extract features beyond pixel values, such as texture and shape descriptors, to capture additional information about structures. Image segmentation may include architecture selection methods, as in experiments with different architectures, such as U-Net, DeepLab, or custom architectures, depending on the complexity and characteristics of the images. Image segmentation may include grid Search or random Search processing methods to systematically explore hyperparameter combinations to find the optimal configuration for a 3D model. As previously disclosed, image segmentation may include separating specific structures or regions of interest 2044 (ROI) from the background or other 211 Attorney Docket No.1518-103PCT1 structures in a given ultrasonic image, wherein a collection of ROIs 2044 may be also incorporated by the shape parameter training data / structure training data 2048. With continued reference to FIG.20, processor 2004 may use a statistical shape model to generate and/or iteratively refine a 3D model 2056 based on a set of shape parameters. As used herein, a “3D model,” is a 3D representation of a structure. In some embodiments, a 3D model may include a heart model. A heart model may include a 3D representation of cardiac anatomy. In some cases, 3D model 2056 may be generated through a direct 3D reconstruction from a series of (2D) ultrasonic images. In a non-limiting example, set of images 2012 may include a plurality of ultrasonic images captured from different angles and positions within and/or around a structure. Processor 2004 may be configured to apply one or more 3D reconstruction algorithms, such as without limitation, marching cubes, contour detection and segmentation, active contour models, and/or the like to create a coherent 3D representation e.g., 3D model 2056 of structure 2016. In some cases, such direct 3D reconstruction may leverage the inherent spatial information within set of images 2012, providing a direct and intuitive way to model the 3D model 2056 of a structure. In a further embodiment, generic 3D modeling techniques may be applied to create the initial 3D model. In some cases, generic 3D modeling techniques may include surface modeling, solid modeling, or parametric modeling, among others. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various 3D reconstruction algorithms that may be used by processor 2004 to generate 3D model 2056 of structure 2016. As used in this disclosure, a “statistical shape model” (SSM) is a data structure representing, including, and/or utilizing a mathematical model that captures principal modes of variation in shape across a population of structures. In some cases, SSM may be constructed by analyzing one or more datasets of shapes and identifying, for example, mean shapes and main modes of variation within the one or more datasets. In a non- limiting example, SSM may start with calculation of at least one mean shape, which represents an average geometry of all shapes of a structure in a given dataset, wherein the at least one mean shape may be served as a central reference point for processor 2004 to understand different variations. In some embodiments, unique SSMs are created for different structure categories, such as different organs or tissues. In a non-limiting example, a first SSM may be created for a first structure category such as kidneys and a second SSM may be created for a second structure category such as hearts. In some cases, dataset may include, without limitation, structure training 212 Attorney Docket No.1518-103PCT1 data, structure training data 2048, and/or any datasets within ultrasonic image databases described herein. SSM may also identify one or more principal modes of variation within given datasets described herein, wherein the “principal modes of variations,” for the purpose of this disclosure, refer to main patterns or directions along which data points vary within dataset. In a non-limiting example, identifying principal modes of variations may include applying principal component analysis (PCA) on given dataset. Additionally, or alternatively, shapes may be described directly using plurality of shape parameter sets (in structure training data 2048). In some cases, shape parameter sets may correspond to a plurality of modes of variations. Further, one or more statistical constraints (e.g., mean, variance, correlation, boundary, proportion constraint and/or the like) may be introduced into SSM 2052 based on the distribution of shape parameters within plurality of shape parameter sets and/or 3D structure dimensions. In some embodiments, each shape parameter within a set of shape parameters may be associated with and/or include a corresponding parameter range. Such a parameter range may, for example, include a range of values associated with a normal and/or healthy structure. Such a parameter range may be determined based on, for example, a subset of possible values of a parameter which historical healthy structures commonly fall into, as determined from a dataset. With continued reference to FIG.20, in some cases, once modes of variation are extracted, processor 2004 may be configured to create a shape representation for any given structure shape within the studied class. In a non-limiting example, 3D model 2056 having a _{shape ^^ may be mathematically represented as ^^ ൌ ^^} ^̅ _^ ^∑ெ ^_{ୀ^ ^^^ ൈ ^^ ^, wherein ^^} ^̅ _{denotes the} mean shape derived from the set of example

of modes of variation considered, ^^_^ are the coefficients or weights for each mode, and ^^_^ are the modes of variation (eigenvectors corresponding to the ^^th principal component). In some cases, coefficients ^^_^ may dictate a degree to which each mode of variation is present in shape ^^. In some cases, coefficients ^^_^ may vary from positive to negative (or negative to positive) based on the deformation of the 3D model 2056 in directions described by each mode of variation. In some cases, 3D model 2056 may include mean shape as described herein. In some cases, 3D model 2056 may include a predictive structure shape that may not have been explicitly seen in the set of example shapes or patient’s heart observations. In some cases, 3D model 2056 may be in 3D VOR as described above. 213 Attorney Docket No.1518-103PCT1 Still referring to FIG.20, generating the 3D model 2056 may include transforming 3D model 2056 to a second 3D model as a function of a plurality of mode changers within SSM 2052, wherein each mode changer of the plurality of mode changers is associated with a model feature of 3D model 2056. As used in this disclosure, a “mode changer” is an algorithmic component derived from PCA configured to encapsulate a specific mode of variation as described above (representing a distinct way in which the shape of 3D model 2056 may deviate from the mean shape). A “model feature,” for the purpose of this disclosure, is a distinct, recognizable and quantifiable attribute or characteristic of the 3D model 2056. For example, and without limitation, model feature may include an anatomical feature such as the size and curvature of the ventricles, the thickness of the heart wall, the positioning of heart valves or the like. In some cases, model feature may correspond to at least one shape parameter as described herein. In a non-limiting example, a mode changer may be associated with the size variation of the left ventricle identified within 3D model 2056. Such mode changer may be adjusted to modify the volume of the left ventricle, resulting in a second 3D model that mimics potential biological variations or specific patient conditions that is different from original 3D model 2056. In some cases, multiple mode changers of SSM 2052 may be adjusted simultaneously. For example, and without limitation, the rigid registration might involve translations and rotations to superimpose the shapes; affine registration could incorporate scaling, shearing, and other linear transformations; while non-rigid methods might employ B-splines, thin-plate splines, or diffeomorphic transformations to flexibly map one shape onto another. In some cases, an averaged position of each corresponding point (or voxel) across all example shapes may be calculated using formula ^̅^_^ ൌ ^{^} ே∑^ே ^_ୀ^ ^^_^^ , where ^̅^_^ is the mean position of the ^^th point (or voxel), ^^_^^ is the position of

in the ^^th example shape, and N is the total number of example shapes in the labeled set. In some cases, principle component analysis (PCA) may be applied to the aligned shapes to extract at least a primary mode of variation. As described herein, a “primary mode of variation” is a mode of variation that have the most significant variability, wherein the “mode of variation,” for the purpose of this disclosure, is a specific pattern or direction of a shape change. In some cases, such significancy may be indicated by the first principal component in PCA. In some cases, a plurality of modes of variation may be extracted, wherein each mode (or principal component) may represent a specific way the shape of structure may be deformed from the mean shape, determined by one or more eigenvectors of the 214 Attorney Docket No.1518-103PCT1 covariance matrix of the aligned shapes. In a non-limiting example, eigenvector with the highest eigenvalue may represent primary mode of variation which captures the largest amount of shape variability within example shapes, while subsequent modes (eigenvectors) capture decreasing amounts of variability. In a non-limiting example, a feature and/or component of apparatus 2000, such as SSM 2052, may be consistent with any feature and/or component, such as an SSM, disclosed in this disclosure Still referring to FIG.20, additionally, processor 2004 may use user feedback to train the machine-learning models described above. For example, structure modeling model and/or shape identification model 2040 may be trained using past inputs and outputs of structure modeling model and/or shape identification model 2040. In some embodiments, if user feedback indicates that a subsequent 3D model outputted by SSM 2052 was “bad,” then that output and the corresponding input e.g., set of ultrasonic images, corresponding CT-based 3D model may be removed from training data used to train structure modeling model and/or shape identification model 2040, and/or may be replaced with a value entered by, e.g., another user that represents an ideal 3D model of the structure given the input the machine learning models originally received, permitting use in retraining, and adding to training data as described above; in either case, machine learning models described herein may be retrained with modified training data. In some embodiments, training data such as structure training data and/or structure training data 2048 may include user feedback. Further, apparatus 2000 may be configured to validate one or more machine learning models described herein against real-world data, identifying areas where machine learning models may be underperforming or misaligned with clinical needs. Such feedback may also be used to guide model training, ensuring that machine learning models are not only accurate but also clinically meaningful and aligned with healthcare or medical professional’s needs and priorities. Stil referring to FIG.20, generating 3D model 2056 includes determining a level of uncertainty 2060 at least at one location of a plurality of locations of the 3D model 2056 based on the set of shape parameters 2036. A location may refer to each voxel of plurality of voxels, cells, geometric marker, and all other identifying markers/data points of a model as described throughout this disclosure. A plurality of locations may refer to the surface of 3D model 2056, such as a set of pixels or a region on a model. “Uncertainty,” as used herein, refers to the lack of confidence or precision in a model's predictions. In some cases, the level of uncertainty 2060 215 Attorney Docket No.1518-103PCT1 may be derived from variability within the distribution of shape parameters, image quality assessment, measurement errors and/or the like. In a non-limiting example, greater changes in structure geometry (indicated by the plurality of shape parameters) may correspond to a greater level of uncertainty at that location. This may be used to inform clinical decisions, for example, areas of high uncertainty may be avoided when planning a pathway for surgical intervention or additional imaging may be requested to reduce uncertainty in critical areas. Still referring to FIG.20, levels of uncertainty may refer to categories of uncertainty such as epistemic uncertainty, aleatoric uncertainty, model parameter uncertainty, pixel-wise uncertainty, boundary uncertainty, uncertainty in time series data, predictive uncertainty, systematic uncertainty, model output uncertainty, and the like. Epistemic uncertainty arises from a lack of knowledge or information. For example, limited training data for certain cardiac pathologies may contribute to higher epistemic uncertainty. Aleatoric uncertainty, also known as data uncertainty, results from inherent randomness or variability in the data. For example, variability in cardiac anatomy among different patients or imaging modalities introduces aleatoric uncertainty. Model Parameter Uncertainty is uncertainty associated with the model parameters, indicating how well the model has learned the underlying patterns in the training data. For example, variations in model parameters due to the stochastic nature of the optimization process contribute to parameter uncertainty. Pixel-wise Uncertainty is associated with individual pixels in the image. It provides a confidence measure for each pixel in the segmentation mask. For example, certain regions of a structure may be more challenging to segment accurately, leading to higher pixel-wise uncertainty. Boundary Uncertainty is related to the boundaries between different structures or regions in the image. For example, the precise delineation of the endocardium or epicardium may be uncertain in regions where the boundaries are not well-defined. Regarding uncertainty in Time Series Data, in tasks involving sequential data, such as cardiac imaging over time, uncertainty can be related to variations in the temporal dimension. For example, segmentation of dynamic structures like the beating heart involves handling uncertainty associated with different phases of the cardiac cycle. Predictive Uncertainty is uncertainty in the model's predictions for unseen data points. For example, when the model encounters a novel pathology or an atypical structure, predictive uncertainty measures its confidence in providing accurate segmentation. Systematic Uncertainty is uncertainty stemming from systematic errors or biases in the data collection process or the model architecture. For 216 Attorney Docket No.1518-103PCT1 example, if the training data is biased towards a specific demographic, the model may exhibit uncertainty when applied to a more diverse patient population. Model Output Uncertainty is uncertainty associated with the actual output of the model, indicating how confident the model is in its segmentation predictions. For example, the model may output a segmentation mask with a probability or confidence score for each pixel, reflecting the uncertainty associated with that pixel's classification. Still referring to FIG.20, a level of uncertainty 2060 may include a degree, statistical measure, percentage, or variable whether linguistic or numerical, and the like identifying a range of uncertainty. For example, processor 2004 may generate probability scores/confidence scores for locations of a model, indicating the model's confidence in its predictions. Calibration plots can be used to assess how well these confidence scores align with the true accuracy. Processor 2004 may perform a threshold analysis to investigate how varying decision thresholds for classification or segmentation affects the trade-off between sensitivity and specificity in uncertain regions. Threshold analysis may include task-specific metrics for clinical relevance. For example, in cardiac image segmentation, critical regions like the myocardium may have stricter uncertainty thresholds compared to less critical regions. Processor 2004 may implement Bayesian Neural Networks (BNNs) to perform posterior predictive checks to evaluate the agreement between the model's predictions and the observed data, such as data store 2024, considering the uncertainty represented by the posterior distribution in Bayesian frameworks. In various embodiments, a level of uncertainty 2060 may be metrics determined by processor 2004, such as Pixel-wise Uncertainty Metrics, Boundary Displacement Error (BDE), Uncertainty- Aware Loss Functions, Calibration Metrics, and the like. Still Referring to FIG.20, processor 2004 is configured to generate a map 2064 regarding one or more levels of uncertainty. A “map,” as used herein, refers to a visualization. Map 2064 may be level(s) of uncertainty to be visualized on the 3D model 2056. Map 2064 may include a color-coded heatmap, including other visual cues, symbols or indicators that alert a user to areas of 3D model 2056 that may require extra caution when used for planning or guidance during a medical procedure. For example, after obtaining the segmentation results from 3D model 2056, map 2064 may be generated. Map 2064 may highlight the uncertainty or confidence level associated with each pixel in the segmentation. Assigning colors to different intensity levels in map 2064 allows for an intuitive visualization. Typically, warmer colors (e.g., 217 Attorney Docket No.1518-103PCT1 red, or yellow) might represent high uncertainty, while cooler colors (e.g., blue, or green) could indicate low uncertainty. The color-coding can be adjusted based on specific thresholds or clinical requirements. Still referring to FIG.20, generating map 2064 may include methods such as Class Activation Mapping (CAM). Class Activation Mapping is a technique that originated for image classification tasks and has been extended to provide visual insights into the regions of an image that are most important for a particular class. CAM allows the visualization of the spatial attention of a convolutional neural network (CNN) by generating heat maps that highlight discriminative regions. CAM may be applied to the last convolutional layer of a CNN. The features extracted by this layer capture high-level semantic information, making it suitable for visualizing the importance of different regions in an image. CAM is typically applied to the last convolutional layer of a CNN. The features extracted by this layer capture high-level semantic information, making it suitable for visualizing the importance of different regions in an image. The output of the global average pooling is then fed into a fully connected layer with a softmax activation function. This converts the features into class scores, indicating the likelihood of the image belonging to different classes. The CAM algorithm computes a weighted sum of the original feature maps based on the weights of the fully connected layer. These weights are determined during the training process and represent the importance of each feature map for a specific class. The weighted sum is applied to the original feature maps, producing a single heat map. This heat map highlights the regions of the input image that contributed most to the prediction for the target class. The generated heat map can be overlaid on the input image, visually indicating which regions are most relevant for the predicted class. Typically, warmer colors (e.g., red, or yellow) represent higher activation or importance. Still Referring to FIG.20, generating map 2064 may include Grad-CAM (Gradient- weighted Class Activation Mapping). Grad-CAM is an extension of Class Activation Mapping (CAM) that enhances the localization capabilities by incorporating gradient information from the final convolutional layer of a neural network. Grad-CAM helps to generate heat maps that highlight discriminative regions in an image, providing more fine-grained insights into where a convolutional neural network (CNN) is focusing its attention when making predictions. In traditional CAM, the last convolutional layer's feature maps are linearly combined to obtain a weighted sum, and the resulting weights are used to create a heat map that highlights relevant 218 Attorney Docket No.1518-103PCT1 regions for a specific class. Grad-CAM improves upon CAM by introducing gradient information. It computes the gradients of the predicted class score with respect to the feature maps of the last convolutional layer. Grad-CAM retains the global average pooling (GAP) operation applied after the last convolutional layer, as it is an integral part of CAM. The GAP operation condenses the spatial information into a single value per feature map. The gradients obtained in the previous step are used to calculate the importance of each feature map. These gradients represent the importance of each feature map in contributing to the final prediction. A weighted sum is computed using these gradients, and this is combined with the original feature maps. The computed sum goes through a ReLU activation function, discarding any negative values. This step emphasizes positive contributions and suppresses negative ones. The ReLU- activated weighted sum is linearly combined with the original feature maps to produce a weighted combination. This combination retains spatial information and helps create a more accurate heat map. The resulting heat map is often normalized to enhance visualization, ensuring that the values are within a specific range (e.g., between 0 and 20). The final heat map generated by Grad-CAM is then overlaid on the input image, highlighting the regions of interest for the predicted class. The intensity of the heat map indicates the importance of different regions. Grad- CAM enhances the interpretability and explainability of deep learning models, allowing practitioners and researchers to understand which parts of an image are crucial for a particular prediction. This is particularly valuable in applications such as medical imaging or any domain where understanding the decision-making process is critical. Still Referring to FIG.20, generating map 2064 may include utilizing a “SmoothGrad technique,” a technique designed to improve the interpretability of neural network predictions by reducing the noise in the attribution maps or heat maps generated by visualizing gradients. It is particularly useful for understanding the decision-making process of deep learning models, especially in scenarios where the explanations need to be robust and less sensitive to input perturbations. The primary goal of SmoothGrad is to enhance the visual quality of attribution maps generated by visualizing gradients. Attribution maps highlight the regions in the input that contribute most to a model's prediction. SmoothGrad aims to reduce the impact of noise in these maps, providing more stable and interpretable visualizations. The key idea behind SmoothGrad is to introduce perturbations to the input data. Instead of attributing the prediction solely to the gradients calculated with respect to the original input, the gradients are averaged over multiple 219 Attorney Docket No.1518-103PCT1 perturbed versions of the input. By averaging the gradients over multiple perturbed samples, SmoothGrad helps reduce the impact of noise or irrelevant features in the attribution maps. This is particularly beneficial when dealing with complex or noisy datasets. Perturbation techniques include adding Gaussian noise, random rotations, or random translations to the input data. These perturbations create variations in the input while preserving the essential features, leading to more stable and reliable attribution maps. For each perturbed input, gradients are calculated with respect to the model's output. These gradients are then averaged over all perturbed samples. This process smoothens the attribution map by reducing the influence of random noise. The averaged gradients may undergo normalization or scaling to ensure that the values are interpretable and within a specific range. This step can enhance the consistency and comparability of the generated attribution maps. The final step involves generating a heat map using the smoothed gradients. The heat map represents the attribution of different regions in the input to the model's prediction, providing a clearer and more stable visualization. Still Referring to FIG.20, generating map 2064 may include implementing one or more Gaussian Processes. A Gaussian Process is a collection of random variables, any finite subset of which has a joint Gaussian distribution. In simpler terms, it's a distribution over functions rather than a distribution over finite-dimensional vectors. Gaussian Processes (GPs) can be applied to generate heat maps in various ways, particularly in the context of regression tasks where one would want to predict continuous values across a spatial domain. Given a set of observed data points, the GP can predict the values at unobserved locations in the spatial domain. Importantly, it also provides uncertainty estimates associated with these predictions. This uncertainty can be visualized as a heat map. The predicted values from the GP represent the main heat map, indicating the expected values across the spatial domain. The uncertainty associated with each prediction can be visualized as an uncertainty heat map. This uncertainty heat map provides insights into regions where the model is less confident about its predictions. Overlay of the main heat map and the uncertainty heat map on the original spatial data may create a composite visualization. Warmer colors in the main heat map might represent higher predicted values, while the uncertainty heat map's intensity could indicate regions where the model's predictions are less certain. Still referring to FIG.20, processor 2004 is configured to overlay map 2064 onto 3D model 2056. In some embodiments, the overlay may be placed on 3D model 2056 and go 220 Attorney Docket No.1518-103PCT1 through a refinement process as described above. In some cases, overlaying 3D model 2056 with map 2064 may include utilizing interactive visualization techniques, which may allow user- mediated augmentation of the set of images. Overlaying map 2064 on a model may include implementing spatial alignment methods, texture mapping techniques wherein the color information from the heat map is mapped onto the vertices or faces of the 3D model, shader programs that define how the heat map values influence the final appearance of the 3D model, visualization software or programming libraries that support 3D rendering and overlay capabilities, interactivity visualization, quality control methods, and the like. For example, texture mapping may include UV Mapping wherein each point on the surface of a 3D model is associated with a set of texture coordinates often denoted as U and V. These coordinates are analogous to the X and Y coordinates on a 2D image. UV mapping establishes the correspondence between points on the 3D model and pixels on the 2D texture. In another example, interactive visualization may create visual representations of data that users can interact with and manipulate. This approach allows users to explore and analyze data dynamically, gaining insights through direct engagement with the visual representation. For example, mouse interactivity may allow users to interact with visual elements using mouse actions, such as hovering over data points for additional information, clicking to drill down into details, or dragging to pan and zoom. Filtering and Selection capabilities may allow a user to filter data based on specific criteria or select subsets of data for closer examination. This is particularly useful when dealing with large datasets. Spatial Exploration may allow users to zoom in to explore details or pan to navigate across the space. Still referring to FIG, 20. in some cases, an ultrasonic image taken during a medical procedure or synthesized for machine learning training purposes may be overlaid at a corresponding location or 3D model. For example, an ICE frame taken during an ICE procedure or synthesized for machine learning training purposes may be also overlaid at a corresponding location or 3D model. Overlaying the ultrasonic image may include registering the ultrasonic image to the generated 3D model 2056 using the image processing model. For example, the processing system may include at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a 3D model related to a structure of a subject, identify a region of interest within the 3D model, wherein identifying the region of interest includes locating at least a point 221 Attorney Docket No.1518-103PCT1 of view on the 3D model and determining a view angle corresponding to the at least a view origin, wherein the at least a point of view and the corresponding view angle define at least one field of view that include at least a portion of the 3D model. The at least a processor may be further configured to generate at least a medical image as a function of the region of interest using an image generator, wherein the at least a medical image captures an anatomical structure of the at least a portion of the 3D model. With continued reference to FIG.20, apparatus 2000 may further include a display device 2068. As used in this disclosure, a “display device” is an electronic device that visually presents information to a user. In an embodiment, display device may include an output interface that translates data such as, without limitation, subsequent 3D model from processor 2004 or other computing devices into a visual form that can be easily understood by user. In some cases, subsequent 3D model/or other data described herein such as, without limitation, ultrasonic images, 3D VOR, shape parameters initial model and/or template model may also be displayed through display device 2068 using a user interface 2072. User interface 2072 may include a graphical user interface (GUI), wherein the GUI may include a window in which subsequent 3D model and/or other data described herein may be displayed. In an embodiment, user interface 2072 may include one or more graphical locator and/or cursor facilities allowing user to interact with subsequent 3D model and/or any other data, or even process described herein; for instance, and without limitation, by using a touchscreen, touchpad, mouse, keyboard, and/or other manual data entry device, user may enter user input containing selecting specific regions, adding comments, adjusting parameter, and/or the like. In a non-limiting example, user interface 2072 may include one or more menus and/or panels permitting selection of measurements, models, visualization of data/model to be displayed and/or used, elements of data, functions, or other aspects of data/model to be edited, added, and/or manipulated, options for importation of and/or linking to application programmer interfaces (APIs), exterior services, data source, machine- learning models, and/or algorithms, or the like. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which a visual interface and/or elements thereof may be implemented and/or used as described in this disclosure. Still referring to FIG.20, in one or more embodiments, apparatus and methods described herein may involve one or more aspects of precise reconstructing of the left atrium (LA), pulmonary veins (PV), and left atrial appendage (LAA) in atrial fibrillation (AF) ablation. 222 Attorney Docket No.1518-103PCT1 “Atrial fibrillation (AF),” as described herein, is a cardiac arrhythmia characterized by irregular and often rapid heart rate. In some cases, AF may lead to various complications, including, without limitation, stroke, heart failure, and/or the like. “AF ablation,” as described herein, is a procedure that aims to isolate and eliminate the abnormal electrical pathways causing the cardiac arrhythmia. LA, PV, and LAA are key structures involved in AF. In an embodiment, precise 3D reconstruction of LA, PV, and LAA may help in understanding their geometry and relationships which are essential for planning and/or executing AF ablation. In some cases, LA, PV, and LAA may be mapped in order to enable clinicians to identify one or more specific sites responsible for AF, allowing targeted ablation that minimizes damages to surrounding tissues. Additionally, or alternatively, apparatus and methods described herein may reduce the risk of complications such as, without limitation, perforation, stenosis, collateral damage, among others to adjacent structures. Apparatus and methods described herein may ensure ablation energy is delivered to the intended locations. Still referring to FIG.20, in some embodiments, a computing device may determine a Left Atrial Appendage Occlusion Device placement as a function of the 3D model. In some embodiments, a computing device may determine a size of a Left Atrial Appendage Occlusion Device placement as a function of the 3D model. In some embodiments, a computing device may determine whether there is leakage resulting from Left Atrial Appendage Occlusion Device placement as a function of the 3D model. In some embodiments, a determined Left Atrial Appendage Occlusion Device size, placement, and/or leakage may be displayed to a user, such as by a display device. Still referring to FIG.20, in some embodiments, an apparatus and/or method described herein may allow ultrasonic imaging to replace and/or be an alternative to MRIs and/or CT scans. This may limit radiation exposure of subjects. Referring now to FIG.21, an exemplary embodiment of a method 2100 of generating a three-dimensional (3D) model with an overlay is illustrated. One or more steps if method 2100 may be implemented, without limitation, as described with reference to other figures. One or more steps of method 2100 may be implemented, without limitation, using at least a processor. Still referring to FIG.21, in some embodiments, method 2100 may include a step 2105 of receiving a set of ultrasonic images of an organ of a subject. In some embodiments, receiving the set of ultrasonic images includes receiving the set of ultrasonic images from a 223 Attorney Docket No.1518-103PCT1 patient profile. In some embodiments, the organ is a heart. In some embodiments, a set of ultrasonic images of the patient’s organ may include an image selected from the list consisting of a transesophageal echocardiogram image, a transthoracic echocardiogram image, and a point-of- care ultrasound image. Still referring to FIG.21, in some embodiments, method 2100 may include a step 2110 of generating a set of shape parameters representing the organ’s shape as a function of the set of ultrasonic images and a shape identification model trained on a training dataset including historical ultrasonic images correlated with historical computed tomography scan data. In some embodiments, the set of shape parameters includes a plurality of numerical descriptors representing at least a geometric characteristic of the organ. In some embodiments, each shape parameter within the set of shape parameters is associated with a corresponding parameter range. Still referring to FIG.21, in some embodiments, method 2100 may include a step 2115 of generating a 3D model of the organ based on the set of shape parameters. In some embodiments, generating the 3D model further includes generating a second 3D model as a function of the 3D model, by varying the set of shape parameters, wherein the second 3D model is statistically constrained. Still referring to FIG.21, in some embodiments, method 2100 may include a step 2120 of generating a map by determining a level of uncertainty at each location of a plurality of locations on the 3D model. In some embodiments, the map includes a color-coded heat map configured to visualize one or more areas of uncertainty on the 3D model. Still referring to FIG.21, in some embodiments, method 2100 may include a step 2125 of overlaying the map onto the 3D model. Still referring to FIG.21, in some embodiments, method 2100 may further include identifying the training dataset and/or training the shape identification model on the training dataset. In some embodiments, identifying a training dataset may include correlating an instance of computed tomography scan data with a historical ultrasonic image as a function of a medical record and a language model. In some embodiments, identifying a training dataset may include generating a synthetic ultrasonic image as a function of an instance of computed tomography scan data. Still referring to FIG.21, in some embodiments, method 2100 may further include determining a Left Atrial Appendage Occlusion Device placement as a function of the 3D model. 224 Attorney Docket No.1518-103PCT1 It is to be noted that any one or more of the aspects and embodiments described herein may be conveniently implemented using one or more machines (e.g., one or more computing devices that are utilized as a user computing device for an electronic document, one or more server devices, such as a document server, etc.) programmed according to the teachings of the present specification, as will be apparent to those of ordinary skill in the computer art. Appropriate software coding can readily be prepared by skilled programmers based on the teachings of the present disclosure, as will be apparent to those of ordinary skill in the software art. Aspects and implementations discussed above employing software and/or software modules may also include appropriate hardware for assisting in the implementation of the machine executable instructions of the software and/or software module. At a high level, aspects of the present disclosure are directed to apparatus and methods for visualization within a three-dimensional (3D) model using a query image and neural networks. In one or more embodiments, at least a processor may be configured to populate a synthetic image repository by generating a plurality of synthetic images from 3D model and position query image in the 3D model by querying the synthetic image repository, wherein neural network encodings may be extracted from both the query image and the plurality of synthetic images. Aspect of the present disclosure may be used to aid medical professionals in medical procedures by providing more precise visual guides. Aspects of the present disclosure may allow for greater versatility in research and development related to cardiac diagnostics. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples. Referring now to FIG.22, an exemplary embodiment of an apparatus 2200 for visualization within a 3D model using neural networks is illustrated. Apparatus 2200 includes at least a processor 2204. Processor 2204 may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Computing device may include, be included in, and/or communicate with a mobile device such as a laptop computer or a smartphone. Processor 2204 may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single 225 Attorney Docket No.1518-103PCT1 computing device or in two or more computing devices. Processor 2204 may interface or communicate with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for connecting processor 2204 to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software etc.) may be communicated to and/or from a computer and/or a computing device. Processor 2204 may include but is not limited to, for example, a first computing device or cluster of computing devices in a first location and a second computing device or cluster of computing devices in a second location. Processor 2204 may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. Processor 2204 may distribute one or more computing tasks as described below across a plurality of computing devices of computing device, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. In one or more embodiments, processor 2204 may be implemented using a “shared nothing” architecture in which data is cached at the worker; this may enable scalability of apparatus 2200 and/or computing device. With continued reference to FIG.22, in one or more embodiments, processor 2204 may be designed and/or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, processor 2204 may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or 226 Attorney Docket No.1518-103PCT1 division of a larger processing task into a set of iteratively addressed smaller processing tasks. Processor 2204 may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing. With continued reference to FIG.22, apparatus 2200 includes a memory 2208 communicatively connected to at least a processor 2204, wherein the memory 2208 contains instructions configuring the at least a processor 2204 to perform any processing steps described herein. For the purposes of this disclosure, “communicatively connected” means connected by way of a connection, attachment, or linkage between two or more relata which allows for reception and/or transmittance of information therebetween. For example, and without limitation, this connection may be wired or wireless, direct or indirect, and between two or more components, circuits, devices, systems, and the like, which allows for reception and/or transmittance of data and/or signal(s) therebetween. Data and/or signals therebetween may include, without limitation, electrical, electromagnetic, magnetic, video, audio, radio and microwave data and/or signals, combinations thereof, and the like, among others. A communicative connection may be achieved, for example and without limitation, through wired or wireless electronic, digital or analog, communication, either directly or by way of one or more intervening devices or components. Further, communicative connection may include electrically coupling or connecting at least an output of one device, component, or circuit to at least an input of another device, component, or circuit. For example, and without limitation, via a bus or other facility for intercommunication between elements of a computing device. Communicative connecting may also include indirect connections via, for example and without limitation, wireless connection, radio communication, low power wide area network, optical communication, magnetic, capacitive, or optical coupling, and the like. In some instances, the terminology “communicatively coupled” may be used in place of communicatively connected in this disclosure. 227 Attorney Docket No.1518-103PCT1 With continued reference to FIG.22, processor 2204 may perform determinations, classification, and/or analysis steps, methods, processes, or the like as described in this disclosure using machine learning processes. For the purposes of this disclosure, a “machine learning process” is a process that automatedly uses a body of data known as “training data” and/or a “training set” (which is described further below in this disclosure) to generate an algorithm that will be performed by a processor 2204/module to produce outputs given data provided as inputs; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. Machine learning process may utilize supervised, unsupervised, lazy-learning processes and/or neural networks, as described further below. With continued reference to FIG.22, processor 2204 is configured to receive a query image 2212. For the purposes of disclosure, a “query image” is an image used as a query to match another image and/or to selectively retrieve information for use in further method steps as disclosed below; each query image has an associated region of interest (ROI) 2216 that is to be determined or estimated in 3D space, as described below. Query image 2212 may include a medical image. For the purposes of this disclosure, a “medical image” is a two-dimensional visual representation containing information pertaining to an interior of a body and functions of organs/tissues therein that may aid clinical analysis and medical intervention. Query image 2212 may include, without limitation, X-ray image, echocardiogram (ECG), magnetic resonance imaging (MRI) scan, computed tomography (CT) scan 2220, ultrasound image including intracardiac echocardiogram (ICE) frame, transthoracic echocardiogram (TTE) frame, magnetic resonance imaging (MRI) scan, and/or transesophageal echocardiogram (TEE) frame, optical image, digital photograph, and/or the like. For the purposes of this disclosure, computed tomography (CT) is a medical imaging technique that uses X-rays to capture cross-sectional images (slices) of a patient’s body; by taking a plurality of slices, a CT scan creates a detailed three-dimensional (3D) representation of internal structures. For the purposes of this disclosure, an “ICE frame” is a 2D ultrasound image that represents anatomy (i.e., walls, chambers, blood vessels, etc.) of at least part of a heart, as described above. For the purposes of this disclosure, a “transthoracic echocardiogram (TTE) frame” is a two-dimensional (2D) ultrasound image collected by placing a probe or ultrasound transducer on patient’s chest or abdomen to collect various views of heart. For the purposes of this disclosure, a “transesophageal echocardiogram 228 Attorney Docket No.1518-103PCT1 (TEE) frame” is a 2D ultrasound image collected by passing a specialized probe containing an ultrasound transducer at its tip into patient’s esophagus; it is an alternative way of performing echocardiography. For the purposes of this disclosure, “echocardiography” is an imaging technique that uses ultrasound to examine heart, the resulting visual image of which is an echocardiogram. Anatomical structures may include, without limitation, chambers (e.g., four chambers including left and right atria and left and right ventricles), valves (i.e., the structures that regulate blood flow between chambers and vessels, including mitral, tricuspid, aortic, and pulmonary valves), vessels (e.g., aorta, pulmonary arteries and veins, and coronary arteries), conduction system (i.e., a network of specialized cells that controls the heart’s electrical activity and rhythm), muscular and connective tissues (e.g., heart’s muscular walls, septa, any other connective tissues that provide structural integrity and enable contraction), left atrial appendage and other appendages, pathological features (e.g., any abnormalities, defects, and/or the like), among others. ICE frame may be either collected and recorded by a medical professional using an image capture device, such as an ICE catheter, or synthesized from a 3D model 2220 using a synthetic ICE generator, as described below. In one or more embodiments, query image 2212 may be saved to and/or retrieved later from a patient profile 2224 and/or a database 2228. With continued reference to FIG.22, for the purposes of this disclosure, a “model” or “3D model” refers to a digital representation of a three-dimensional object, capturing its internal structures and geometry. In one or more embodiments, 3D model 2220 may be a digital representation (i.e., a 3D heart model) of a patient’s heart, capturing its anatomy, geometry, and potentially functional properties. The apparatus and methods described in this disclosure may be agnostic to how 3D model 2220 is generated. As nonlimiting examples, 3D heart model may be generated from electro-anatomical mapping, pre-operative computed tomography (CT), MRI scans, or synthetically reconstructed using echocardiography frames such as, without limitation, ICE frames and transthoracic echocardiogram (TTE) frames. For the purposes of this disclosure, a patient is a human or any individual organism, on whom or on which a procedure, study, or otherwise experiment, such as without limitation, atrial fibrillation ablation, may be conducted. In a nonlimiting example, processor 2204 may receive model 2220 from a human patient with atrial fibrillation who is undergoing a procedure, an individual undergoing cardiac screening, a participant in a clinical trial, an individual with congenital heart disease, a heart transplant candidate, an individual receiving follow-up care after cardiac surgery, a healthy volunteer, an 229 Attorney Docket No.1518-103PCT1 individual with heart failure, or the like. Additionally or alternatively, patient may include an animal model (i.e., an animal used to model atrial fibrillation such as a laboratory rat). With continued reference to FIG.22, in one or more embodiments, at least a processor 2204 may be configured to construct 3D model 2220 based on patient profile 2224. For the purposes of this disclosure, a “patient profile” is a comprehensive collection of information related to an individual patient. In one or more embodiments, patient profile 2224 may include a variety of data, including metadata as described below, that, when combined, provide a detailed picture of patient's overall health. In one or more embodiments, patient profile 2224 may include demographic data of patient; for example, and without limitation, patient profile 2224 may include basic information about patient such as name, age, gender, ethnicity, socioeconomic status, and/or the like. In one or more embodiments, each patient profile 2224 may also include patient’s medical history; for example, and without limitation, patient profile may include a detailed record of the patient's past health conditions, medical procedures, hospitalizations, and illnesses such as surgeries, treatments, medications, and/or the like. In one or more embodiments, each patient profile 2224 may include lifestyle information of patient; for example, and without limitation, patient profile 2224 may include details about the patient's diet, exercise habits, smoking and alcohol consumption, and other behaviors that could impact patient’s health. In one or more embodiments, patient profile 2224 may include patient’s family history; for example, and without limitation, patient profile 2224 may include a record of hereditary diseases. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various type of data within patient profiles 2224 that apparatus 2200 may receive and process in accordance with this disclosure. With continued reference to FGI.22, in one or more embodiments, patient profile 2224 may include a plurality of heart images and associated metadata. For the purposes of this disclosure, “metadata” are secondary data providing background information about one or more aspects of certain primary data that potentially make it easier to track and/or work with the primary data, as described below. In one or more embodiments, plurality of heart images may include a plurality of computed tomography (CT) scans of the patient’s heart. For the purposes of this disclosure, computed tomography is a medical imaging technique that uses X-rays to capture cross-sectional images (slices) of patient’s body. By taking a plurality of slices, a CT scan creates a detailed 3D representation of internal structures. Other exemplary embodiments of 230 Attorney Docket No.1518-103PCT1 heart images may include, without limitation, X-ray images, magnetic resonance imaging (MRI) scans, ultrasound images including ICE frames, optical images, digital photographs, or any other form of visual data, as described above. With continued reference to FIG.22, at least a processor 2204 may be configured to construct 3D model 2220 using a computer vision module 2232. For the purposes of this disclosure, a “computer vision module” is a computational component designed to perform one or more computer vision, image processing, and/or modeling tasks. In one or more embodiments, computer vision module 2232 may receive patient profile 2224 and generate model 2220 as a function of a set of images (and associated metadata). In one or more embodiments, computer vision module 2232 may include an image processing module, wherein heart images may be pre- processed using the image processing module. For the purposes of this disclosure, an “image processing module” is a component designed to process digital images such as heart images described herein. For example, and without limitation, image processing module may be configured to compile a plurality of images of a multi-layer scan to create an integrated image. In one or more embodiments, image processing module may include a plurality of software algorithms that can analyze, manipulate, or otherwise enhance an image, such as, without limitation, a plurality of image processing techniques as described below. In one or more embodiments, computer vision module 2232 may also include hardware components such as, without limitation, one or more graphics processing units (GPUs) that can accelerate the processing of a large number of images. In one or more embodiments, computer vision module 2232 may be implemented with one or more image processing libraries such as, without limitation, OpenCV, PIL/Pillow, ImageMagick, and the like. In a nonlimiting example, in order to generate a heart model, one or more image processing tasks, such as noise reduction, contrast enhancement, intensity normalization, image segmentation, and/or the like, may be performed by computer vision module 2232 on a plurality of CT scans to isolate heart and major vascular structures from surrounding tissues. In one or more embodiments, one or more machine learning models may be used to perform CT scans segmentations, for example, and without limitation, a U-net (i.e., a convolution neural network containing a contracting path as an encoder and an expansive path as a decoder, wherein the encoder and the decoder forms a U-shaped structure). With continued reference to FIG.22, in one or more embodiments, model 2220 may be received from a statistical shape model 2236. For the purposes of this disclosure, a “statistical 231 Attorney Docket No.1518-103PCT1 shape model (SSM)” is a data structure representing, including, and/or utilizing a mathematical model that captures principal modes of variation in shape across a population of similar three- dimensional structures, such as cardiac anatomies. SSM 2236 may capture a plurality of models 2220 associated with a plurality of patients. In one or more embodiments, SSM 2236 may be used to capture the variability in anatomical structures among different patients; for instance, SSM 2236 of a human heart may be constructed from a plurality of heart images collected from a plurality of individuals. In one or more embodiments, when model 2220 represents a heart, the model 2220 generated from SSM 2236 may capture an “average” heart shape and main ways in which heart shapes may vary among plurality of individuals. In a nonlimiting example, SSM 2236 described herein may be consistent with any SSM disclosed in this disclosure. With continued reference to FIG.22, in one or more embodiments, SSM 2236 may be generated by processor 2204 as a function of a set of labeled example shapes, each in a form of point-based representations or meshes. In one or more embodiments, example shapes may be represented in a 3D voxel occupancy representation (VOR). In one or more embodiments, model 2220 may include a VOR of patient’s heart. For the purposes of this disclosure, a "3D voxel occupancy representation" is a 3D digital representation of a spatial structure of the cardiac anatomy of a heart, wherein the representation is composed of a plurality of discrete volumetric elements known as voxels. For the purposes of this disclosure, a “voxel” is a 3D equivalent of a pixel used in 2D imaging. While a pixel represents a point in a 2D image and may include properties such as color and/or brightness, a voxel may represent a volume in a 3D space and may include additional properties such density/occupancy as described below. In one or more embodiments, each voxel within a plurality of voxels in 3D VOR may represent a specific portion of heart. With continued reference to FIG.22, in one or more embodiments, when model 2220 and/or SSM 2236 represents a heart, segmentation of the heart may include a plurality of pixel values, e.g., 0~255, each representing a presence of heart tissue at that location. In a nonlimiting example, computer vision module 2232 may be configured to generate a mesh representation of a patient’s heart based on plurality of CT scan segmentations or other image segmentations, wherein the mesh representation may include a 3D VOR, as described above, using Pix2Vox. Additionally or alternatively, exemplary computer vision tasks may include, without limitation, object recognition, feature detection, edge/corner detection, and the like. 232 Attorney Docket No.1518-103PCT1 Nonlimiting examples of feature detection may include scale invariant feature transform (SIFT), canny edge detection, Shi Tomasi corner detection, and/or the like. In one or more embodiments, generating mesh representation of patient’s heart may include employing, by computer vision module 2232, one or more transformations to orient one or more images with respect to a 3D coordinate system; exemplary transformations include without limitation homography transforms and affine transforms. Computer vision module 2232 may implement one or more 3D modeling algorithms, such as without limitation, marching cubes, contour detection and segmentation, active contour models, and/or the like to generate a coherent 3D representation based on mesh representation of an object, e.g., model 2220. In one or more embodiments, generic 3D modeling techniques may be applied by computer vision module 2232 to generate model 2220. In one or more embodiments, generic 3D modeling techniques may include surface modeling, solid modeling, or parametric modeling, among others. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various image processing, computer vision, and modeling tasks that may be performed by processor 2204 to generate model 2220 from a set of images such as heart images. With continued reference to FIG.22, in one or more embodiments, voxel may be a smallest distinguishable box-shaped part (i.e., 22px × 22px × 22px) of 3D representation of heart. In one or more embodiments, each voxel within a plurality of voxels in 3D VOR may be represented as a cube or rectangular prism (although other shapes may be used in specialized applications). Each voxel may include a size that determines the resolution of a 3D model. In one or more embodiments, smaller voxels may provide higher resolution; however, it may require more computational resources (e.g., RAM) for processor 2204 to process. In one or more embodiments, each voxel may include one or more embedded values (i.e., specific numerical or categorical data associated with each voxel). In one or more embodiments, embedded values may represent various attributes or characteristics of the corresponding portion of heart that voxel represents. In a nonlimiting example, embedded values may include density values, intensity values, texture information, or any other quantitative measures that provide insights into the underlying content (e.g., tissue). In another nonlimiting example, each voxel may include a presence indicator, i.e., a data element that indicates a presence or absence (i.e., occupancy) of content within a portion of an object (e.g., heart), as described in U.S. Pat. App. Ser. No. 233 Attorney Docket No.1518-103PCT1 228/376,688. Such embedded values may be derived from corresponding labels of example shape. With continued reference to FIG.22, in one or more embodiments, processor 2204 may be configured to align a set of labeled example shapes to a common reference frame using rigid, affine, or otherwise nonrigid registration methods to generate SSM 2236. For example, and without limitation, rigid registration may involve translations and rotations to superimpose the shapes; affine registration may incorporate scaling, shearing, and other linear transformations; nonrigid methods may employ B-splines, thin-plate splines, or diffeomorphic transformations to flexibly map one shape onto another. In one or more embodiments, an averaged position of each corresponding point (or voxel) across all example shapes may be calculated using formula ^̅^_^ ൌ ^ _ே ∑^ே ^_ୀ^ ^^_^^ , where ^̅^_^ is the mean position of the ^^th point (or voxel), ^^_^^ is the position of the ^^th point in the ^^th example shape, and ^^ is the total number of example shapes in the labeled set. In one or more embodiments, principal component analysis (PCA) may be applied to the aligned shapes to extract at least a primary mode of variation. For the purposes of this disclosure, a “primary mode of variation” is a mode of variation that has the most significant variability. For the purposes of this disclosure, a “mode of variation” is a specific pattern or direction of a shape change. In one or more embodiments, such significancy may be indicated by a first principal component in PCA. In one or more embodiments, a plurality of modes of variation may be extracted, wherein each mode (or principal component) may represent a specific way a shape may be deformed from a mean shape, determined by one or more eigenvectors of the covariance matrix of the aligned shapes. In a nonlimiting example, eigenvector with the highest eigenvalue may represent a primary mode of variation which captures the largest amount of shape variability within example shapes, while subsequent modes (eigenvectors) capture decreasing amounts of variability. With continued reference to FIG.22, in one or more embodiments, once modes of variation are extracted, processor 2204 may be configured to create a shape representation for any given shape within a studied class. In one or more embodiments, model 2220 may be constructed using SSM 2236, wherein model 2220 may integrate mean shape and plurality of modes of variation. In a nonlimiting example, model 2220 having a shape ^^ may be _{mathematically represented as ^^ ൌ ^^} ^̅ _{^ ∑} ^ெ ^_{ୀ^ ^^^ ൈ ^^ ^, wherein ^^} ^̅ _{denotes mean shape derived} from set of example shapes, ^^

of modes of variation considered, ^^_^ are the 234 Attorney Docket No.1518-103PCT1 coefficients or weights for each mode, and ^^_^ are the modes of variation (eigenvectors corresponding to the ^^th principal component). In one or more embodiments, coefficients ^^_^ may dictate a degree to which each mode of variation is present in shape ^^. In one or more embodiments, coefficients ^^_^ may vary from positive to negative (or negative to positive) based on a deformation of model 2220 in directions described by each mode of variation. In one or more embodiments, model 2220 may include mean shape as described herein. In one or more embodiments, model 2220 may include a predictive shape that may not have been explicitly seen in example shapes or observations. In one or more embodiments, model 2220 may be in 3D VOR as described above. With continued reference to FIG.22, in one or more embodiments, processor 2204 may be configured to perform shape extraction from segmented CT scans or other similar medical images, as described above. For example, and without limitation, marching cubes algorithm or similar techniques may be employed to convert a voxel-based representation from CT segmentation into mesh, wherein the mesh may represent the outer surface of patient’s heart. In one or more embodiments, mesh may vary in resolutions, with more grid capturing finer details. In one or more embodiments, a consistent number of landmark points may be used to represent patient’s heart surface. In a nonlimiting example, one or more landmark points may be manually annotated by medical professionals to ensure that the landmark points correspond to specific anatomical locations of patient’s heart. In one or more embodiments, one or more landmark points may be automatically derived using one or more computer vision algorithms as described herein. Landmark points may be uniformly spaced across the surface of extracted shape. In one or more embodiments, the size of heart shape may be normalized so that the number of landmark points remain consistent between different heart shapes. In one or more embodiments, SSM 2236 may include an implementation of generalized Procrustes analysis (GPA) to find a desired rigid transformation (translation, rotation) that aligns with example shapes. In a nonlimiting example, processor 2204 may be configured to minimize the sum of squared distance between corresponding landmark points across each heart shape. In one or more embodiments, size normalization may be reverted after such alignment. Constructing model 2220 may include combining mean shape computed by averaging positions of corresponding landmarks points and one or more modes of variations. In a nonlimiting example, model 2220 235 Attorney Docket No.1518-103PCT1 may include a template model generated based on a plurality of standard templates, as described in U.S. Pat. App. Ser. No.228/376,688. With continued reference to FIG.22, in one or more embodiments, model 2220, such as a heart model, may be constructed by extracting images, such as heart images, from patient profile 2224 (subsequent to patient identity verification and obtaining consent from subject). In one or more embodiments, patient profile 2224 may be obtained through hospital information system (HIS) or any other data acquisition platform to securely access patient’s electronic medical record (EMR) or other relevant databases. Images such as heart images may be directly or indirectly downloaded or exported. In one or more embodiments, each CT scan within heart images may be in a usable and/or computer-readable format such as, without limitation, DICOM format, and necessary metadata such as, without limitation, patient information, study information, image modality, CT scanner information, slice thickness, pixel spacing, matrix size, and/or the like may be included. In one or more embodiments, metadata may also include acquisition parameters such as, without limitation, tube voltage (kV), tube current (mA), exposure time, total dose length product (DLP), CT dose index (CTDI), rotation time, number of acquisitions, contrast agent used (if any), contrast phase, and/or the like. In one or more embodiments, receiving model 2220 may include recording an access and extraction of heart images from patient profile 2224; for instance, and without limitation, this process may be documented, by processor 2204, in patient’s medical record, database 2228, and/or other appropriate logs. With continued reference to FIG.22, in one or more embodiments, model 2220 may be directly imported from database 2228 or a similar repository containing pre-constructed models. In one or more embodiments, database 2228 may be based on historical patient scans, expert-constructed models, and/or the like. For instance, and without limitation, a heart model repository may consist of models derived from a diverse population, capturing various cardiac pathologies, anomalies, or physiological states. Database 2228 may be implemented, without limitation, as a relational database, a key-value retrieval database such as a NoSQL database, or any other format or structure for use as database that a person of ordinary skill in the art would recognize as suitable upon review of the entirety of this disclosure. Database 2228 may alternatively or additionally be implemented using a distributed data storage protocol and/or data structure, such as a distributed hash table or the like. Database 2228 may include a plurality of 236 Attorney Docket No.1518-103PCT1 data entries and/or records as described above. Data entries in database 2228 may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in database 2228 or another relational database. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in database 2228 may store, retrieve, organize, and/or reflect data and/or records as used herein, as well as categories and/or populations of data consistently with this disclosure. With continued reference to FIG.22, in one or more embodiments, patient profile 2224 may further include ECG data. For the purposes of this disclosure, “ECG data” are data related to an electrocardiogram of patient that corresponds to patient profile 2224. As a nonlimiting example, ECG data may accompany query ICE frame, as described below. For the purposes of this disclosure, an “electrocardiogram” is a recording of electrical activity of patient’s heart over a period of time. In one or more embodiments, ECG data may include one or more recordings captured by a plurality of electrodes placed on patient’s skin. In one or more embodiments, ECG data may include information regarding a P wave, T wave, QRS complex, PR interval, ST segment, and/or the like. In one or more embodiments, ECG data may be used to identify specific cardiac events or phases of a cardiac cycle, e.g., isovolumic relaxation, ventricular filling, isovolumic contraction, and rapid ventricular ejection. In a nonlimiting example, patient profile 2224 and ECG data described herein may be consistent with any patient profile and ECG data disclosed in this disclosure. With continued reference to FIG.22, in one or more embodiments, model 2220 may be directly imported from one or more external sources. In a nonlimiting example, model 2220 may be received from a dedicated computer software, e.g., specialized software solutions available for medical imaging and 3D model generation. In one or more embodiments, model 2220 may be exported from such software which may provide model segmentation, rendering, and generation capabilities tailored for cardiac structures. In another nonlimiting example, one or more third-party platforms (for patient data management, diagnostic imaging, and other healthcare functionalities) that support DICOM standards may allow for extraction and sharing model 2220 for synthetizing medical images as described in detail below. In a nonlimiting example, model 2220 may be received from several medical imaging and modeling services that are available on cloud. Such model 2220 may be sourced from a cloud-based service (e.g., SaaS). 237 Attorney Docket No.1518-103PCT1 With continued reference to FIG.22, model 2220 includes a plurality of regions of interest (ROIs) 2220; in one or more embodiments, each ROI 2216 within the plurality of ROIs may correspond to one query image 2212 and may be specified when the query image 2212 is matched to a corresponding synthetic image within a synthetic image repository, as described below. For the purposes of this disclosure, a “region of interest (ROI)” 2216 is a specific and pre-defined spatial subset of an image or a 3D model. In one or more embodiments, ROI 2216 may include a volume that has been designated for closer analysis or further processing as described in detail below due to its potential significance or relevance in synthesizing images. In one or more embodiments, identifying ROI 2216 within model 2220 may include isolating ROI 2216 from surrounding structure or structures that may be less relevant. In one or more embodiments, ROI 2216 may be manually selected by user. In one or more embodiments, one or more graphical tools and/or imaging software may be used to outline a particular area within model 2220 or an image captured from model 2220. In one or more embodiments, processor 2204 may be configured to automatically detect and define ROI 2216. In one or more embodiments, a computer vision module 2232 configured to perform one or more computer vision tasks such as, without limitation, thresholding, edge detection, or machine learning process may be used to recognize ROI 2216 with specific features or anomalies. With continued reference to FIG.22, in one or more embodiments, ROI 2216 may also include temporal ROI. In one or more embodiments, ROI 2216 may be not only spatial but also temporal. In one or more embodiments, a specific timeframe within a sequence may be designated as a ROI. In a nonlimiting example, temporal ROI may focus on a specific time segment or interval within a dynamic dataset, e.g., model 2220, with an animation that simulates a cardiac cycle. In one or more embodiments, temporal ROI may change over time. For example, and without limitation, temporal ROI may include a time-series images capturing patient’s heart activity, or a sequence showcasing blood flow within the cardiac structure. In a nonlimiting example, ROI 2216 may include temporal ROI set to capture a specific phase of cardiac cycle such as systole or diastole. In one or more embodiments, ROI 2216 may include a hierarchical ROI. In a nonlimiting example, processor 2204 may identify one or more smaller sub-ROIs within a larger ROI, each with its significance or weight. With continued reference to FIG.22, in or more embodiments, ROI 2216 may include a at least a field of view 2240. Each field of view 2240 may include at least a portion of 238 Attorney Docket No.1518-103PCT1 model 2220 and/or may further include at least a point of view 2244 and at least a view angle 2248. For the purposes of this disclosure, a “point of view” is a specific spatial location or origin form which an image or scene is observed or captured. In a nonlimiting example, point of view 2244 may be configured to mimic the location of an image capture device such as ICE catheter, within or near patient’s heart. In one or more embodiments, at least a point of view 2244 may be imagined as the location of a virtual image capture device. In one or more embodiments, at least a point of view 2244 may determine from where within model 2220 or its vicinity “pseudo” ultrasound waves are emitted and/or received. Given that ICE is a type of endoluminal ultrasound, in one or more embodiments, at least a point of view 2244 may be intracardiac and located inside heart chambers. Exemplary point of views 2244 may include, without limitation, ventricular point of view, atrial point of view, near-valvular point of view, and/or the like. In a nonlimiting example, ROI 2216 may be identified and at least a point of view 2244 may be located on the left ventricle’s wall, targeting its thickness and motion to assess potential cardiomyopathy. For the purposes of this disclosure, a “view angle” is an angular orientation or direction (i.e., defined by one or more ^^ and ^^ angles within spherical coordinates) associated with and projected from at least a point of view 2244. In one or more embodiments, view angle 2248 may determine the segment of a scene or image that is visible or captured. In a nonlimiting example, view angle 2248 may reflect the orientation of an imaging plane relative to the structure of interest within identified ROI 2216. In one or more embodiments, view angle 2248 corresponding to at least a point of view 2244 may define the tilt of the imaging plane, determining which structures come into field of view 2240. In one or more embodiments, field of view 2240 may indicate an area of a scene that may be captured by image capture device within defined bounds (e.g., spatial boundary of ROI 2216) inside model 2220. Exemplary view angle 2248 may include apical view (visualize patient’s heart from its apex), parasternal view (oriented laterally from the mid-sternal line), subcostal view (with angle inferiorly positioned). In one or more embodiments, view angle 2248 may correspond to the angle of the sector of a resultant medical image, such as an ICE image as described in detail below (which resembles a sector or- pie slice shape), wherein an ICE catheter tip may act as the sector’s apex (i.e., point of view 2244) that delineates an ultrasound wave’s spread and hence, the width of captured anatomy. In a nonlimiting example, a narrower view angle may be chosen to focus on a specific region of 239 Attorney Docket No.1518-103PCT1 patient’s heart e.g., a valve. Conversely, a broader view angle may capture a more extensive heart region, offering a comprehensive overview of model 2220. With continued reference to FIG.22, in one or more embodiments, one or more machine learning models may be used to perform certain function or functions of apparatus 2200, such as generating at least a synthetic image, extracting neural network encodings of at least a medical image, generating a plurality of shape parameters, and querying synthetic image repository, as described in detail below. Processor 2204 may use a machine learning module to implement one or more algorithms as described herein or generate one or more machine learning models, such as a pattern recognition model, as described below. However, machine learning module is exemplary and may not be necessary to generate one or more machine learning models and perform any machine learning described herein. In one or more embodiments, one or more machine learning models may be generated using training data. Training data may include inputs and corresponding predetermined outputs so that machine learning model may use correlations between the provided exemplary inputs and outputs to develop an algorithm and/or relationship that then allows the machine learning model to determine its own outputs for inputs. Training data may contain correlations that a machine learning process may use to model relationships between two or more categories of data elements. Exemplary inputs and outputs may come from database 2228 or be provided by a user. In one or more embodiments, machine learning module may obtain training data by querying communicatively connected database 2228 that includes past inputs and outputs. Training data may include inputs from various types of databases, resources, and/or user inputs and outputs correlated to each of those inputs, so that machine learning model may determine an output. Correlations may indicate causative and/or predictive links between data, which may be modeled as relationships, such as mathematical relationships, by machine learning models, as described in further detail below. In one or more embodiments, training data may be formatted and/or organized by categories of data elements by, for example, associating data elements with one or more descriptors corresponding to categories of data elements. As a nonlimiting example, training data may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data may be linked to descriptors of categories by tags, tokens, or other data elements. In one or more embodiments, 240 Attorney Docket No.1518-103PCT1 training data may include previous outputs such that one or more machine learning models may iteratively produce outputs. With continued reference to FIG.22, in one or more embodiments, processor 2204 may implement one or more aspects of “generative artificial intelligence (AI)”, a type of AI that uses machine learning algorithms to create, establish, or otherwise generate data such as, without limitation, synthetic medical images as described below that are similar to one or more training medical images within training data. In one or more embodiments, machine learning module described herein may generate one or more generative machine learning models that are trained on one or more set of example medical images previously generated. One or more generative machine learning models may be configured to generate new examples that are similar to the training data of the one or more generative machine learning models but are not exact replicas; for instance, and without limitation, data quality or attributes of the generated examples may bear a resemblance to the training data provided to one or more generative machine learning models, wherein the resemblance may pertain to underlying patterns, features, or structures found within the provided training data. With continued reference to FIG.22, in one or more embodiments, upon receiving query image 2212, processor 2204 is configured to extract neural network encodings 2252 as a function of the received query image 2212. For the purposes of this disclosure, “neural network encodings” are a plurality of parameters extracted by one or more neural networks that collectively describe features of a system and/or connections between elements therein; neural network encodings may include weights/biases/coefficients of neural network nodes, embeddings (vectors) generated by the neural networks, or a combination thereof. In one or more embodiments, neural network encodings 2252 may be extracted by generating a plurality of shape parameters 2256. In one or more embodiments, plurality of shape parameters 2256 may be generated by training a pattern recognition model 2260, as described below. In one or more embodiments, neural network encodings 2252 may be extracted based on generated plurality of shape parameters 2256. Details regarding the principles of neural networks and their implementations are described below. With continued reference to FIG.22, for the purposes of this disclosure, a “shape parameter” is a numerical value or descriptor that quantitatively represents geometric or morphological characteristics of patient’s heart. In a nonlimiting example, plurality of shape 241 Attorney Docket No.1518-103PCT1 parameters 2256 may include information and/or metadata calculated, determined, and/or extracted from query image 2212 and/or plurality of synthetic medical images as described below, such as, dimensions, angles, curvatures, areas, texture, symmetry, and/or the like. In one or more embodiments, processor 2204 may be configured to parameterize (model) features (e.g., edges, textures, contours, and the like) using convolutional neural networks, as described in detail below. Such parameterization may involve processor 2204 to derive one or more shape parameters 2256 including one or more morphological descriptors that quantitatively describe an object, such as patient’s heart, based on extracted features. With continued reference to FIG.22, in general, generating plurality of shape parameters 2256 may include i) receiving pattern recognition training data 2264 including a plurality of training images as inputs correlated to plurality of shape parameters 2256 as outputs; ii) training pattern recognition model 2260 using the pattern recognition training data 2264; and iii) generating the plurality of shape parameters 2256 using the pattern recognition model. In one or more embodiments, pattern recognition training data 2264 may include actual images, such as actual medical images (e.g., actual ICE frames) collected and/or saved by a medical professional or retrieved from patient profile 2224 and/or database 2228. In one or more embodiments, pattern recognition training data 2264 may contain synthetic images, such as synthetic medical images, as described below. In one or more embodiments, pattern recognition training data 2264 may be filtered, replaced, and/or otherwise updated as a function of one or more user inputs. With continued reference to FIG.22, in one or more embodiments, processor 2204 is further configured to query a synthetic image repository 2268 for at least a matching synthetic image 2272 based on extracted neural network encodings 2252 of query image 2212. Synthetic image repository 2268 includes plurality of synthetic images 2272 (e.g., a plurality of synthetic ICE frames, as described in detail below), and neural network encodings 2252 are extracted from each synthetic image 2272 by following the same or similar procedures as described above for query image 2212. Synthetic image repository 2268 may be implemented in any manner suitable for implementation of database 2228, as described in this disclosure. Each synthetic image 2272 has a one-to-one correspondence with ROI 2216, field of view 2240, point of view 2244, and/or view angle 2248 within 3D model 2220. In one or more embodiments, each synthetic image 2272 may be stored in database 2228 alongside its corresponding neural network encodings, ROI 2216, field of view 2240, point of view 2244, and/or view angle 2248. Querying synthetic image 242 Attorney Docket No.1518-103PCT1 repository 2268 involves comparing extracted neural network encodings 2252 of query image 2212 with extracted neural network encodings 2252 of each synthetic image 2272 within the plurality of synthetic images 2272. For the purposes of this disclosure, a “matching” synthetic image is a synthetic image with the same neural network encodings (i.e., embeddings or vectors, as described above), the same overall geometric features, and the same pattern of organization between elements therein as query image 2212. With continued reference to FIG.22, generation of synthetic images 2272 described in this disclosure may be consistent with any apparatus and/or methods disclosed in this disclosure. In one or more embodiments, plurality of synthetic images 2272 is generated by executing a camera transformation program 2276 configured to simulate at least a perspective of image capture device such as ICE catheter. For the purposes of this disclosure, a “camera transformation program” is a software or algorithm that manipulates location, perspective, and orientation of a virtual camera in relation to an object or scene. In one or more embodiments, camera transformation program 2276 may be executed to effectively transform or alter how ROI 2216 within model 2220 is visualized, simulating the effects of physically moving or adjusting a real-world camera or image capture device, such as ICE catheter or the like. In one or more embodiments, camera transformation program 2276 may involve moving at least a virtual camera’s position in 3D space. In one or more embodiments, virtual camera may be placed at the at least a point of view 2244 and/or the at least a view angle 2248. In one or more embodiments, virtual camera may be in the same object space as model 2220. In a nonlimiting example, camera transformation program 2276 may include translation configured to shift camera left, right, up, down, forward, or backward. In one or more embodiments, camera transformation program 2276 may include one or more instructions on configuring virtual camera’s orientation based on a horizontal or vertical axis. For example, and without limitation, virtual camera may be configured to pitch (tilt up or down), yaw (turn left or right), or roll (tilt sideways). In one or more embodiments, camera transformation program 2276 may adjust virtual camera’s perspective to “zoom” in or out on model 2220. In one or more embodiments, camera transformation program 2276 may be implemented through one or more image generators, as described below. With continued reference to FIG.22, in one or more embodiments, executing camera transformation program 2276 may include generating a 2D projection 2280 of 3D structures by 243 Attorney Docket No.1518-103PCT1 rendering ROI 2216 as a function of a set of imaging parameters using virtual camera positioned at the ROI 2216. For the purposes of this disclosure, a “2D projection” is a projection of 3D structures, such as a part of model 2220, onto a 2D projection plane. In one or more embodiments, 2D projection plane may be a pre-selected and/or standardized projection plane, such as the three orthogonal planes ( ^^ ^^ plane, ^^ ^^ plane, and ^^ ^^ plane) defined within the Cartesian coordinates. In one or more embodiments, such 2D projection of 3D structures may capture spatial and/or morphological features of one or more anatomical structures as described herein as they would appear from at least a point of view 2244, from at least a view angle 2248, and/or under certain imaging parameters. For the purposes of this disclosure, a “set of imaging parameters” refers to a collection of specific variables and configurations (of virtual camera) that determines how synthetic image 2272 may be generated, processed, and/or visualized. In one or more embodiments, set of imaging parameters may replicate one or more intricacies of real- world imaging, such as collection of ICE frames. In one or more embodiments, users, e.g., clinicians or medical professionals, may manually set or adjust set of imaging parameters through user interface as described below. In one or more embodiments, set of imaging parameters may be autodetected based on an initial generation of synthetic image 2272 and/or preliminary data. For example, and without limitation, set of image parameters may include a pre-defined subset of parameters configured for viewing particular heart regions or structures of mean shape. One or more machine learning models as described herein may be implemented to adjust set of image parameters iteratively based on the quality or clarity of an initial scan until desired synthetic image 2272 is achieved. With continued reference to FIG.22, in a nonlimiting example, camera transformation program 2276 may be configured to simulate projection as if image capture device is inserted from the apex of patent’s heart and angled towards the mitral valve, giving a detailed view of the valve’s leaflets and adjoining heart structures. In one or more embodiments, camera transformation program 2276 may be configured to determine how 3D objects, e.g., model 2220, are projected onto a 2D visual plane. Exemplary image projections may include, without limitation, orthographic (parallel) projection, perspective (converging lines) projection, and the like. In a nonlimiting example, for a close-up detailed view of ROI 2216 without depth distortions, an orthographic projection may be preferred, while for a more holistic view of how structures relate to one another in 3D space, a perspective projection may be more appropriate. 244 Attorney Docket No.1518-103PCT1 With continued reference to FIG.22, in one or more embodiments, for certain query image 2212, there may only be “near matches” instead of exact matches (e.g., based on matching vectors between extracted neural network encodings) within synthetic image repository 2268. In one or more embodiments, when only one near match is detected, processor 2204 may be configured to sample around the near match (e.g., within a certain threshold distance and/or angle) to identify an exact match. In one or more embodiments, when two or more near matches are detected, processor 2204 may be configured to interpolate between the two or more near matches to identify a 2D projection 2280 that is an exact match. Details regarding how 2D projections may be generated are described above in this disclosure. With continued reference to FIG.22, in one or more embodiments, processor 2204 may be configured to generate at least a synthetic image 2272 (e.g., a synthetic ICE frame) using at least an image generator. For the purposes of this disclosure, an “image generator” is a system, apparatus, or software module designed to produce or synthesize visual representations (images) based on certain input data. In one or more embodiments, image generator may be configured to generate at least a synthetic image 2272 based on input data such as, without limitation, model 2220, ROI 2216, point of view 2244, and view angle 2248, among others. In one or more embodiments, generation performed by image generator may be rooted in real-world data, simulated data, or a combination thereof. In one or more embodiments, image generator may include a software component that processes raw data from one or more imaging device, e.g., MRI, CT, or ultrasound machines, and reconstruct them into interpretable visual displays. With continued reference to FIG.22, in one or more embodiments, image generator may include a generative machine learning module, such as an image translation module, equipped with one or more generative models. For the purposes of this disclosure, a “generative model” is a statistical model of joint probability distribution ^^^ ^^, ^^^ on a given observable variable, ^^, representing features or data that can be directly measured or observed (e.g., model 2220, heart images, and/or associated metadata, among others) and target variable, ^^, representing outcomes or labels that one or more generative models aims to predict or generate (e.g., synthetic image 2272). Exemplary generative models include generative adversarial models (GANs), diffusion models, and the like. In one or more embodiments, generative models may rely on Bayes theorem to find joint probability; for instance, and without limitation, naive Bayes 245 Attorney Docket No.1518-103PCT1 classifiers may be employed by computing device to categorize input data such as, without limitation, model 2220 derived from CT scans into different views. With continued reference to FIG.22, in a nonlimiting example, one or more generative machine learning models may include one or more naive Bayes classifiers generated, by processor 2204, using a naive bayes classification algorithm. Naive Bayes classification algorithm may generate classifiers by assigning class labels to problem instances, represented as vectors of element values. Class labels are drawn from a finite set. Naive Bayes classification algorithm may include generating a family of algorithms that assume that the value of a particular element is independent of the value of any other element, given a class variable. Naive Bayes classification algorithm may be based on Bayes Theorem expressed as ^^^ ^^/ ^^^ ൌ ^^^ ^^/ ^^^ ൈ ^^^ ^^^ ൊ ^^^ ^^^, where ^^^ ^^/ ^^^ is the probability of hypothesis ^^ given data ^^, also known as posterior probability; ^^^ ^^/ ^^^ is the probability of data ^^ given that the hypothesis ^^ was true; ^^^ ^^^ is the probability of hypothesis ^^ being true regardless of data, also known as prior probability of ^^; and ^^^ ^^^ is the probability of data regardless of the hypothesis. A naive Bayes algorithm may be generated by first transforming training data into a frequency table. Processor 2204 may then calculate a likelihood table by calculating probabilities of different data entries and classification labels. Processor 2204 may utilize a naive Bayes equation to calculate a posterior probability for each class. A class containing the highest posterior probability is the outcome of prediction. With continued reference to FIG.22, although naive Bayes classifier may be primarily known as a probabilistic classification algorithm; however, it may also be considered a generative model described herein due to its capability of modeling the joint probability distribution ^^^ ^^, ^^^ over observable variables, ^^, and target variable, ^^. In one or more embodiments, naive Bayes classifier may be configured to make an assumption that the features, ^^, are conditionally independent given class label, ^^, allowing generative model to estimate a joint distribution as ^^^ ^^, ^^^ ൌ ^^^ ^^^∏ ^^ ^^^ ^^_^| ^^^, wherein ^^^ ^^^ is the prior probability of the class, and ^^^ ^^_^| ^^^ is the conditional probability of each feature given the class. One or more generative machine learning models containing naive Bayes classifiers may be trained on labeled training data, estimating conditional probabilities ^^^ ^^_^| ^^^ and prior probabilities ^^^ ^^^ for each class; for instance, and without limitation, using techniques such as Maximum Likelihood Estimation (MLE). One or more generative machine learning models containing naive Bayes 246 Attorney Docket No.1518-103PCT1 classifiers may select a class label ^^ according to prior distribution, ^^^ ^^^, and for each feature ^^_^, sample at least a value according to conditional distribution, ^^^ ^^_^| ^^^. Sampled feature values may then be combined to form one or more new data instances with selected class label, ^^. In a nonlimiting example, one or more generative machine learning models may include one or more naive Bayes classifiers to generate new synthetic images 2272, such as synthetic ICE frames, as a function of input data such as, without limitation, at least a point of view 2244 and at least a view angle 2248, wherein the models may be trained using training data containing plurality of models 2220 and ROIs 2216, as described herein as input correlated to plurality of synthetic images 2272. With continued reference to FIG.22, in one or more embodiments, processor 2204 may be configured to continuously monitor image generator. In an embodiment, processor 2204 may configure discriminator to provide ongoing feedback and further corrections as needed to subsequent input data. An iterative feedback loop may be created as processor 2204 continuously receives real-time data, identifies errors (e.g., distance between generated synthetic image 2272 and real images) as a function of real-time data, delivering corrections based on the identified errors and monitoring subsequent model outputs and/or user feedback on the delivered corrections. In one or more embodiments, processor 2204 may be configured to retrain one or more generative machine learning models within image generator based on user modified/annotated images or update training data of one or more generative machine learning models within image generator by integrating validated images (i.e., subsequent model output) into original training data. In such embodiment, iterative feedback loop may allow image generator to adapt to user’s needs and performance requirements, enabling one or more generative machine learning models described herein to learn and update based on user responses and generated feedback. Other exemplary embodiments of generative machine learning models may include, without limitation, long short-term memory networks (LSTMs), (generative pre-trained) transformer (GPT) models, mixture density networks (MDN), and/or the like. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various generative machine learning models that may be used to synthetize images as described herein. With continued reference to FIG.22, in one or more embodiments, image generator may be further configured to generate a multimodal neural network that combines various neural 247 Attorney Docket No.1518-103PCT1 network architectures described herein. In a nonlimiting example, multimodal neural network may combine LSTM for time-series analysis with GPT models for natural language processing. Such fusion may be applied by computing device to generate synthetic image 2272. In one or more embodiments, multimodal neural network may also include a hierarchical multimodal neural network, wherein the hierarchical multimodal neural network may involve a plurality of layers of integration. For instance, and without limitation, different models may be combined at various stages of the network. Convolutional neural network (CNN) may be used for image feature extraction, followed by LSTMs for sequential pattern recognition, and a MDN at the end for probabilistic modeling. Other exemplary embodiments of multimodal neural network may include, without limitation, ensemble-based multimodal neural network, cross-modal fusion, adaptive multimodal network, among others. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various multimodal neural networks and combination thereof that may be implemented by apparatus 2200 in accordance with this disclosure. With continued reference to FIG.22, in one or more embodiments, processor 2204 may be configured to generate at least a synthetic image 2272 using a generative adversarial network (GAN). For the purposes of this disclosure, a “generative adversarial network” is a type of artificial neural network with at least two sub models (i.e., neural networks), a generator and a discriminator, that compete against each other in a process that ultimately results in the generator learning to generate new data samples, wherein the “generator” is a component of the GAN that learns to create hypothetical data by incorporating feedback from the “discriminator” configured to distinguish real data from the hypothetical data. In one or more embodiments, generator may learn to make discriminator classify its output as real. In one or more embodiments, discriminator may include a supervised machine learning model while generator may include an unsupervised machine learning model, as described in further detail below. With continued reference to FIG.22, in one or more embodiments, discriminator may include one or more discriminative models, i.e., models of conditional probability ^^^ ^^| ^^ ൌ ^^^ of target variable, ^^, given observed variable, ^^. In one or more embodiments, discriminative models may learn boundaries between classes or labels in given training data. In a nonlimiting example, discriminator may include one or more classifiers as described in further detail below to distinguish between different categories, e.g., real vs. fake, or states, e.g., TRUE vs. FALSE 248 Attorney Docket No.1518-103PCT1 within the context of generated data such as, without limitations, synthetic images 2272, and/or the like. In one or more embodiments, processor 2204 may implement one or more classification algorithms such as, without limitation, Support Vector Machines (SVM), Logistic Regression, Decision Trees, and/or the like to define decision boundaries. With continued reference to FIG.22, in a nonlimiting example, generator of GAN may be responsible for creating synthetic data, such as synthetic images 2272 (e.g., synthetic ICE frames), that resemble true medical images (e.g., actual ICE frames). In one or more embodiments, GAN may be configured to receive model 2220 and/or set of images as input and generate corresponding examples of synthetic images 2272 containing information describing a 3D structure in different fields of view 2240. In one or more embodiments, processor 2204 may be configured to train GAN using a plurality of 2D projections 2280 as described above and generating at least a synthetic image 2272 using the trained GAN at ROI 2216, field of view 2240, point of view 2244, and/or view angle 2248. In one or more embodiments, when generating at least a synthetic image 2272, discriminator of GAN may evaluate the authenticity of the synthetic image 2272 by comparing it to true medical images; for example, discriminator may distinguish between genuine and generated ICE frames and provide feedback to generator to improve the model performance. Additionally or alternatively, GAN may include a conditional GAN as an extension of the basic GAN as described herein that allows for generation of synthetic images 2272 using model 2220 and/or set of images based on certain labels. In standard GAN, generator may produce samples from random noise, whereas in conditional GAN, generator may produce samples based on random noise and a given condition or label. With continued reference to FIG.22, additionally or alternatively, one or more generative models may also include a variational autoencoder (VAE). For the purposes of this disclosure, a “variational autoencoder” is an autoencoder (i.e., an artificial neural network architecture) whose encoding distribution is regularized during the model training process in order to ensure that its latent space includes desired properties allowing new data sample generation. In one or more embodiments, VAE may include a prior and noise distribution respectively, trained using expectation-maximization meta-algorithms such as, without limitation, probabilistic PCA, sparse coding, among others. In a nonlimiting example, VAE may use a neural network as an amortized approach to jointly optimize across input data and output a plurality of parameters for corresponding variational distribution as it maps from a known input 249 Attorney Docket No.1518-103PCT1 space to a low-dimensional latent space. Additionally or alternatively, VAE may include a second neural network, for example, and without limitation, a decoder, wherein the “decoder” is configured to map from latent space to input space. With continued reference to FIG.22, in one or more embodiments, generating at least a synthetic image 2272 using generative model may specifically involve training an image translation model 2284. For the purposes of this disclosure, an “image translation model” is a machine learning model configured to map images from a first domain to a second domain while preserving the content of the first domain. Implementation of image translation model 2284 may be consistent with any details related to machine learning described in this disclosure without limitation. In one or more embodiments, image translation model 2284 may be configured to perform unpaired image-to-image translation, wherein no pair information is established between first domain and second domain. Specifically, processor 2204 may be configured to i) receive image translation training data including a plurality of training images and a plurality of training 2D projections; ii) train image translation model 2284 by correlating the plurality of training images with the plurality of training 2D projections; and iii) synthesize the at least a synthetic image 2272 as a function of at least a 2D projection 2280 using the trained image translation model 2284. In one or more embodiments, training images may include at least a real image, such as a real ICE frame collected by medical professional. In one or more embodiments, training images may be retrieved from patient profile 2224, database 2228, or another image repository of similar nature. Training 2D projections may include any type of 2D projections and/or be consistent with any method of generating 2D projections described above in this disclosure. A trained image translation model 2284 may be able to use one or more 2D projections 2280 generated from 3D model 2220 to generate at least a synthetic image 2272, such as a synthetic ICE frame, that resembles a real image, such as a real ICE frame, without receiving any real image as input. With continued reference to FIG.22, processor 2204 is configured to display an estimated ROI 2288 of query image 2212 within 3D model 2220. For the purposes of this disclosure, an estimated ROI is an approximate fraction within 3D model 2220 that significantly overlaps with the actual ROI 2216 for query image 2212 with a reasonable level of certainty; in other words, an estimated ROI may deviate slightly from actual ROI 2216, but the deviation is minor enough ensure the overall precision of apparatus 2200 during its operation, e.g., a medical 250 Attorney Docket No.1518-103PCT1 procedure. In one or more embodiments, estimated ROI 2288 may be a function of ROI 2216 associated with matching synthetic image 2272 from query. In one or more embodiments, displaying estimated ROI 2288 may include overlaying a 2D cross section 2292 within at least a portion of 3D model 2220, as described below. In one or more embodiments, estimated ROI 2288 and/or elements related thereto may be displayed on one or more display devices 2296, as described below. With continued reference to FIG.22, apparatus 2200 may further include or be coupled to at least a display device 2296. For the purposes of this disclosure, a “display device” is an electronic device that visually presents information to user. In one or more embodiments, display device 2296 may include an output interface that translates data such as, without limitation, synthetic image 2272, from processor 2204 or other computing devices into a visual form that can be easily understood by user. In one or more embodiments, synthetic image 2272 and/or other data described herein such as, without limitation, model 2220, patient profile 2224, and/or the like may also be displayed through display device 2296 using a user interface. User interface may include a graphical user interface (GUI), wherein the GUI may include a window in which query image 2212, plurality of synthetic image 2272, among other data described herein, may be displayed. In one or more embodiments, user interface may include one or more graphical locator and/or cursor facilities allowing user to interact with query image 2212, synthetic image 2272, and/or any other data, or even process described herein; for instance, and without limitation, by using a touchscreen, touchpad, mouse, keyboard, and/or other manual data entry device, user may enter user input containing selecting specific regions, adding comments, adjusting parameter, and/or the like. In a nonlimiting example, user interface may include one or more menus and/or panels permitting selection of measurements, models, visualization of data/model to be displayed and/or used, elements of data, functions, or other aspects of data/model to be edited, added, and/or manipulated, options for importation of and/or linking to application programmer interfaces (APIs), exterior services, data source, machine-learning models, and/or algorithms, or the like. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which a visual interface and/or elements thereof may be implemented and/or used as described in this disclosure. With continued reference to FIG.22, it should be noted that apparatus 2200 and methods described herein are not limited to medical or cardiac applications only. For example, 251 Attorney Docket No.1518-103PCT1 and without limitation, visualization capabilities disclosed herein may be effectively adapted for use within other organs, such as liver, where precision and minimally invasive diagnostics are also crucial. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will recognize one or more embodiments described herein (although principally focused on the heart) and their underlaying principles may be readily transferrable to a broader spectrum of medical imaging and intervention applications such as, without limitation, transcatheter intervention (which is rapidly supplanting traditional open surgery), or other nonmedical contexts that are not currently disclosed. Referring now to FIG.23A, a flow diagram 2300a of an exemplary embodiment for a synthetic image generation process is illustrated. In one or more embodiments, processor 2204 may be configured to receive a model 2220 and identify at least a ROI 2216 based on the received model 2220. Field of view 2240, which may include at least a point of view 2244 and at least a view angle 2248, may be determined to capture ROI 2216. In one or more embodiments, model 2220 received by processor 2204 may be derived from CT scans or other similar images using SSM 2236, as described above. Synthetic image 2272, such as synthetic ICE frame 2304, may then be generated, by processor 2204, as a function of field of view 2240. For the purposes of this disclosure, a “synthetic ICE frame” refers to a digitally generated or simulated image that emulates a visual representation obtained from field of view 2240, as described above. In one or more embodiments, synthetic ICE frames 2304 may be produced using computational methods and/or models such as, without limitation, an image generator 2308 having one or more camera transformation program 2276 and/or generative machine learning models based on pre-existing data, models, or simulations, e.g., model 2220, as described above. In a nonlimiting example, synthetic ICE frames 2304 may include a simplified version, e.g., an image illustrating heart anatomy via a plurality of lines indicating contours of heart’s structure as shown in FIG.23A. One or more image processing techniques and/or computer vision algorithms as described above, such as, without limitation, histogram equalization, adaptive filtering, edge detection (e.g., Canny or Sobel operators), contour extraction, and/or the like may be applied, by imaging processing module and/or computer vision module 2232 as described above, at field of view 2240. Synthetic ICE frame 2304 may be rendered on a blank canvas or background that mimics the echogenicity of ICE frames according to extracted contours, wherein the extracted contours may be represented as bold lines and enhanced with shading to give depth. In one or more 252 Attorney Docket No.1518-103PCT1 embodiments, synthetic ICE frame 2304 may be validated and verified by overlaying synthetic ICE frame 2304 onto field of view 2240, ensuring accuracy and resemblance. Referring now to FIG.23B, an exemplary embodiment 2300b of 2D cross section 2212 overlaid within at least a portion of 3D model 2220 is illustrated, wherein the 2D cross section 2212 contains estimated ROI 2288 of query image 2212. In one or more embodiments, estimated ROI 2288 may adapt to at least a change in input in real time. As a nonlimiting example, change in input may be a change in position of image capture device (e.g., ICE catheter) during a procedure that results in a change in query medical image 2272. As another nonlimiting example, change in input may be a fluctuation of cardiac anatomy over time (e.g., a cardiac cycle or heartbeat) that results in a change in query medical image 2272. As a nonlimiting example, estimated ROI 2288 may contain one or more rotatable views that may aid medical professionals in positioning image capture device, such as ICE catheter, during medical procedures such as an atrial fibrillation ablation procedure. In one or more embodiments, processor 2204 may be further configured to evaluate the certainty in estimated ROI 2288; if the certainty falls below a certain threshold, processor may be configured to receive at least a supplemental query image, and iteratively update the estimated ROI 2288 as a function of the at least a supplemental query image, until a desired certainty is reached. In a nonlimiting example, receiving at least a supplemental query image and/or updating estimated ROI 2288 described herein may be consistent with any detail disclosed in this disclosure. Referring now to FIG.24, an exemplary embodiment of method 2400 that provides visualization within model 2220 is described. At step 2405, method 2400 includes receiving, by at least a processor 2204, a query image 2212. This step may be implemented with reference to details described above in this disclosure and without limitation. In one or more embodiments, query image 2212 may be a query medical image, such as a query ICE frame. With continued reference to FIG.24, at step 2410, method 2400 includes extracting, by at least a processor 2204, neural network encodings 2252 as a function of the received query image 2212. This step may be implemented with reference to details described above in this disclosure and without limitation. In one or more embodiments, neural network encodings 2252 may be extracted by generating plurality of shape parameters 2256, wherein the generation of shape parameters 2256 may involve training a pattern recognition model 2260. 253 Attorney Docket No.1518-103PCT1 With continued reference to FIG.24, at step 2415, method 2400 includes querying, by at least a processor 2204, a synthetic image repository 2268 for at least a matching synthetic image 2272 based on extracted neural network encodings 2252 of query image 2212. This step may be implemented with reference to details described above in this disclosure and without limitation. In one or more embodiments, synthetic image repository 2268 may contain plurality of synthetic images 2272 and their corresponding neural network encodings 2252. In one or more embodiments, plurality of synthetic images 2272 may be generated by executing camera transformation program 2276 configured to simulate at least a perspective of image capture device. In one or more embodiments, plurality of synthetic medical images may be generated using an image translation model 2284. With continued reference to FIG.24, at step 2420, method 2400 includes displaying, by at least a processor 2204, estimated ROI 2288 of query image 2212 within 3D model 2220 by positioning query image 2212 as a function of at least a matching synthetic image 2272. This step may be implemented with reference to details described above in this disclosure and without limitation. In one or more embodiments, displaying estimated ROI within the 3D model 2220 may include overlaying 2D cross section 2212 containing the estimated ROI 2288 within at least a portion of the 3D model 2220. It is to be noted that any one or more of the aspects and embodiments described herein may be conveniently implemented using one or more machines (e.g., one or more computing devices that are utilized as a user computing device for an electronic document, one or more server devices, such as a document server, etc.) programmed according to the teachings of the present specification, as will be apparent to one of ordinary skill in the computer art. Appropriate software coding can readily be prepared by skilled programmers based on the teachings of the present disclosure, as will be apparent to those of ordinary skill in the software art. Aspects and implementations discussed above employing software and/or software modules may also include appropriate hardware for assisting in the implementation of the machine executable instructions of the software and/or software module. Such software may be a computer program product that employs a machine-readable storage medium. A machine- readable storage medium may be any medium that is capable of storing and/or encoding a sequence of instructions for execution by a machine (e.g., a computing device) and that causes the machine to perform any one of the methodologies and/or embodiments described herein. 254 Attorney Docket No.1518-103PCT1 Examples of a machine-readable storage medium include, but are not limited to, a magnetic disk, an optical disc (e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-only memory “ROM” device, a random-access memory “RAM” device, a magnetic card, an optical card, a solid-state memory device, an EPROM, an EEPROM, and any combinations thereof. A machine-readable medium, as used herein, is intended to include a single medium as well as a collection of physically separate media, such as, for example, a collection of compact discs or one or more hard disk drives in combination with a computer memory. As used herein, a machine-readable storage medium does not include transitory forms of signal transmission. Such software may also include information (e.g., data) carried as a data signal on a data carrier, such as a carrier wave. For example, machine-executable information may be included as a data- carrying signal embodied in a data carrier in which the signal encodes a sequence of instruction, or portion thereof, for execution by a machine (e.g., a computing device) and any related information (e.g., data structures and data) that causes the machine to perform any one of the methodologies and/or embodiments described herein. Examples of a computing device include, but are not limited to, an electronic book reading device, a computer workstation, a terminal computer, a server computer, a handheld device (e.g., a tablet computer, a smartphone, etc.), a web appliance, a network router, a network switch, a network bridge, any machine capable of executing a sequence of instructions that specify an action to be taken by that machine, and any combinations thereof. In one example, a computing device may include and/or be included in a kiosk. With continued reference to FIG.25, the figure shows a diagrammatic representation of one embodiment of a computing device in the exemplary form of a computing system 2500 within which a set of instructions for causing the computing system 2500 to perform any one or more of the aspects and/or methodologies of the present disclosure may be executed. It is also contemplated that multiple computing devices may be utilized to implement a specially configured set of instructions for causing one or more of the devices to perform any one or more of the aspects and/or methodologies of the present disclosure. Computing system 2500 may include a processor 2504 and a memory 2508 that communicate with each other, and with other components, via a bus 2512. Bus 2512 may include any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures. Processor 2504 may 255 Attorney Docket No.1518-103PCT1 include any suitable processor, such as without limitation a processor incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit, which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; processor 2504 may be organized according to Von Neumann and/or Harvard architecture as a nonlimiting example. Processor 2504 may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor, field programmable gate array, complex programmable logic device, graphical processing unit, general-purpose graphical processing unit, tensor processing unit, analog or mixed signal processor, trusted platform module, a floating-point unit, and/or system on a chip. With continued reference to FIG.25, memory 2508 may include various components (e.g., machine-readable media) including, but not limited to, a random-access memory component, a read only component, and any combinations thereof. In one example, a basic input/output system 2516, including basic routines that help to transfer information between elements within computing system 2500, such as during start-up, may be stored in memory 2508. Memory 2508 (e.g., stored on one or more machine-readable media) may also include instructions (e.g., software) 2520 embodying any one or more of the aspects and/or methodologies of the present disclosure. In another example, memory 2508 may further include any number of program modules including, but not limited to, an operating system, one or more application programs, other program modules, program data, and any combinations thereof. With continued reference to FIG.25, computing system 2500 may also include a storage device 2524. Examples of a storage device (e.g., storage device 2524) include, but are not limited to, a hard disk drive, a magnetic disk drive, an optical disc drive in combination with an optical medium, a solid-state memory device, and any combinations thereof. Storage device 2524 may be connected to bus 2512 by an appropriate interface (not shown). Example interfaces include, but are not limited to, small computer system interface, advanced technology attachment, serial advanced technology attachment, universal serial bus, IEEE 1394 (FIREWIRE), and any combinations thereof. In one example, storage device 2524 (or one or more components thereof) may be removably interfaced with computing system 2500 (e.g., via an external port connector (not shown)). Particularly, storage device 2524 and an associated machine-readable medium 2528 may provide nonvolatile and/or volatile storage of machine- readable instructions, data structures, program modules, and/or other data for computing system 256 Attorney Docket No.1518-103PCT1 2500. In one example, software 2520 may reside, completely or partially, within machine- readable medium 2528. In another example, software 2520 may reside, completely or partially, within processor 2504. With continued reference to FIG.25, computing system 2500 may also include an input device 2532. In one example, a user of computing system 2500 may enter commands and/or other information into computing system 2500 via input device 2532. Examples of input device 2532 include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), a cursor control device (e.g., a mouse), a touchpad, an optical scanner, a video capture device (e.g., a still camera, a video camera), a touchscreen, and any combinations thereof. Input device 2532 may be interfaced to bus 2512 via any of a variety of interfaces (not shown) including, but not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE interface, a direct interface to bus 2512, and any combinations thereof. Input device 2532 may include a touch screen interface that may be a part of or separate from display 2536, discussed further below. Input device 2532 may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface as described above. With continued reference to FIG.25, user may also input commands and/or other information to computing system 2500 via storage device 2524 (e.g., a removable disk drive, a flash drive, etc.) and/or network interface device 2540. A network interface device, such as network interface device 2540, may be utilized for connecting computing system 2500 to one or more of a variety of networks, such as network 2544, and one or more remote devices 2548 connected thereto. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide-area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network, such as network 2544, may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information 257 Attorney Docket No.1518-103PCT1 (e.g., data, software 2520, etc.) may be communicated to and/or from computing system 2500 via network interface device 2540. With continued reference to FIG.25, computing system 2500 may further include a video display adapter 2552 for communicating a displayable image to a display device, such as display device 2536. Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter 2552 and display device 2536 may be utilized in combination with processor 2504 to provide graphical representations of aspects of the present disclosure. In addition to a display device, computing system 2500 may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus 2512 via a peripheral interface 2556. Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof. The foregoing has been a detailed description of illustrative embodiments of the invention. Various modifications and additions can be made without departing from the spirit and scope of this invention. Features of each of the various embodiments described above may be combined with features of other described embodiments as appropriate in order to provide a multiplicity of feature combinations in associated new embodiments. Furthermore, while the foregoing describes a number of separate embodiments, what has been described herein is merely illustrative of the application of the principles of the present invention. Additionally, although particular methods herein may be illustrated and/or described as being performed in a specific order, the ordering is highly variable within ordinary skill to achieve methods, systems, and software according to the present disclosure. Accordingly, this description is meant to be taken only by way of example, and not to otherwise limit the scope of this invention. Exemplary embodiments have been disclosed above and illustrated in the accompanying drawings. It will be understood by those skilled in the art that various changes, omissions and additions may be made to that which is specifically disclosed herein without departing from the spirit and scope of the present invention. 258 Attorney Docket No.1518-103PCT1 The foregoing has been a detailed description of illustrative embodiments of the invention. Various modifications and additions can be made without departing from the spirit and scope of this invention. Features of each of the various embodiments described above may be combined with features of other described embodiments as appropriate in order to provide a multiplicity of feature combinations in associated new embodiments. Furthermore, while the foregoing describes a number of separate embodiments, what has been described herein is merely illustrative of the application of the principles of the present invention. Additionally, although particular methods herein may be illustrated and/or described as being performed in a specific order, the ordering is highly variable within ordinary skill to achieve embodiments as disclosed herein. Accordingly, this description is meant to be taken only by way of example, and not to otherwise limit the scope of this invention. In the descriptions above and in the claims, phrases such as “at least one of” or “one or more of” may occur followed by a conjunctive list of elements or features. The term “and/or” may also occur in a list of two or more elements or features. Unless otherwise implicitly or explicitly contradicted by the context in which it is used, such a phrase is intended to mean any of the listed elements or features individually or any of the recited elements or features in combination with any of the other recited elements or features. For example, the phrases “at least one of A and B;” “one or more of A and B;” and “A and/or B” are each intended to mean “A alone, B alone, or A and B together.” A similar interpretation is also intended for lists including three or more items. For example, the phrases “at least one of A, B, and C;” “one or more of A, B, and C;” and “A, B, and/or C” are each intended to mean “A alone, B alone, C alone, A and B together, A and C together, B and C together, or A and B and C together.” In addition, use of the term “based on,” above and in the claims is intended to mean, “based at least in part on,” such that an unrecited feature or element is also permissible. The subject matter described herein can be embodied in systems, apparatus, methods, and/or articles depending on the desired configuration. The implementations set forth in the foregoing description do not represent all implementations consistent with the subject matter described herein. Instead, they are merely some examples consistent with aspects related to the described subject matter. Although a few variations have been described in detail above, other modifications or additions are possible. In particular, further features and/or variations can be provided in addition to those set forth herein. For example, the implementations described 259 Attorney Docket No.1518-103PCT1 above can be directed to various combinations and sub-combinations of the disclosed features and/or combinations and sub-combinations of several further features disclosed above. In addition, the logic flows depicted in the accompanying figures and/or described herein do not necessarily require the particular order shown, or sequential order, to achieve desirable results. Other implementations may be within the scope of the following claims. 260 Attorney Docket No.1518-103PCT1

Claims

WHAT IS CLAIMED IS: 1. An apparatus for generating a three-dimensional (3D) model of cardiac anatomy via machine-learning, wherein the apparatus comprises: at least a process; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a set of images of a cardiac anatomy pertaining to a subject; generate a three-dimensional (3D) data structure representing the cardiac anatomy as a function of the set of images, wherein generating the 3D data structure comprises: receiving cardiac anatomy training data, wherein the cardiac anatomy training data comprises a plurality of image sets as input and a plurality of cardiac anatomy models as output; training a cardiac anatomy modeling model using the cardiac anatomy training data; and generating the 3D data structure representing data structure representing the cardiac anatomy as a function of the set of images using the trained cardiac anatomy modeling model; generate an initial 3D model of the cardiac anatomy; refine the generated initial 3D model of the cardiac anatomy as a function of the 3D data structure representing the cardiac anatomy; and generate a subsequent 3D model of the cardiac anatomy as a function of the refinement.

2. The apparatus of claim 1, wherein receiving the set of images comprises receiving the set of images from a patient profile.

3. The apparatus of claim 1, wherein receiving the cardiac anatomy training data comprises generating the cardiac anatomy training data using a synthetic ICE data generator.

4. The apparatus of claim 1, wherein the 3D data structure representing the cardiac anatomy comprises: a 3D voxel occupancy representation (VOR) having a plurality of voxels, wherein each voxel of the plurality of voxels comprises a corresponding presence indicator. 261 Attorney Docket No.1518-103PCT1

5. The apparatus of claim 4, wherein the 3D data structure representing the cardiac anatomy further comprises: a 3D grid configured to map the presence indicators of the plurality of voxels, wherein the 3D grid comprises one or more spatial features extracted from the set of images of the cardiac anatomy.

6. The apparatus of claim 1, wherein generating the 3D data structure representing the cardiac anatomy further comprises: generating a set of shape parameters based on the set of images of the cardiac anatomy.

7. The apparatus of claim 6, wherein generating the set of shape parameters based on the set of images comprises: training a shape identification model using cardiac geometry training data, wherein the cardiac geometry training data comprises the plurality of image sets as input correlated to a plurality of shape parameter sets as output; and generating the set of shape parameters as a function of the set of images using the trained shape identification model.

8. The apparatus of claim 1, wherein the initial 3D model of the cardiac anatomy comprises a template model selected from a plurality of pre-determined template models.

9. The apparatus of claim 8, wherein refining the initial 3D model of the cardiac anatomy comprises: deforming the template model to match the generated 3D data structure representing the cardiac anatomy.

10. The apparatus of claim 8, wherein refining the initial 3D model of the cardiac anatomy comprises: adjusting the subsequent 3D model of the cardiac anatomy as a function of a set of shape parameters.

11. A method for generating a three-dimensional (3D) model of cardiac anatomy via machine-learning, wherein the method comprises: receiving, by at least a processor, a set of images of a cardiac anatomy pertaining to a subject; 262 Attorney Docket No.1518-103PCT1 generating, by the at least a processor, a 3D data structure representing the cardiac anatomy as a function of the set of images, wherein generating the 3D data structure comprises: receiving cardiac anatomy training data, wherein the cardiac anatomy training data comprises a plurality of image sets as input and a plurality of computed tomography (CT) based cardiac anatomy models as output; training a cardiac anatomy modeling model using the cardiac anatomy training data; and generating the 3D data structure representing the cardiac anatomy as a function of the set of images using the trained cardiac anatomy modeling model; generating, by the at least a processor, an initial 3D model of the cardiac anatomy; refining, by the at least a processor, the generated initial 3D model of the cardiac anatomy as a function of the 3D data structure representing the cardiac anatomy using; and generating, by the at least a processor, a subsequent 3D model of the cardiac anatomy as a function of the refinement.

12. The method of claim 11, wherein receiving the set of images comprises receiving the set of images from a patient profile.

13. The method of claim 11, wherein receiving the cardiac anatomy training data comprises generating the cardiac anatomy training data using a synthetic ICE data generator.

14. The method of claim 11, wherein the 3D data structure representing the cardiac anatomy comprises: a 3D voxel occupancy representation (VOR) having a plurality of voxels, wherein each voxel of the plurality of voxels comprises a corresponding presence indicator.

15. The method of claim 14, wherein the 3D data structure representing the cardiac anatomy further comprises: a 3D grid configured to map the presence indicators of the plurality of voxels, wherein the 3D grid comprises one or more spatial features extracted from the set of images of the cardiac anatomy.

16. The method of claim 11, wherein generating the 3D data structure representing the cardiac anatomy further comprises: 263 Attorney Docket No.1518-103PCT1 generating a set of shape parameters based on the set of images of the cardiac anatomy.

17. The method of claim 16, wherein generating the set of shape parameters based on the set of images comprises: training a shape identification model using cardiac geometry training data, wherein the cardiac geometry training data comprises the plurality of image sets as input correlated to a plurality of shape parameter sets as output; and generating the set of shape parameters as a function of the set of images using the trained shape identification model.

18. The method of claim 11, wherein the initial 3D model of the cardiac anatomy comprises a template model selected from a plurality of pre-determined template models.

19. The method of claim 18, wherein refining the initial 3D model of the cardiac anatomy comprises: deforming the template model to match the generated 3D data structure representing the cardiac anatomy using a statistical shape model.

20. The method of claim 18, wherein refining the initial 3D model of the cardiac anatomy comprises: adjusting the subsequent 3D model of the cardiac anatomy as a function of a set of shape parameters using a statistical shape model.

21. An apparatus for generating a three-dimensional (3D) model of cardiac anatomy via machine-learning, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a set of images of a cardiac anatomy pertaining to a subject; generate cardiac anatomy training data using a 3D heart model, wherein the cardiac anatomy training data comprises a plurality of synthetic images; train a cardiac anatomy modeling model using the generated cardiac anatomy training data; generate a three-dimensional (3D) data structure representing the cardiac anatomy using the trained cardiac anatomy modeling model; and 264 Attorney Docket No.1518-103PCT1 refine an initial 3D model as a function of the 3D data structure representing the cardiac anatomy.

22. The apparatus of claim 21, wherein receiving the set of images comprises receiving the set of images from a patient profile.

23. The apparatus of claim 21, wherein the 3D heart model is configured to receive ongoing feedback and corrections to the 3D heart model and provide corrections to subsequent synthetic images.

24. The apparatus of claim 23, wherein generating the cardiac anatomy training data comprises: generating the 3D heart model using a plurality of CT scans; generating, as a function of the 3D heart model, a plurality of synthetic ICE frames using a synthetic ICE data generator; and generating the cardiac anatomy training data as a function of the plurality of synthetic ICE frames.

25. The apparatus of claim 21, wherein the 3D data structure representing the cardiac anatomy comprises: a 3D voxel occupancy representation (VOR) having a plurality of voxels, wherein each voxel of the plurality of voxels comprises a corresponding presence indicator.

26. The apparatus of claim 25, wherein the 3D data structure representing the cardiac anatomy further comprises: a 3D grid configured to map the presence indicators of the plurality of voxels, wherein the 3D grid comprises one or more spatial features extracted from the set of images of the cardiac anatomy.

27. The apparatus of claim 21, wherein the plurality of synthetic images comprise synthetic ICE image frames, wherein the synthetic ICE image frames comprise bold lines and shading to represent extracted contours of an ICE image.

28. The apparatus of claim 21, wherein the initial 3D model of the cardiac anatomy comprises a template model selected from a plurality of pre-determined template models.

29. The apparatus of claim 28, wherein refining the initial 3D model of the cardiac anatomy comprises: 265 Attorney Docket No.1518-103PCT1 deforming the template model to match the generated 3D data structure representing the cardiac anatomy.

30. The apparatus of claim 28, wherein refining the initial 3D model of the cardiac anatomy comprises: adjusting the refined initial 3D model of the cardiac anatomy as a function of a set of shape parameters.

31. A method for generating a three-dimensional (3D) model of cardiac anatomy via machine-learning, wherein the method comprises: receiving, by at least a processor, a set of images of a cardiac anatomy pertaining to a subject; generating, by the at least a processor, cardiac anatomy training data using a 3D heart model, wherein the cardiac anatomy training data comprises a plurality of synthetic images; training, by the at least a processor, a cardiac anatomy modeling model using the generated cardiac anatomy training data; generating, by the at least a processor, a three-dimensional (3D) data structure representing the cardiac anatomy using the trained cardiac anatomy modeling model; and refining, by the at least a processor, an initial 3D model as a function of the 3D data structure representing the cardiac anatomy.

32. The method of claim 31, wherein receiving the set of images comprises receiving the set of images from a patient profile.

33. The method of claim 31, wherein the 3D heart model is configured to receive ongoing feedback and corrections to the 3D heart model and provide corrections to subsequent synthetic images.

34. The method of claim 31, wherein generating the cardiac anatomy training data comprises: generating the 3D heart model using a plurality of CT scans; generating, as a function of the 3D heart model, a plurality of synthetic Ice frames using a synthetic ICE data generator; and generating the cardiac anatomy training data as a function of the plurality of synthetic ICE frames. 266 Attorney Docket No.1518-103PCT1

35. The method of claim 31, wherein the 3D data structure representing the cardiac anatomy comprises: a 3D voxel occupancy representation (VOR) having a plurality of voxels, wherein each voxel of the plurality of voxels comprises a corresponding presence indicator.

36. The method of claim 35, wherein the 3D data structure representing the cardiac anatomy further comprises: a 3D grid configured to map the presence indicators of the plurality of voxels, wherein the 3D grid comprises one or more spatial features extracted from the set of images of the cardiac anatomy.

37. The method of claim 31, wherein the plurality of synthetic images comprise synthetic ICE image frames, wherein the synthetic ICE image frames comprise bold lines and shading to represent extracted contours of an ICE image.

38. The method of claim 31, wherein the initial 3D model of the cardiac anatomy comprises a template model selected from a plurality of pre-determined template models.

39. The method of claim 38, wherein refining the initial 3D model of the cardiac anatomy comprises: deforming the template model to match the generated 3D data structure representing the cardiac anatomy.

40. The method of claim 38, wherein refining the initial 3D model of the cardiac anatomy comprises: adjusting the refined initial 3D model of the cardiac anatomy as a function of a set of shape parameters.

41. An apparatus for generating a three-dimensional (3D) model of an anatomical object via machine-learning, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a set of images of an anatomical object pertaining to a subject; generate anatomy training data using a 3D anatomical model, wherein the anatomy training data comprises a plurality of image sets as input and a plurality of anatomical object models as output; 267 Attorney Docket No.1518-103PCT1 train an anatomy modeling model using the generated anatomy training data; generate a three-dimensional (3D) data structure representing the anatomical object using the trained anatomy modeling model; and refine an initial 3D model as a function of the 3D data structure representing the anatomical object.

42. The apparatus of claim 41, wherein the set of images comprise one or more ultrasonic images.

43. The apparatus of claim 41, wherein the anatomical object comprises an organ.

44. The apparatus of claim 41, wherein receiving the set of images comprises receiving the set of images from a patient profile.

45. The apparatus of claim 44, wherein: receiving the set of images from the patient profile further comprises receiving (ECG) data associated with the subject form the patient profile; and the anatomy training data further comprises the plurality of image sets and a plurality of ECG data as inputs and the plurality of anatomical object models as outputs.

46. The apparatus of claim 45, wherein the trained anatomy modeling model comprises a multimodal machine learning model.

47. The apparatus of claim 41, wherein the 3D anatomical model is configured to receive ongoing feedback and corrections to the 3D anatomical model and provide corrections to subsequent synthetic images.

48. The apparatus of claim 41, wherein generating the initial 3D model comprises determining a level of uncertainty of at least one location of a plurality of locations of the initial 3D model.

49. The apparatus of claim 41, wherein generating the initial 3D model further comprises generating a map visualizing a level of uncertainty on the 3D model.

50. The apparatus of claim 41, wherein the initial 3D model of the anatomical object comprises a template model selected from a plurality of pre-determined template models.

51. A method for generating a three-dimensional (3D) model of an anatomical object via machine-learning, wherein the method comprises: receiving, by at least a processor, a set of images of an anatomical object pertaining to a subject; 268 Attorney Docket No.1518-103PCT1 generating, by the at least a processor, anatomy training data using a 3D anatomical model, wherein the anatomy training data comprises a plurality of image sets as input and a plurality of anatomical object models as output; training, by the at least a processor, an anatomy modeling model using the generated anatomy training data; generating, by the at least a processor, a three-dimensional (3D) data structure representing the anatomical object using the trained anatomy modeling model; and refining, by the at least a processor, an initial 3D model as a function of the 3D data structure representing the anatomical object.

52. The method of claim 51, wherein the set of images comprise one or more ultrasonic images.

53. The method of claim 51, wherein the anatomical object comprises an organ.

54. The method of claim 51, wherein receiving, by the at least a processor, the set of images comprises receiving the set of images from a patient profile.

55. The method of claim 54, wherein: receiving, the set of images from the patient profile further comprises receiving (ECG) data associated with the subject form the patient profile; and the anatomy training data further comprises the plurality of image sets and a plurality of ECG data as inputs and the plurality of anatomical object models as outputs.

56. The method of claim 55, wherein the trained anatomy modeling model comprises a multimodal machine learning model.

57. The method of claim 51, wherein receiving, by the at least a processor, the set of images comprises receiving the set of images from a patient profile.

58. The method of claim 51, wherein generating, by the at least a processor, the anatomy training data using the 3D anatomical model comprises: classifying the set of images to an anatomical categorization; and generating the anatomy training data using the 3D anatomical model as a function of the anatomical categorization. 269 Attorney Docket No.1518-103PCT1

59. The method of claim 51, wherein the 3D anatomical model is configured to receive ongoing feedback and corrections to the 3D anatomical model and provide corrections to subsequent synthetic images.

60. The method of claim 51, wherein generating the initial 3D model further comprises generating a map visualizing a level of uncertainty on the 3D model.

61. The method of claim 51, wherein the initial 3D model of the anatomical object comprises a template model selected from a plurality of pre-determined template models.

62. An apparatus for synthetizing medical images, wherein the apparatus comprises: at least a process; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a heart model related to a patient’s heart; identify a region of interest within the heart model, wherein identifying the region of interest comprises: locating at least a point of view on the heart model; and determining a view angle corresponding to the at least a point of view, wherein the at least a point of view and the corresponding view angle define at least one field of view that include at least a portion of the heart model; and generate at least a medical image as a function of the region of interest using an image generator, wherein the at least a medical image captures an anatomical structure of the at least a portion of the heart model.

63. The apparatus of claim 62, wherein receiving the heart model comprises: constructing the heart model based on a patient profile pertaining to the patient using computer vision module, wherein the patient profile comprises a set of images of the patient’s heart and associated metadata.

64. The apparatus of claim 63, wherein the patient profile further comprises electrocardiogram (ECG) data.

65. The apparatus of claim 62, wherein receiving the heart model comprises: transforming the heart model to a second heart model using a Statistical Shape Model as a function of a plurality of mode changers within the Statistical Shape Model, 270 Attorney Docket No.1518-103PCT1 wherein each mode changer of the plurality of mode changers is associated with a model feature of the heart model.

66. The apparatus of claim 62, wherein the heart model comprises a 3D voxel occupancy representation (VOR) of the patient’s heart.

67. The apparatus of claim 62, wherein generating the at least a medical image comprises: executing a camera transformation program configured to simulate at least a perspective of a probe using the image generator.

68. The apparatus of claim 67, wherein executing the camera transformation program comprises: generating a projection of the anatomical structure by rendering the region of interest as a function of a set of imaging parameters using a virtual camera positioned at the at least a point of view with the corresponding view angle.

69. The apparatus of claim 62, wherein the image generator comprises a generative adversarial network (GAN).

70. The apparatus of claim 69, wherein generating the at least a medical comprises: training the GAN using a plurality of anatomical structure projections; and synthesizing at least a medical image using the trained GAN at the at least a point of view with the corresponding view angle.

71. The apparatus of claim 64, wherein the memory contains instructions further configuring the at least a processor to: compile a plurality of medical images into a video as a function of the ECG data, wherein the video is synchronized with a cardiac cycle indicated by the ECG data.

72. A method for synthetizing medical images, wherein the method comprises: receiving, by at least a processor, a heart model related to a patient’s heart; identifying, by the at least a processor, a region of interest within the heart model, wherein identifying the region of interest comprises: locating at least a point of view on the heart model; and determining a view angle corresponding to the at least a point of view, wherein the at least a point of view and the corresponding view angle define at least one field of view that include at least a portion of the heart model; and 271 Attorney Docket No.1518-103PCT1 generating, by the at least a processor, at least a medical image as a function of the region of interest using an image generator, wherein the at least a medical image captures an anatomical structure of the at least a portion of the heart model.

73. The method of claim 72, wherein receiving the heart model comprises: constructing the heart model based on a patient profile pertaining to the patient using a computer vision module, wherein the patient profile comprises a set of images of the patient’s heart and associated metadata.

74. The method of claim 73, wherein the patient profile further comprises electrocardiogram (ECG) data.

75. The method of claim 72, wherein receiving the heart model comprises: transforming the heart model to a second heart model using a Statistical Shape Model as a function of a plurality of mode changers within the Statistical Shape Model, wherein each mode changer of the plurality of mode changers is associated with a model feature of the heart model.

76. The method of claim 72, wherein the heart model comprises a 3D voxel occupancy representation (VOR) of the patient’s heart.

77. The method of claim 72, wherein generating the at least a medical image comprises: executing a camera transformation program configured to simulate at least a perspective of a probe using the image generator.

78. The method of claim 77, wherein executing the camera transformation program comprises: generating a projection of the anatomical structure by rendering the region of interest as a function of a set of imaging parameters using a virtual camera positioned at the at least a point of view with the corresponding view angle.

79. The method of claim 72, wherein the image generator comprises a generative adversarial network (GAN).

80. The method of claim 79, wherein generating the at least a medical image comprises: training the GAN using a plurality of anatomical structure projections; and synthesizing at least a medical image using the trained GAN at the at least a point of view with the corresponding view angle.

81. The method of claim 74, further comprises: 272 Attorney Docket No.1518-103PCT1 compiling, by the at least a processor, a plurality of medical images into a video as a function of the ECG data, wherein the video is synchronized with a cardiac cycle indicated by the ECG data.

82. An apparatus for synthetizing medical images, wherein the apparatus comprises: at least a process; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive an ultrasound image of a patient’s organ; generate an organ model related to the patient’s organ as a function of the ultrasound image; identify a region of interest within the organ model, wherein identifying the region of interest comprises: locating at least a point of view on the organ model; and determining a view angle corresponding to the at least a point of view, wherein the at least a point of view and the corresponding view angle define at least one field of view that include at least a portion of the organ model; and generate at least a medical image as a function of the region of interest using an image generator, wherein the at least a medical image captures an anatomical structure of the at least a portion of the organ model.

83. The apparatus of claim 82, wherein the ultrasound image of the patient’s organ comprises one or more of a transesophageal echocardiogram image, transthoracic echocardiogram image, and point-of-care ultrasound image.

84. The apparatus of claim 82, wherein generating the organ model comprises generating a three-dimensional (3D) data structure representing the patient’s organ using an anatomy modeling model.

85. The apparatus of claim 84, wherein generating the 3D data structure representing the patient’s organ using the anatomy modeling model comprises: generating anatomy training data, wherein the anatomy training data comprises a plurality of image sets as input and a plurality of anatomical object models as output; training the anatomy modeling model using the anatomy training data; and 273 Attorney Docket No.1518-103PCT1 generating the 3D data structure using the trained anatomy modeling model.

86. The apparatus of claim 82, wherein the image generator comprises a generative machine- learning model.

87. The apparatus of claim 86, wherein generating the at least a medical image of the patient’s organ comprises: receiving image training data, wherein the image training data comprises exemplary organ models correlated to exemplary medical images; training the generative machine-learning model using the image training data; and generating the at least a medical image of the patient’s organ using the generative machine-learning model.

88. The apparatus of claim 82, wherein identifying the region of interest within the organ model comprises: selecting a first set of points from a medical image; determining a second set of points on the organ model corresponding to the first set of points; and mapping a plurality of points of the medical image to the organ model using a relationship between the first set of points and the second set of points.

89. The apparatus of claim 88, wherein mapping the plurality of points of the medical image to the organ model using the relationship between the first set of points and the second set of points comprises determining a rigid transformation from the first set of points to the second set of points.

90. The apparatus of claim 82, wherein generating the organ model comprises: transforming the organ model to a second organ model using a Statistical Shape Model as a function of a plurality of mode changers within the Statistical Shape Model, wherein each mode changer of the plurality of mode changers is associated with a model feature of the organ model.

91. The apparatus of claim 82, wherein: generating the at least a medical image comprises generating a plurality of medical images; and the memory contains instructions further configuring the at least a processor to: compile the plurality of medical images into a video; and 274 Attorney Docket No.1518-103PCT1 display the video on a display device.

92. A method for synthetizing medical images, wherein the method comprises: receiving, by at least a processor, an ultrasound image of a patient’s organ; generating, by at least a processor, an organ model related to the patient’s organ as a function of the ultrasound image; identifying, by the at least a processor, a region of interest within the organ model, wherein identifying the region of interest comprises: locating at least a point of view on the organ model; and determining a view angle corresponding to the at least a point of view, wherein the at least a point of view and the corresponding view angle define at least one field of view that include at least a portion of the organ model; and generating, by the at least a processor, at least a medical image as a function of the region of interest using an image generator, wherein the at least a medical image captures an anatomical structure of the at least a portion of the organ model.

93. The method of claim 92, wherein the ultrasound image of the patient’s organ comprises one or more of a transesophageal echocardiogram image, transthoracic echocardiogram image, and point-of-care ultrasound image.

94. The method of claim 92, wherein generating the organ model comprises generating a three-dimensional (3D) data structure representing the patient’s organ using an anatomy modeling model.

95. The method of claim 94, wherein generating the 3D data structure representing the patient’s organ using the anatomy modeling model comprises: generating anatomy training data, wherein the anatomy training data comprises a plurality of image sets as input and a plurality of anatomical object models as output; training the anatomy modeling model using the anatomy training data; and generating the 3D data structure using the trained anatomy modeling model.

96. The method of claim 92, wherein the image generator comprises a generative machine- learning model.

97. The method of claim 96, wherein generating the at least a medical image of the patient’s organ comprises: 275 Attorney Docket No.1518-103PCT1 receiving image training data, wherein the image training data comprises exemplary organ models correlated to exemplary medical images; training the generative machine-learning model using the image training data; and generating the at least a medical image of the patient’s organ using the generative machine-learning model.

98. The method of claim 92, wherein identifying the region of interest within the organ model comprises: selecting a first set of points from a medical image; determining a second set of points on the organ model corresponding to the first set of points; and mapping a plurality of points of the medical image to the organ model using a relationship between the first set of points and the second set of points.

99. The method of claim 98, wherein mapping the plurality of points of the medical image to the organ model using the relationship between the first set of points and the second set of points comprises determining a rigid transformation from the first set of points to the second set of points.

100. The method of claim 92, wherein generating the organ model comprises: transforming the organ model to a second organ model using a Statistical Shape Model as a function of a plurality of mode changers within the Statistical Shape Model, wherein each mode changer of the plurality of mode changers is associated with a model feature of the organ model.

101. The method of claim 92, wherein: generating the at least a medical image comprises generating a plurality of medical images; and the method further comprises: compiling, by the at least a processor, the plurality of medical images into a video; and displaying, by the at least a processor, the video on a display device.

102. A method of generating a three-dimensional (3D) model of cardiac anatomy, the method comprising: using at least a processor, receiving a first set of images of cardiac anatomy; 276 Attorney Docket No.1518-103PCT1 using at least a processor, generating a first 3D model of the cardiac anatomy as a function of the first set of images; using at least a processor, calculating a level of uncertainty at a plurality of locations on the first 3D model; using at least a processor, receiving a second set of images of the cardiac anatomy corresponding to a high uncertainty region of the first 3D model; and using at least a processor, generating a second 3D model as a function of the second set of images.

103. The method of claim 102, wherein receiving a second set of images comprises: using a display device, displaying the first 3D model of the cardiac anatomy to a user; and by the user, positioning a cardiac image capture device for capturing an image of a low confidence region.

104. The method of claim 103, wherein displaying the first 3D model of the cardiac anatomy to the user comprises: using a display device, displaying the first 3D model of the cardiac anatomy to a user; generating a first map comprising a level of uncertainty at each location of a plurality of locations on the generated first 3D model; and overlaying the first map onto the first 3D model.

105. The method of claim 104, wherein the first map identifies the high uncertainty region of the first 3D model.

106. The method of claim 104, wherein the first map comprises a color-coded heat map configured to visualize one or more areas of uncertainty on the first 3D model.

107. The method of claim 102, wherein receiving a second set of images comprises capturing a second set of images using a cardiac image capture device, wherein the cardiac image capture device comprises an intracardiac echocardiography catheter.

108. The method of claim 102, wherein the method further comprises removing an image of the first set of images from the first set of images.

109. The method of claim 102, wherein the method further comprises duplicating an image of the first set of images and adding the duplicate to the first set of images. 277 Attorney Docket No.1518-103PCT1

110. The method of claim 102, wherein generating the first 3D model comprises generating the first 3D model using a neural network.

111. The method of claim 110, wherein: generating the first 3D model using a neural network comprises generating a set of shape parameters based on the first set of images; generating the set of shape parameters comprises: receiving cardiac geometry training data comprising a plurality of image sets as inputs correlated to a plurality of shape parameter sets as outputs; training a shape identification model using the cardiac geometry training data; and generating the set of shape parameters using the shape identification model; and the first 3D model is generated based on the set of shape parameters.

112. The method of claim 111, wherein the high uncertainty region is determined using model output uncertainty.

113. The method of claim 111, wherein the plurality of shape parameter sets of the cardiac geometry training data is generated using computed tomography.

114. The method of claim 110, wherein the neural network comprises a convolutional neural network.

115. The method of claim 111, wherein generating the first 3D model further comprises using a statistical shape model to generate the first 3D model as a function of the set of shape parameters.

116. The method of claim 111, wherein the set of shape parameters comprises a plurality of numerical descriptors, wherein each numerical descriptor of the plurality of numerical descriptors represents a geometric characteristic of the cardiac anatomy.

117. The method of claim 111, wherein each shape parameter within the set of shape parameters comprises a corresponding parameter range.

118. The method of claim 102, further comprising continuously updating, using the processor, the second 3D model as a function of further sets of images.

119. The method of claim 102, further comprising displaying the second 3D model to a user.

120. The method of claim 119, wherein displaying the second 3D model of the cardiac anatomy to the user comprises: 278 Attorney Docket No.1518-103PCT1 generating a second map by determining a level of uncertainty at each location of a plurality of locations on the generated second 3D model; and overlaying the second map onto the second 3D model.

121. The method of claim 120, wherein the second map comprises a color-coded heat map configured to visualize one or more areas of uncertainty on the second 3D model.

122. An apparatus of generating a three-dimensional (3D) model of a patient’s organ, the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a first set of images of a patient’s organ; determine, at a trained neural network, a first set of shape parameters as a function of the first set of images; generate a first 3D model of the patient’s organ as a function of the first set of shape parameters; calculate a level of uncertainty at each location of a plurality of locations on the first 3D model of the patient’s organ; receive a second set of images of the patient’s organ corresponding to a high uncertainty region of the first 3D model; determine, at the trained neural network, a second set of shape parameters as a function of the first set of images and the second set of images; and generate a second 3D model of the patient’s organ as a function of the second set of shape parameters.

123. The apparatus of claim 122, wherein the first set of images and the second set of images of the patient’s organ comprises a plurality of ultrasound images, and wherein the plurality of ultrasound images comprises one or more of a transesophageal echocardiogram image, transthoracic echocardiogram image, and point-of-care ultrasound image.

124. The apparatus of claim 122, wherein determining the second set of shape parameters comprises: 279 Attorney Docket No.1518-103PCT1 combining the second set of images with the first set of images by replacing one or more images corresponding to the high uncertainty region of the first 3D model within the first set of images with the second set of images.

125. The apparatus of claim 124, wherein determining the second set of shape parameters comprises: calibrating the trained neural network by fine-tuning the trained neural network using the first set of images; and determining the second set of shape parameters as a function of the second set of images using the trained neural network.

126. The apparatus of claim 122, wherein generating the first 3D model comprises generating, as a function of the first set of shape parameters, the first 3D model using a statistical shape model.

127. The apparatus of claim 126, wherein generating the second 3D model comprises adjusting, at the statistical shape model, the first 3D model as a function of the second set of shape parameters.

128. The apparatus of claim 122, wherein calculating the level of uncertainty at each location of the plurality of locations of the first 3D model of the patient’s organ comprises: generating a first map comprising the level of uncertainty at each location of the plurality of locations on the first 3D model of the patient’s organ; overlaying the first map onto the first 3D model; and displaying, using a display device, the first 3D model of the patient’s organ to a user.

129. The apparatus of claim 128, wherein generating the second 3D model of the patient’s organ comprises: generating a second map by re-calculating the level of uncertainty at each location of the plurality of locations on the second 3D model of the patient’s organ; overlaying the second map onto the second 3D model of the patient’s organ; and displaying, using the display device, the second 3D model of the patient’s organ to the user.

130. The apparatus of claim 128, wherein receiving the second set of images of the patient’s organ comprises: 280 Attorney Docket No.1518-103PCT1 identifying, on the first map, the high uncertainty region of the first second 3D model of patient’s organ by comparing the level of uncertainty at each location of the plurality of locations to a pre-determined uncertainty threshold.

131. The apparatus of claim 129, wherein each one of the first map and the second map comprises a color-coded heat map configured to visualize one or more areas of uncertainty on the first 3D model and second 3D model of the patient’s organ respectively.

132. A method of generating a three-dimensional (3D) model of a patient’s organ, the method comprises: using at least a processor, receiving a first set of images of a patient’s organ; using the at least a processor, determining, at a trained neural network, a first set of shape parameters as a function of the first set of images; using the at least a processor, generating a first 3D model of the patient’s organ as a function of the first set of shape parameters; using the at least a processor, calculating a level of uncertainty at each location of a plurality of locations on the first 3D model of the patient’s organ; using the at least a processor, receiving a second set of images of the patient’s organ corresponding to a high uncertainty region of the first 3D model; using the at least a processor, determining, at the trained neural network, a second set of shape parameters as a function of the first set of images and the second set of images; and using the at least a processor, generating a second 3D model of the patient’s organ as a function of the second set of shape parameters.

133. The method of claim 132, wherein the first set of images and the second set of images of the patient’s organ comprises a plurality of ultrasound images, and wherein the plurality of ultrasound images comprises one or more of a transesophageal echocardiogram image, transthoracic echocardiogram image, and point-of-care ultrasound image.

134. The method of claim 132, wherein determining the second set of shape parameters comprises: 281 Attorney Docket No.1518-103PCT1 combining the second set of images with the first set of images by replacing one or more images corresponding to the high uncertainty region of the first 3D model within the first set of images with the second set of images.

135. The method of claim 134, wherein determining the second set of shape parameters comprises: calibrating the trained neural network by fine-tuning the trained neural network using the first set of images; and determining the second set of shape parameters as a function of the second set of images using the trained neural network.

136. The method of claim 132, wherein generating the first 3D model comprises generating, as a function of the first set of shape parameters, the first 3D model using a statistical shape model.

137. The method of claim 136, wherein generating the second 3D model comprises adjusting, at the statistical shape model, the first 3D model as a function of the second set of shape parameters.

138. The method of claim 132, wherein calculating the level of uncertainty at each location of the plurality of locations of the first 3D model of the patient’s organ comprises: generating a first map comprising the level of uncertainty at each location of the plurality of locations on the first 3D model of the patient’s organ; overlaying the first map onto the first 3D model; and displaying, using a display device, the first 3D model of the patient’s organ to a user.

139. The method of claim 138, wherein generating the second 3D model of the patient’s organ comprises: generating a second map by re-calculating the level of uncertainty at each location of the plurality of locations on the second 3D model of the patient’s organ; overlaying the second map onto the second 3D model of the patient’s organ; and displaying, using the display device, the second 3D model of the patient’s organ to the user.

140. The method of claim 138, wherein receiving the second set of images of the patient’s organ comprises: 282 Attorney Docket No.1518-103PCT1 identifying, on the first map, the high uncertainty region of the first second 3D model of patient’s organ by comparing the level of uncertainty at each location of the plurality of locations to a pre-determined uncertainty threshold.

141. The method of claim 139, wherein each one of the first map and the second map comprises a color-coded heat map configured to visualize one or more areas of uncertainty on the first 3D model and second 3D model of the patient’s organ respectively.

142. An apparatus for generating a three-dimensional (3D) model of cardiac anatomy with an overlay, wherein the apparatus comprises: at least a process; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a set of images of a cardiac anatomy pertaining to a subject; generate a set of shape parameters based on the set of images, wherein generating the set of shape parameters comprises generating the set of shape parameters as a function of the set of images and a shape identification model; generate a 3D model of the cardiac anatomy based on the set of shape parameters; generate a map by determining a level of uncertainty at each location of a plurality of locations on the generated 3D model; and overlay an image from the set of images onto the 3D model.

143. The apparatus of claim 142, wherein generating the set of shape parameters further comprises: inputting the set of images into the shape identification model; and wherein the shape identification model has been trained using cardiac geometry training data comprising a plurality of image sets as input correlated to a plurality of shape parameter sets as output.

144. The apparatus of claim 143, wherein generating the set of shape parameters further comprises: receiving the cardiac geometry training data comprising the plurality of image sets as input correlated to the plurality of shape parameter sets as output; and 283 Attorney Docket No.1518-103PCT1 training the shape identification model using the cardiac geometry training data.

145. The apparatus of claim 142, wherein the set of shape parameters comprises a plurality of numerical descriptors representing at least a geometric characteristic of the cardiac anatomy.

146. The apparatus of claim 142, wherein each shape parameter within the set of shape parameters comprises a corresponding parameter range.

147. The apparatus of claim 142, wherein receiving the set of images comprises receiving the set of images from a patient profile.

148. The apparatus of claim 142, further comprising receiving cardiac anatomy training data, wherein receiving the cardiac anatomy training data comprises generating the cardiac anatomy training data using a synthetic ICE data generator.

149. The apparatus of claim 142, wherein the instructions further configured to the at least a processor to overlay the map onto the 3D model.

150. The apparatus of claim 149, wherein the map comprises a color-coded heat map configured to visualize one or more areas of uncertainty on the 3D model.

151. The apparatus of claim 149, wherein overlaying the 3D model with the map comprises utilizing interactive visualization techniques configured to allow user-mediated augmentation of the set of images of cardiac anatomy.

152. The apparatus of claim 142, wherein generating the 3D model further comprises generating a second 3D model as a function of the 3D model, by varying the set of shape parameters, wherein the second 3D model is statistically constrained.

153. The apparatus of claim 142, wherein overlaying the map onto the 3D model comprises overlaying an ICE frame to a corresponding location of the 3D model.

154. A method for generating a three-dimensional (3D) model of cardiac anatomy with an overlay, wherein the method comprises: receiving, by a processor, a set of images of a cardiac anatomy pertaining to a subject; generating, by the processor, a set of shape parameters based on the set of images, wherein generating the set of shape parameters comprises: generating the set of shape parameters using the set of images and a shape identification model; 284 Attorney Docket No.1518-103PCT1 generating, by the processor, a 3D model of the cardiac anatomy based on the set of shape parameters; generating, by the processor, a map by determining a level of uncertainty at each location of a plurality of locations on the generated 3D model; and overlaying, by the processor, an image from the set of images onto the 3D model.

155. The method of claim 154, wherein generating the set of shape parameters further comprises: inputting the set of images into the shape identification model; and wherein the shape identification model has been trained using cardiac geometry training data comprising a plurality of image sets as input correlated to a plurality of shape parameter sets as output.

156. The method of claim 155, wherein generating the set of shape parameters further comprises: receiving the cardiac geometry training data comprising the plurality of image sets as input correlated to the plurality of shape parameter sets as output; and training the shape identification model using the cardiac geometry training data.

157. The method of claim 154, wherein the set of shape parameters comprises a plurality of numerical descriptors representing at least a geometric characteristic of the cardiac anatomy.

158. The method of claim 154, wherein each shape parameter within the set of shape parameters comprises a corresponding parameter range.

159. The method of claim 154, wherein receiving the set of images comprises receiving the set of images from a patient profile.

160. The method of claim 154, further comprising receiving cardiac anatomy training data wherein receiving the cardiac anatomy training data comprises generating the cardiac anatomy training data using a synthetic ICE data generator.

161. The method of claim 154, further comprising overlay, using the at least a processor, the map onto the 3D model.

162. The method of claim 161, wherein the map comprises a color-coded heat map configured to visualize one or more areas of uncertainty on the 3D model. 285 Attorney Docket No.1518-103PCT1

163. The method of claim 161, wherein overlaying the 3D model with the map comprises utilizing interactive visualization techniques configured to allow user-mediated augmentation of the set of images of cardiac anatomy.

164. The method of claim 154, wherein generating the 3D model further comprises generating a second 3D model as a function of the 3D model, by varying the set of shape parameters, wherein the second 3D model is statistically constrained.

165. The method of claim 154, wherein overlaying the 3D model comprises overlaying an ICE frame to a corresponding location of the 3D model.

166. An apparatus for generating a three-dimensional (3D) model with an overlay, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a set of ultrasonic images of an organ of a subject; generate a set of shape parameters representing the organ’s shape as a function of the set of ultrasonic images and a shape identification model trained on a training dataset comprising historical ultrasonic images correlated with historical computed tomography scan data; generate a 3D model of the organ based on the set of shape parameters; generate a map by determining a level of uncertainty at each location of a plurality of locations on the 3D model; and overlay the map onto the 3D model.

167. The apparatus of claim 166, wherein the set of ultrasonic images of the organ comprises an image selected from a list consisting of a transesophageal echocardiogram image, a transthoracic echocardiogram image, and a point-of-care ultrasound image.

168. The apparatus of claim 166, wherein: the memory contains instructions configuring the at least a processor to identify the training dataset; the memory contains instructions configuring the at least a processor to train the shape identification model on the training dataset; and 286 Attorney Docket No.1518-103PCT1 identifying the training dataset comprises correlating an instance of computed tomography scan data with a historical ultrasonic image as a function of a medical record and a language model.

169. The apparatus of claim 166, wherein: the memory contains instructions configuring the at least a processor to identify the training dataset; the memory contains instructions configuring the at least a processor to train the shape identification model on the training dataset; and identifying the training dataset comprises generating a synthetic ultrasonic image as a function of an instance of computed tomography scan data.

170. The apparatus of claim 166, wherein the memory contains instructions configuring the at least a processor to determine a Left Atrial Appendage Occlusion Device placement as a function of the 3D model.

171. The apparatus of claim 166, wherein the set of shape parameters comprises a plurality of numerical descriptors representing at least a geometric characteristic of the organ.

172. The apparatus of claim 166, wherein each shape parameter within the set of shape parameters is associated with a corresponding parameter range.

173. The apparatus of claim 166, wherein receiving the set of ultrasonic images comprises receiving the set of ultrasonic images from a patient profile.

174. The apparatus of claim 166, wherein the map comprises a color-coded heat map configured to visualize one or more areas of uncertainty on the 3D model.

175. The apparatus of claim 166, wherein generating the 3D model further comprises generating a second 3D model as a function of the 3D model, by varying the set of shape parameters, wherein the second 3D model is statistically constrained.

176. A method of generating a three-dimensional (3D) model with an overlay, wherein the method comprises: using at least a processor, receiving a set of ultrasonic images of an organ of a subject; using the at least a processor, generating a set of shape parameters representing the organ’s shape as a function of the set of ultrasonic images and a shape identification model trained on a training dataset comprising historical ultrasonic images correlated with historical computed tomography scan data; 287 Attorney Docket No.1518-103PCT1 using the at least a processor, generating a 3D model of the organ based on the set of shape parameters; using the at least a processor, generating a map by determining a level of uncertainty at each location of a plurality of locations on the 3D model; and using the at least a processor, overlaying the map onto the 3D model.

177. The method of claim 176, wherein the set of ultrasonic images of the organ comprises an image selected from a list consisting of a transesophageal echocardiogram image, a transthoracic echocardiogram image, and a point-of-care ultrasound image.

178. The method of claim 176, wherein: the method further comprises identifying the training dataset; the method further comprises training the shape identification model on the training dataset; and identifying the training dataset comprises correlating an instance of computed tomography scan data with a historical ultrasonic image as a function of a medical record and a language model.

179. The method of claim 176, wherein: the method further comprises identifying the training dataset; the method further comprises training the shape identification model on the training dataset; and identifying the training dataset comprises generating a synthetic ultrasonic image as a function of an instance of computed tomography scan data.

180. The method of claim 176, wherein the method further comprises determining a Left Atrial Appendage Occlusion Device placement as a function of the 3D model.

181. The method of claim 176, wherein the set of shape parameters comprises a plurality of numerical descriptors representing at least a geometric characteristic of the organ.

182. The method of claim 176, wherein each shape parameter within the set of shape parameters is associated with a corresponding parameter range.

183. The method of claim 176, wherein receiving the set of ultrasonic images comprises receiving the set of ultrasonic images from a patient profile.

184. The method of claim 176, wherein the map comprises a color-coded heat map configured to visualize one or more areas of uncertainty on the 3D model. 288 Attorney Docket No.1518-103PCT1

185. The method of claim 176, wherein generating the 3D model further comprises generating a second 3D model as a function of the 3D model, by varying the set of shape parameters, wherein the second 3D model is statistically constrained.

186. An apparatus that provides visualization within a three-dimensional (3D) model, the apparatus comprising: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a query image; extract neural network encodings as a function of the received query image; query a synthetic image repository for at least a matching synthetic image based on the extracted neural network encodings, wherein: the synthetic image repository comprises a plurality of synthetic images, wherein neural network encodings are extracted as a function of each synthetic image within the plurality of synthetic images; each synthetic image within the plurality of synthetic images corresponds to a region of interest in a 3D model; and querying the synthetic image repository comprises comparing the extracted neural network encodings of the query image with the extracted neural network encodings of each synthetic image within the plurality of synthetic images; and display an estimated region of interest within the 3D model by positioning the query image as a function of the at least a matching synthetic image.

187. The apparatus of claim 186, wherein: the query image comprises a query intracardiac echocardiography (ICE) frame; and the plurality of synthetic images comprises a plurality of synthetic ICE frames.

188. The apparatus of claim 186, wherein the 3D model is constructed based on a patient profile, wherein the patient profile comprises a plurality of heart images and associated metadata.

189. The apparatus of claim 186, wherein the plurality of synthetic images is generated by executing a camera transformation program configured to simulate at least a perspective 289 Attorney Docket No.1518-103PCT1 of an image capture device.

190. The apparatus of claim 189, wherein executing the camera transformation program comprises generating at least a two-dimensional (2D) projection of a structure by rendering the region of interest as a function of a set of imaging parameters using a virtual camera positioned at the region of interest.

191. The apparatus of claim 190, wherein at least a synthetic image within the plurality of synthetic images is generated using a generative model.

192. The apparatus of claim 191, wherein generating the at least a synthetic image using a generative model comprises: receiving image translation training data comprising a plurality of training images and a plurality of training 2D projections; training an image translation model by correlating the plurality of training images with the plurality of training 2D projections; and synthesizing the at least a synthetic image as a function of the at least a 2D projection using the trained image translation model.

193. The apparatus of claim 186, wherein the 3D model is constructed from a plurality of computed tomography (CT) scans.

194. The apparatus of claim 186, wherein the 3D model is constructed using a plurality of magnetic resonance imaging (MRI) scans.

195. The apparatus of claim 186, wherein the 3D model is constructed using a plurality of transthoracic echocardiogram (TTE) frames.

196. The apparatus of claim 186, wherein displaying the estimated region of interest within the 3D model comprises overlaying a two-dimensional (2D) cross section comprising the estimated region of interest of the query image within at least a portion of the 3D model.

197. The apparatus of claim 186, wherein the at least a processor is further configured to: receive at least a supplemental query image; and iteratively update the estimated region of interest as a function of the at least a supplemental query image.

198. A method that provides visualization within a 3D model, the method comprising: receiving, by at least a processor, a query image; extracting, by the at least a processor, neural network encodings as a function of the 290 Attorney Docket No.1518-103PCT1 received query image; querying, by the at least a processor, a synthetic image repository for at least a matching synthetic image based on the extracted neural network encodings, wherein: the synthetic image repository comprises a plurality of synthetic images, wherein neural network encodings are extracted as a function of each synthetic image within the plurality of synthetic images; each synthetic image within the plurality of synthetic images corresponds to a region of interest in a 3D model; and querying the synthetic image repository comprises comparing the extracted neural network encodings of the query image with extracted neural network encodings of each synthetic image within the plurality of synthetic images; and displaying, by the at least a processor, an estimated region of interest within the 3D model by positioning the query image as a function of the at least a matching synthetic image.

199. The method of claim 198, wherein: the query image comprises a query ICE frame; and the plurality of synthetic images comprises a plurality of synthetic ICE frames.

200. The method of claim 198, wherein the 3D model is constructed based on a patient profile, wherein the patient profile comprises a plurality of heart images and associated metadata.

201. The method of claim 198, wherein the plurality of synthetic images is generated by executing a camera transformation program configured to simulate at least a perspective of an image capture device.

202. The method of claim 201, wherein executing the camera transformation program comprises generating at least a two-dimensional (2D) projection of a structure by rendering the region of interest as a function of a set of imaging parameters using a virtual camera positioned at the region of interest.

203. The method of claim 202, wherein at least a synthetic image within the plurality of synthetic images is generated using a generative model.

204. The method of claim 203, wherein generating the at least a synthetic image using a generative model comprises: 291 Attorney Docket No.1518-103PCT1 receiving image translation training data comprising a plurality of training images and a plurality of training 2D projections; training an image translation model by correlating the plurality of training images with the plurality of training 2D projections; and synthesizing the at least a synthetic image as a function of the at least a 2D projection using the trained image translation model.

205. The method of claim 198, wherein the 3D model is constructed using a plurality of computed tomography (CT) scans.

206. The method of claim 198, wherein the 3D model is constructed using a plurality of magnetic resonance imaging (MRI) scans.

207. The method of claim 198, wherein the 3D model is constructed using a plurality of transthoracic echocardiogram (TTE) frames.

208. The method of claim 198, wherein displaying the estimated region of interest within the 3D model comprises overlaying a 2D cross section comprising the estimated region of interest of the query image within at least a portion of the 3D model.

209. The method of claim 198, wherein the method further comprises: receiving at least a supplemental query image; and iteratively updating the estimated region of interest as a function of the at least a supplemental query image. 292 Attorney Docket No.1518-103PCT1