WO2023132392A1 - Method and system for analyzing blood flow characteristics in carotid artery by means of particle-based simulation - Google Patents

Abstract

A method, for analyzing blood flow characteristics in a carotid artery, executed by a computing device according to one embodiment of the present disclosure comprises the steps of: obtaining an image of a patient's carotid artery; generating a 3D image simulating the shape of the carotid artery on the basis of the image of the carotid artery; receiving information relating to the blood flow of the patient; performing a simulation using a plurality of particles in the 3D image corresponding to the carotid artery on the basis of the received information relating to the user's blood flow; and determining the information relating to the blood flow characteristics in the carotid artery on the basis of the result of the simulation performed.

Description

Method and system for analyzing blood flow characteristics in carotid artery through particle-based simulation

The present invention relates to a method and system for analyzing characteristics in a carotid artery through particle-based simulation, and specifically, to perform a simulation using a plurality of particles in a 3D image corresponding to information on a patient's blood flow and carotid artery It relates to methods and systems.

Recently, a technique for analyzing blood vessel characteristics in a patient using an image of the patient has been developed. To this end, conventional techniques have attempted to analyze the shape of blood vessels in the patient through images of the patient and discover vascular diseases through the analysis results. The vascular diseases thus discovered could be provided to doctors, patients, pathologists, and the like.

However, it is difficult to analyze the flow of blood in the blood vessel only with the shape of the blood vessel analyzed according to the existing technique. In particular, blood flowing in blood vessels is a fluid, and even if the blood vessels have a similar shape, they may flow differently within the blood vessels. That is, it is difficult to predict blood flow characteristics only with the shape of a blood vessel, and it is difficult to predict a patient's disease determined according to blood flow characteristics.

The present disclosure provides a method for analyzing blood flow characteristics in a carotid artery through particle-based simulation, a computer program stored in a recording medium, and a system (device) to solve the above problems.

The present disclosure may be implemented in a variety of ways, including a method, apparatus (system) or computer program stored on a readable storage medium.

According to an embodiment of the present disclosure, a method for analyzing blood flow characteristics in a carotid artery through particle-based simulation, performed by at least one processor of an information processing system, includes acquiring an image of a patient's carotid artery; Generating a 3D image that simulates the shape of the carotid artery based on the image of the carotid artery, receiving information about blood flow in the patient, and generating a 3D image corresponding to the carotid artery based on the received information about blood flow in the patient. The method includes performing a simulation using a plurality of particles and determining information about blood flow characteristics in the carotid artery based on a result of the performed simulation.

According to one embodiment of the present disclosure, the determining of information on the determined blood flow characteristics in the carotid artery includes determining pathological information in the carotid artery.

According to an embodiment of the present disclosure, the pathological information includes at least one of information on at least one stenotic region within the carotid artery, information on wall shear stress given to the inner wall of the carotid artery, or dynamics within the carotid artery. .

According to an embodiment of the present disclosure, determining information on blood flow characteristics in the carotid artery includes inferring pathological information in the carotid artery from the determined blood flow characteristics in the carotid artery using a first machine learning model. Here, the first machine learning model is learned to output reference pathological information of a plurality of reference patients from a plurality of reference blood flow characteristics of a plurality of reference patients.

According to an embodiment of the present disclosure, the determining of information on blood flow characteristics in the carotid artery includes outputting information about blood flow characteristics in the carotid artery from a result of a simulation performed using a second machine learning model. . In addition, the step of determining the information on the characteristics of blood flow in the carotid artery may include receiving biometric information of the patient and using the third machine learning model to determine the biometric information of the patient and the output information about the characteristics of blood flow in the carotid artery. and outputting pathological information within.

According to one embodiment of the present disclosure, the information on the blood flow in the patient includes at least one of blood flow velocity, blood flow amount, and blood pressure in a specific region in the patient's carotid artery.

According to one embodiment of the present disclosure, at least one of blood flow velocity, blood flow amount, or blood pressure in a specific region in a patient's carotid artery is set to have a value within a predetermined region.

According to one embodiment of the present disclosure, generating a 3D image includes extending at least a partial region of the carotid artery and adding a structure node outside the carotid artery.

According to an embodiment of the present disclosure, the step of performing a simulation using a plurality of particles in the 3D image corresponding to the carotid artery may include moving the plurality of particles in the carotid artery with respect to the contracted or expanded carotid artery according to the patient's heartbeat. and performing the simulation by putting

According to one embodiment of the present disclosure, the step of determining information on blood flow characteristics in the carotid artery includes analyzing the information on the blood flow characteristics in the carotid artery in real time by analyzing motions of a plurality of particles moving in the carotid artery. do.

According to one embodiment of the present disclosure, a computer program stored in a computer readable recording medium is provided to execute a method of analyzing blood flow characteristics in a carotid artery in a computer.

According to another embodiment of the present disclosure, a system for analyzing characteristics of blood flow in a carotid artery acquires an image of a patient's carotid artery by a memory storing one or more instructions and executing one or more instructions of the memory, and Based on the image, a 3D image that simulates the shape of the carotid artery is generated, information on the blood flow in the patient is received, and a plurality of particles are used in the 3D image corresponding to the carotid artery based on the received information on the patient's blood flow. and at least one processor configured to perform a simulation and determine information about characteristics of blood flow in the carotid artery based on a result of the performed simulation.

According to some embodiments of the present disclosure, since blood flow characteristics in the carotid artery are analyzed through simulation in a 3D image corresponding to the carotid artery as well as the shape of the carotid artery, the blood flow characteristics in the carotid artery can be more accurately analyzed.

According to some embodiments of the present disclosure, blood flow characteristics may be determined through particle-based simulation in a 3D image corresponding to a carotid artery. Accordingly, even if blood vessel contraction/expansion occurs according to heartbeat, unlike grid-based simulation, it is possible to simulate by inserting particles so as to be movable in the blood vessel. Blood flow analysis is possible without the need to recreate the shape. Furthermore, it is possible to provide more realistic simulation results (digital twin) through such particle-based simulation, and such simulation results can be generated in real time.

The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned are clear to those skilled in the art (referred to as "ordinary technicians") from the description of the claims. will be understandable.

Embodiments of the present disclosure will be described with reference to the accompanying drawings described below, wherein like reference numbers indicate like elements, but are not limited thereto.

1 is a diagram illustrating an example of a process of analyzing blood flow characteristics in a carotid artery according to an embodiment of the present disclosure.

2 is a schematic diagram illustrating a configuration in which an information processing system according to an embodiment of the present disclosure is communicatively connected with a plurality of user terminals.

3 is a block diagram showing internal configurations of a user terminal and an information processing system according to an embodiment of the present disclosure.

4 is a diagram showing an internal configuration of a processor of an information processing system according to an embodiment of the present disclosure.

5 is a diagram illustrating an example of a method of determining pathological information using a first machine learning model according to an embodiment of the present disclosure.

6 is a diagram illustrating an example of a method for determining pathological information using a second machine learning model and a third machine learning model according to another embodiment of the present disclosure.

7 is a diagram illustrating an example of a machine learning model according to an embodiment of the present disclosure.

8 is a diagram illustrating an example of a method of pre-processing a 3D image before performing a particle-based simulation according to an embodiment of the present disclosure.

9 is an exemplary diagram illustrating a process of 3D particle-based simulation according to an embodiment of the present disclosure.

10 is a flowchart illustrating an example of a method for analyzing blood flow characteristics in a carotid artery according to an embodiment of the present disclosure.

Hereinafter, specific details for the implementation of the present disclosure will be described in detail with reference to the accompanying drawings. However, in the following description, if there is a risk of unnecessarily obscuring the gist of the present disclosure, detailed descriptions of well-known functions or configurations will be omitted.

In the accompanying drawings, identical or corresponding elements are given the same reference numerals. In addition, in the description of the following embodiments, overlapping descriptions of the same or corresponding components may be omitted. However, omission of a description of a component does not intend that such a component is not included in an embodiment.

Advantages and features of the disclosed embodiments, and methods of achieving them, will become apparent with reference to the following embodiments in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below and may be implemented in various different forms, only the present embodiments make the present disclosure complete, and the present disclosure fully covers the scope of the invention to those skilled in the art. It is provided only to inform you.

Terms used in this specification will be briefly described, and the disclosed embodiments will be described in detail. The terms used in this specification have been selected from general terms that are currently widely used as much as possible while considering the functions in the present disclosure, but they may vary according to the intention of a person skilled in the related field, a precedent, or the emergence of new technology. In addition, in a specific case, there is also a term arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the invention. Therefore, terms used in the present disclosure should be defined based on the meaning of the term and the general content of the present disclosure, not simply the name of the term.

Expressions in the singular number in this specification include plural expressions unless the context clearly dictates that they are singular. Also, plural expressions include singular expressions unless the context clearly specifies that they are plural. When it is said that a certain part "includes" a certain component throughout the specification, it means that it may further include other components without excluding other components unless otherwise stated.

Also, the term 'module' or 'unit' used in the specification means a software or hardware component, and the 'module' or 'unit' performs certain roles. However, 'module' or 'unit' is not meant to be limited to software or hardware. A 'module' or 'unit' may be configured to reside in an addressable storage medium and may be configured to reproduce one or more processors. Thus, as an example, 'module' or 'unit' refers to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, It may include at least one of procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, or variables. Functions provided within components and 'modules' or 'units' may be combined into a smaller number of components and 'modules' or 'units', or further components and 'modules' or 'units'. can be further separated.

According to an embodiment of the present disclosure, a 'module' or 'unit' may be implemented as a processor and a memory. 'Processor' should be interpreted broadly to include general-purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), controllers, microcontrollers, state machines, and the like. In some circumstances, 'processor' may refer to an application specific integrated circuit (ASIC), programmable logic device (PLD), field programmable gate array (FPGA), or the like. 'Processor' refers to a combination of processing devices, such as, for example, a combination of a DSP and a microprocessor, a combination of a plurality of microprocessors, a combination of one or more microprocessors in conjunction with a DSP core, or a combination of any other such configurations. You may. Also, 'memory' should be interpreted broadly to include any electronic component capable of storing electronic information. 'Memory' includes random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable-programmable read-only memory (EPROM), It may also refer to various types of processor-readable media, such as electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage, registers, and the like. A memory is said to be in electronic communication with the processor if the processor can read information from and/or write information to the memory. Memory integrated with the processor is in electronic communication with the processor.

In the present disclosure, a 'system' may include at least one of a server device and a cloud device, but is not limited thereto. For example, a system may consist of one or more server devices. As another example, a system may consist of one or more cloud devices. As another example, the system may be operated by configuring a server device and a cloud device together.

In the present disclosure, a 'machine learning model' may include any model used to infer an answer to a given input. According to one embodiment, the machine learning model may include an artificial neural network model including an input layer (layer), a plurality of hidden layers, and an output layer. Here, each layer may include one or more nodes. For example, the first machine learning model may be trained to output pathological information in the carotid artery from the determined blood flow characteristics in the carotid artery. In another example, the second machine learning model may be trained to output information about characteristics of blood flow in the carotid artery from the result of the performed simulation. In another example, the third machine learning model may be trained to output pathological information within the carotid artery from the received biometric information of the patient and the blood flow information output from the second machine learning model. Also, the machine learning model may include weights associated with a plurality of nodes included in the machine learning model. Here, the weight may include any parameter related to the machine learning model. In the present disclosure, a machine learning model may refer to an artificial neural network model, and an artificial neural network model may refer to a machine learning model. A machine learning model according to the present disclosure may be a model learned using various learning methods. For example, various learning methods such as supervised learning, unsupervised learning, and reinforcement learning may be used in the present disclosure.

In the present disclosure, 'learning' refers to a machine learning model using learning data including at least one of blood flow characteristics in the patient's carotid artery, particle-based simulation results in the carotid artery, biometric information of the patient, and pathological information of the patient. It can refer to any process that changes the included weights. According to an embodiment, learning may refer to a process of changing or updating weights associated with a machine learning model through one or more forward propagation and backward propagation of the machine learning model using learning data. there is.

In the present disclosure, 'each of a plurality of A' or 'each of a plurality of A' may refer to each of all components included in a plurality of A's, or each of some components included in a plurality of A's. .

In the present disclosure, 'similar' may include all meanings of the same or similar. For example, that two pieces of information are similar may indicate that the two pieces of information are the same or similar to each other.

In the present disclosure, an 'instruction' may refer to a component of a computer program and executed by a processor as a series of instructions grouped on the basis of a function.

In this disclosure, 'image' may refer to one or more images. Also, an image may refer to a video, and a video may refer to an image.

1 is a diagram illustrating an example of a process of analyzing blood flow characteristics in a carotid artery according to an embodiment of the present disclosure. An information processing system (not shown) may obtain an image 110 of the patient's carotid artery. Here, the image of the patient's carotid artery may include one or more 2D images using X-rays and the like, and 3D images captured using computer tomography (CT) and magnetic resonance imaging (MRI).

According to one embodiment, the information processing system may generate a 3D image 120 simulating the shape of the carotid artery based on the obtained image of the carotid artery. The 3D image 120 may be generated from a 2D image or a 3D image obtained by capturing at least a portion of the carotid artery. These 3D images 120 can be used to perform particle-based simulations.

The information processing system may receive information 130 about blood flow in the patient. The information 130 on the blood flow inside the patient may represent any information representing the characteristics of the blood flow in any blood vessel of the patient, but is not limited thereto, but, for example, the patient's blood pressure, blood flow speed, blood flow amount, etc. can include The information 130 on the blood flow inside the patient may be obtained by an invasive method or a non-invasive method.

According to an embodiment, the information 130 on blood flow within the patient may include information on at least one of a blood flow rate, blood flow amount, and blood pressure in a specific region of the patient's carotid artery. Here, at least one of blood flow rate, blood flow, or blood pressure in a specific region of the patient's carotid artery may be set to have a value within a predetermined region.

Then, the information processing system may perform a simulation 140 using a plurality of particles in the 3D image 120 in the carotid artery based on the obtained information 130 on the patient's blood flow. That is, the information processing system may generate a digital twin corresponding to the patient's carotid artery. In order to generate such a simulation result (digital twin), the information processing system can simulate the carotid artery by displaying it based on particles instead of expressing it as a grid. For example, such a particle-based simulation method may include Smoothed Particle Hydrodynamics (SPH), Moving Particle Semi-Implicit (MPS), and Lattice Boltzmann Method (LBM), but is not limited thereto. For example, an LBM-based simulation method, which is one of these particle-based methods, can predict blood flow in the carotid artery using the probability distribution function of virtual particles on a lattice. This blood flow (fluid) analysis method reduces the amount of calculation. However, accurate results can be obtained. In particular, analysis of complicated boundaries such as blood flow or multi-phase flows can be easily analyzed.

According to an embodiment, the information processing system may determine information 150 about blood flow characteristics in the carotid artery based on the performed simulation result. Here, the information 150 on blood flow characteristics in the carotid artery may refer to any information indicating characteristics of blood flow in the carotid artery. For example, the information on the characteristics of blood flow in the carotid artery 150 may include information about the shape of the carotid artery obtained from the generated 3D image, blood flow velocity, blood flow, blood pressure, and/or FFR in the carotid artery predicted through particle-based simulation. (Fractional flow reserve) information, etc. may be included, but is not limited thereto.

2 is a schematic diagram illustrating a configuration in which an information processing system according to an embodiment of the present disclosure is communicatively connected with a plurality of user terminals. As shown, the plurality of user terminals 210_1 , 210_2 , and 210_3 may be connected to an information processing system 230 capable of providing a blood flow characteristic analysis service in the carotid artery through a network 220 . Here, the plurality of user terminals 210_1 , 210_2 , and 210_3 may include terminals of users (doctors, pathologists, patients, etc.) to be provided with a service providing blood flow characteristics and/or pathological information in the carotid artery. In one embodiment, the information processing system 230 may store, provide, and execute one or more computer-executable programs (eg, downloadable applications) and data related to a blood flow analysis program in the carotid artery, a particle-based simulation program, and the like. It may include a server device and/or database, or one or more distributed computing devices and/or distributed database based on a cloud computing service.

The service for analyzing blood flow in the carotid artery provided by the information processing system 230 may be provided to the user through a blood flow analysis application in the carotid artery installed in each of the plurality of user terminals 210_1 , 210_2 , and 210_3 . For example, the information processing system 230 provides information corresponding to an image analysis request for a patient's carotid artery received from the user terminals 210_1, 210_2, and 210_3 through a blood flow analysis application in the carotid artery, or processing corresponding thereto. can be performed.

A plurality of user terminals 210_1 , 210_2 , and 210_3 may communicate with the information processing system 230 through the network 220 . The network 220 may be configured to enable communication between the plurality of user terminals 210_1, 210_2, and 210_3 and the information processing system 230. Depending on the installation environment, the network 220 may be, for example, a wired network such as Ethernet, a wired home network (Power Line Communication), a telephone line communication device and RS-serial communication, a mobile communication network, a wireless LAN (WLAN), It may consist of a wireless network such as Wi-Fi, Bluetooth, and ZigBee, or a combination thereof. The communication method is not limited, and the user terminals (210_1, 210_2, 210_3) as well as a communication method utilizing a communication network (eg, mobile communication network, wired Internet, wireless Internet, broadcasting network, satellite network, etc.) that the network 220 may include ), short-range wireless communication between them may also be included.

In FIG. 2, a mobile phone terminal 210_1, a tablet terminal 210_2, and a PC terminal 210_3 are illustrated as examples of user terminals, but are not limited thereto, and the user terminals 210_1, 210_2, and 210_3 may perform wired and/or wireless communication. It may be any computing device capable of this and capable of installing and running an application for analyzing characteristics of blood flow in the carotid artery. For example, the user terminal includes a smartphone, a mobile phone, a navigation device, a computer, a laptop computer, a digital broadcasting terminal, a PDA (Personal Digital Assistants), a PMP (Portable Multimedia Player), a tablet PC, a game console, an AI speaker, It may include a wearable device, an internet of things (IoT) device, a virtual reality (VR) device, an augmented reality (AR) device, a set-top box, and the like. In addition, although three user terminals 210_1, 210_2, and 210_3 are shown in FIG. 2 to communicate with the information processing system 230 through the network 220, it is not limited thereto, and a different number of user terminals may be connected to the network ( 220) to communicate with the information processing system 230.

According to an embodiment, the information processing system 230 may receive an image of the patient's carotid artery and a request for analyzing blood flow in the carotid artery from the plurality of user terminals 210_1, 210_2, and 210_3. Then, the information processing system 230 performs a particle-based simulation on the received image of the carotid artery to generate information on blood flow characteristics in the carotid artery and/or pathology information in the carotid artery, thereby generating a plurality of user terminals 210_1, 210_2, 210_3).

3 is a block diagram showing internal configurations of a user terminal and an information processing system according to an embodiment of the present disclosure. The user terminal 210 may refer to any computing device capable of executing a blood flow analysis application and the like in the carotid artery and capable of wired/wireless communication, for example, a mobile phone terminal 210_1 of FIG. 2, a tablet terminal ( 210_2), a PC terminal 210_3, and the like. As shown, the user terminal 210 may include a memory 312 , a processor 314 , a communication module 316 and an input/output interface 318 . Similarly, the information processing system 230 may include a memory 332 , a processor 334 , a communication module 336 and an input/output interface 338 . As shown in FIG. 3, the user terminal 210 and the information processing system 230 are configured to communicate information and/or data through the network 220 using respective communication modules 316 and 336. It can be. In addition, the input/output device 320 may be configured to input information and/or data to the user terminal 210 through the input/output interface 318 or output information and/or data generated from the user terminal 210.

The memories 312 and 332 may include any non-transitory computer readable media. According to one embodiment, the memories 312 and 332 are non-perishable mass storage devices such as random access memory (RAM), read only memory (ROM), disk drives, solid state drives (SSDs), flash memory, and the like. (permanent mass storage device) may be included. As another example, a non-perishable mass storage device such as a ROM, SSD, flash memory, or disk drive may be included in the user terminal 210 or the information processing system 230 as a separate permanent storage device separate from memory. In addition, the memories 312 and 332 may store an operating system and at least one program code (eg, a code for an application for analyzing blood flow in the carotid artery that is installed and driven in the user terminal 210 ).

These software components may be loaded from a computer readable recording medium separate from the memories 312 and 332 . A recording medium readable by such a separate computer may include a recording medium directly connectable to the user terminal 210 and the information processing system 230, for example, a floppy drive, a disk, a tape, a DVD/CD- It may include a computer-readable recording medium such as a ROM drive and a memory card. As another example, software components may be loaded into the memories 312 and 332 through a communication module rather than a computer-readable recording medium. For example, at least one program is loaded into the memories 312 and 332 based on a computer program installed by files provided by developers or a file distribution system that distributes application installation files through the network 220 . It can be.

The processors 314 and 334 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. Instructions may be provided to processors 314 and 334 by memories 312 and 332 or communication modules 316 and 336 . For example, processors 314 and 334 may be configured to execute instructions received according to program codes stored in a recording device such as memory 312 and 332 .

The communication modules 316 and 336 may provide configurations or functions for the user terminal 210 and the information processing system 230 to communicate with each other through the network 220, and the user terminal 210 and/or information processing. System 230 may provide configurations or functions for communicating with other user terminals or other systems (eg, separate cloud systems, etc.). For example, a request or data generated by the processor 314 of the user terminal 210 according to a program code stored in a recording device such as the memory 312 (eg, an image of a patient's carotid artery and/or a carotid artery) Blood flow analysis request, etc.) may be transmitted to the information processing system 230 through the network 220 under the control of the communication module 316 . Conversely, a control signal or command provided under the control of the processor 334 of the information processing system 230 passes through the communication module 336 and the network 220 and through the communication module 316 of the user terminal 210. It may be received by the user terminal 210 . For example, the user terminal 210 may receive a result of blood flow analysis in the carotid artery from the information processing system 230 through the communication module 316 .

The input/output interface 318 may be a means for interfacing with the input/output device 320 . As an example, the input device may include a device such as a camera, keyboard, microphone, mouse, etc. including an audio sensor and/or image sensor, and the output device may include a device such as a display, speaker, haptic feedback device, or the like. can As another example, the input/output interface 318 may be a means for interface with a device in which a configuration or function for performing input and output is integrated into one, such as a touch screen. For example, when the processor 314 of the user terminal 210 processes a command of a computer program loaded into the memory 312, information and/or data provided by the information processing system 230 or other user terminals are used. A service screen or the like configured as described above may be displayed on the display through the input/output interface 318 . Although the input/output device 320 is not included in the user terminal 210 in FIG. 3 , it is not limited thereto, and the user terminal 210 and the user terminal 210 may be configured as one device. In addition, the input/output interface 338 of the information processing system 230 is connected to the information processing system 230 or means for interface with a device (not shown) for input or output that the information processing system 230 may include. can be In FIG. 3, the input/output interfaces 318 and 338 are shown as separate elements from the processors 314 and 334, but are not limited thereto, and the input/output interfaces 318 and 338 may be included in the processors 314 and 334. there is.

The user terminal 210 and the information processing system 230 may include more components than those shown in FIG. 3 . However, there is no need to clearly show most of the prior art components. According to one embodiment, the user terminal 210 may be implemented to include at least some of the aforementioned input/output devices 320 . In addition, the user terminal 210 may further include other components such as a transceiver, a global positioning system (GPS) module, a camera, various sensors, and a database. For example, when the user terminal 210 is a smart phone, it may include components that are generally included in a smart phone, for example, an acceleration sensor, a gyro sensor, a camera module, various physical buttons, and a touch screen. Various components such as a button using a panel, an input/output port, and a vibrator for vibration may be implemented to be further included in the user terminal 210 . According to one embodiment, the processor 314 of the user terminal 210 may be configured to operate an application providing a product planning exhibition creation service. At this time, codes related to the application and/or program may be loaded into the memory 312 of the user terminal 210 .

While a program for analysis of blood flow characteristics in the carotid artery is running, the processor 314 uses an input device such as a camera, microphone, etc. including a touch screen, keyboard, audio sensor and/or image sensor connected to the input/output interface 318. Inputted or selected text, image, video, voice and/or action may be received, and the received text, image, video, voice and/or action may be stored in the memory 312 or the communication module 316 and network It can be provided to the information processing system 230 through 220. For example, the processor 314 receives an input representing a user's selection of a blood flow image in the carotid artery. It can be provided to the information processing system 230 through the communication module 316 and the network 220 . As another example, the processor 314 may receive a user input requesting analysis of a blood flow image in the selected carotid artery and provide the received user input to the information processing system 230 through the communication module 316 and the network 220. . As another example, the processor 314 receives user input inputting information about the patient's blood flow through the input device 320, and transmits the information about the patient's blood flow to the network 220 and the communication module 316. It can be provided to the information processing system 230 through.

The processor 314 of the user terminal 210 manages, processes, and/or stores information and/or data received from the input device 320, other user terminals, the information processing system 230, and/or a plurality of external systems. can be configured to Information and/or data processed by processor 314 may be provided to information processing system 230 via communication module 316 and network 220 . The processor 314 of the user terminal 210 may transmit and output information and/or data to the input/output device 320 through the input/output interface 318 . For example, the processor 314 may display the received information and/or data on the screen of the user terminal.

The processor 334 of the information processing system 230 may be configured to manage, process, and/or store information and/or data received from a plurality of user terminals 210 and/or a plurality of external systems. Information and/or data processed by the processor 334 may be provided to the user terminal 210 through the communication module 336 and the network 220 . In one embodiment, the processor 334 of the information processing system 230 performs a particle-based simulation in the received carotid artery image based on the blood flow analysis request received from the user terminal 210 in the patient's carotid artery. analysis results can be generated.

The processor 334 of the information processing system 230 uses the output device 320 such as a display output capable device (eg, a touch screen, a display, etc.) or an audio output capable device (eg, a speaker) of the user terminal 210. It may be configured to output processed information and/or data. For example, the processor 334 of the information processing system 230 transmits the generated blood flow characteristics and/or pathological information generated based on the blood flow characteristics in the patient's carotid artery to the communication module 336 and the network 220. may be provided to the user terminal 210 through and configured to output blood flow characteristics and/or pathological information in the carotid artery through a display output capable device of the user terminal 210.

4 is a diagram showing an internal configuration of a processor 334 of an information processing system according to an embodiment of the present disclosure. According to an embodiment, the processor 334 may include a 3D image generator 410, a simulation performer 420, a blood flow characteristic information generator 430, and a pathological information determiner 440. Although the internal configuration of the processor 334 is described separately for each function in FIG. 4 , it should be noted that this does not necessarily mean that the processor 334 is physically separated. In addition, the internal configuration of the processor 334 shown in FIG. 4 is only an example, and is not illustrated only as an essential configuration. Accordingly, in some embodiments, the processor 334 may be implemented differently, such as additionally including components other than the illustrated internal configurations, or omitting some internal components of the illustrated configurations.

The processor 334 may receive an image of the patient's carotid artery. Here, the image of the patient's carotid artery may be a 2D image or a 3D image including the patient's carotid artery captured by an arbitrary medical imaging device. According to an embodiment, an image of a patient's carotid artery may be received through a communicable storage medium (eg, a hospital system, a local/cloud storage system, etc.) or a user terminal. For example, a 2D image may be captured through an X-ray imaging device, and a 3D image may be obtained through a CT or MRI imaging device.

The 3D image generation unit 410 may generate a 3D image that simulates the shape of the carotid artery based on the image of the carotid artery. According to an embodiment, the 3D image generator 410 may acquire a plurality of 2D images as the image 110 of the patient's carotid artery. In this case, the 3D image generation unit 410 may generate a 3D image that simulates the shape of the carotid artery using the acquired 2D image. To this end, the 3D image generation unit 410 may generate a 3D image that simulates the shape of the carotid artery using a predetermined 3D image generation technique using a plurality of 2D images. For example, a 3D image generation technique may refer to a technique using stereo vision geometry such as epipolar geometry, but is not limited thereto.

In another embodiment, when a 3D image is obtained as an image of the patient's carotid artery, the 3D image generator 410 may extract a plurality of 2D slice images corresponding to the patient's carotid artery in the acquired 3D image. . Then, the 3D image generator 410 may reconstruct a 3D image simulating the patient's carotid artery using the extracted plurality of 2D slice images. The 3D image thus generated may be provided to the simulation performing unit 420 .

The simulation performer 420 may receive a 3D image corresponding to the patient's carotid artery from the 3D image generator 410 . Also, the simulation performer 420 may receive information about blood flow in the patient. Here, information about blood flow in the patient may include information about blood flow and/or information about analysis conditions. For example, information about blood flow in a patient may include at least one of initial density, viscosity, and initial velocity of blood flow.

The simulation performer 420 may receive information about the blood flow in the patient from an arbitrary storage medium or through input from a user terminal. Here, the information on blood flow within the patient may include information on at least one of blood flow velocity, blood flow amount, and blood pressure in a specific region of the patient's carotid artery. Here, the specific region may refer to an arbitrary region located in the patient's carotid artery, but is not limited thereto, for example, CCA (common carotid artery), ICA (internal carotid artery), ECA (external carotid artery) It may refer to the edge area of one side or both sides, and the like.

According to an embodiment, information on at least one of blood flow rate, blood flow amount, and blood pressure in a specific region of the patient's carotid artery may be set to have a value within a predetermined region as an analysis condition for a particle-based simulation. That is, the information on the patient's blood flow may be assumed to be one or more values within a predetermined region, rather than a value actually measured from the patient. Here, the predetermined region may refer to at least one of an average blood flow velocity interval, an average blood flow interval, and an average blood pressure interval of a specific group of patients. A specific group may refer to a group of people without any disease, a group of people with a specific disease (eg, stenosis, etc.), or a group of all people regardless of disease. Accordingly, the simulation performer 420 may receive the set values as information on the blood flow in the patient.

Then, the simulation performing unit 420 may perform a simulation using a plurality of particles in the 3D image of the carotid artery based on the information about the patient's blood flow. For example, a particle may refer to an object having an arbitrary shape that can virtually flow in a blood vessel, but is not limited thereto, but may refer to a bead-shaped object. Here, the bead-shaped object may have an arbitrary particle size that can flow in the carotid artery, for example, 10 microns or 100 microns, but may also be smaller than these.

According to an embodiment, the simulation performer 420 may perform a simulation within a 3D image corresponding to the carotid artery through LBM-based fluid analysis. For example, the simulation performer 420 may provide information about the patient's blood flow, for example, one side of the patient's common carotid artery (CCA), internal carotid artery (ICA), and/or external carotid artery (ECA) of the patient's carotid artery. and/or information of at least one of blood flow rate, blood flow amount, and blood pressure of both edges may be received. The simulation performer 420 may determine information about a region for calculating and outputting blood flow data in the 3D image corresponding to the carotid artery based on this information. The simulation performer 420 may calculate flow data for a plurality of particles in the area for calculation and perform LBM-based fluid analysis simulation based on the previously determined area for output. Here, portions of each of the plurality of regions for calculation in the carotid artery may overlap each other. Likewise, portions of the plurality of regions for output within the carotid artery may also overlap each other. The result simulated in the 3D image corresponding to the carotid artery, that is, the digital twin, may be provided to the blood flow characteristic information generating unit 430 .

The blood flow characteristic information generation unit 430 may determine information about the blood flow characteristics in the patient's carotid artery based on the simulation result received from the simulation performer 420 . Here, the information on the characteristics of blood flow in the carotid artery includes information on the shape of the carotid artery obtained from the generated 3D image, blood flow rate in the carotid artery predicted through particle-based simulation, blood flow rate, blood pressure, and fractional flow reserve (FFR). At least one of the information may be included.

According to an embodiment, the blood flow characteristic information generating unit 430 may output information about blood flow characteristics in the carotid artery from a result of a simulation performed using a machine learning model. In addition, the machine learning model may be learned to output reference blood flow characteristics in the carotid artery of a corresponding reference patient from a plurality of reference simulation results in 3D images corresponding to each carotid artery of a plurality of reference patients. Information about blood flow characteristics in the carotid artery may be provided to the pathological information determining unit 440 .

The pathological information determination unit 440 may infer pathological information in the carotid artery from blood flow characteristics in the carotid artery using a machine learning model. Here, the machine learning model may be trained to output reference pathological information of a plurality of reference patients from a plurality of reference blood flow characteristics of a plurality of reference patients. Here, the pathological information may include, but is not limited to, at least one of information on at least one stenotic region within the carotid artery, information on wall shear stress given to the inner wall of the carotid artery, or dynamics within the carotid artery.

In another embodiment, the pathological information determining unit 440 may further receive biometric information of the patient. Here, the patient's biometric information may include information such as the patient's age, height, weight, body fat percentage, vessel calcification, and hematocrit, but is not limited thereto. The blood flow characteristic information generating unit 430 may output pathological information within the carotid artery from biometric information of the patient and information on the blood flow characteristics of the patient received using a machine learning model. Here, the machine learning model may be learned to output pathological information in the carotid artery of a corresponding patient from biometric information of each of a plurality of reference patients and information about blood flow characteristics of the corresponding patient.

5 is a diagram illustrating an example of a method of determining pathological information 520 using a first machine learning model 510 according to an embodiment of the present disclosure. The inference process of the first machine learning model 510 shown in FIG. 5 may be performed by a processor (eg, at least one processor of an information processing system or at least one processor of a user terminal). As shown, the first machine learning model 510 may output pathological information 520 based on the information 150 on the patient's blood flow characteristics. In the case of learning, the first machine learning model 510 may be learned so that pathological information of a corresponding patient is inferred from information on blood flow characteristics of each of a plurality of reference patients.

Then, when the processor learns the first machine learning model 510, the information on the blood flow characteristics of a plurality of reference patients is used as learning input data, and the pathological information of the reference patients corresponding to the input blood flow characteristics is the correct answer data. (ground truth). Here, the pathological information may include information about at least one stenotic region within the carotid artery, information about wall shear stress given to the inner wall of the carotid artery, and/or dynamics within the carotid artery.

6 is a diagram illustrating an example of a method of determining pathological information 640 using a second machine learning model 610 and a third machine learning model 630 according to another embodiment of the present disclosure. As shown, pathological information 640 may be generated using the biometric information 620 and the result of the particle-based simulation 140 in the carotid artery of a target patient using two machine learning models. . The reasoning process of the second machine learning model 610 and the third machine learning model 630 shown in FIG. 6 is a processor (eg, at least one processor of an information processing system or at least one processor of a user terminal) can be performed by

According to an embodiment, the second machine learning model 610 may receive a result of the simulation 140 in the carotid artery of a target patient and output information 150 about blood flow characteristics in the carotid artery of the patient. there is. The information 150 on blood flow characteristics may be used as input data of the third machine learning model 630 . Here, when the processor learns the second machine learning model 610, the processor receives reference simulation results in the carotid arteries of a plurality of reference patients as input data, and in the carotid arteries of the reference patients corresponding to the input reference simulation results. Pathological information can be used as ground truth.

The third machine learning model 630 may receive, as input data, information 150 on blood flow characteristics of a target patient and biometric information 620 of the patient, which are outputs from the second machine learning model 610. . The third machine learning model 630 may output pathological information 640 from these input data. Here, the processor receives, as input data, information on reference blood flow characteristics within the carotid arteries of a plurality of reference patients when learning the third machine learning model 630, and references corresponding to the input reference blood flow characteristics. Pathological information within the patient's carotid artery can be used as ground truth.

In FIG. 6 , the second machine learning model 610 and the third machine learning model 630 are described in detail as being distinguished from each other, but are not limited thereto. For example, the second machine learning model 610 and the third machine learning model 630 may be implemented as one machine learning model or two or more machine learning models.

7 is a diagram illustrating an example of a machine learning model according to an embodiment of the present disclosure. According to one embodiment, the machine learning model may refer to the artificial neural network model 700 . For example, at least one of the first machine learning model 510, the second machine learning model 610, and the third machine learning model 630 may be an artificial neural network model. Here, the artificial neural network model 700 is a statistical learning algorithm implemented based on the structure of a biological neural network or a structure that executes the algorithm in machine learning technology and cognitive science.

According to one embodiment, the artificial neural network model 700, as in a biological neural network, is an artificial neuron nodes that form a network by combining synapses, and repeatedly adjusts synaptic weights to obtain the correct response corresponding to a specific input. By learning to reduce the error between the output and the inferred output, it is possible to represent a machine learning model having problem solving ability. For example, the artificial neural network model 700 may include an arbitrary probability model, a neural network model, and the like used in artificial intelligence learning methods such as machine learning and deep learning.

According to an embodiment, the artificial neural network model 700 may include an artificial neural network model configured to receive blood flow characteristics in the carotid artery and output pathological information in the carotid artery. In another embodiment, the artificial neural network model 700 may be configured to receive a simulation result in the carotid artery and output information about blood flow characteristics in the carotid artery. In another embodiment, the artificial neural network model 700 may be configured to output pathological information in the carotid artery by receiving biometric information of the patient and information about blood flow characteristics of the patient.

The artificial neural network model 700 is implemented as a multilayer perceptron (MLP) composed of multilayer nodes and connections between them. The artificial neural network model 700 according to this embodiment may be implemented using one of various artificial neural network model structures including MLP. As shown in FIG. 7, the artificial neural network model 700 includes an input layer 720 that receives an input signal or data 710 from the outside, and an output layer that outputs an output signal or data 750 corresponding to the input data. 740, located between the input layer 720 and the output layer 740, receives signals from the input layer 720, extracts characteristics, and transfers n (where n is a positive integer) to the output layer 740. It is composed of hidden layers 730_1 to 730_n. Here, the output layer 740 receives signals from the hidden layers 730_1 to 730_n and outputs them to the outside.

The learning method of the artificial neural network model 700 includes a supervised learning method that learns to be optimized for problem solving by inputting a teacher signal (correct answer), and an unsupervised learning method that does not require a teacher signal. ) way. In one embodiment, the information processing system uses learning data including blood flow characteristics in the carotid artery of a reference patient and pathological information in the carotid artery of a reference patient to output pathological information in the carotid artery by receiving blood flow characteristics in the carotid artery and outputting pathological information in the carotid artery ( The artificial neural network model 700 may be trained through supervised learning. In another embodiment, the information processing system receives the result of the particle-based simulation in the carotid artery and outputs information on the blood flow characteristics in the carotid artery of the reference patient and the reference simulation result in the carotid artery of the reference patient and the blood flow in the carotid artery of the reference patient. The artificial neural network model 700 may be trained through supervised learning using learning data including information on characteristics. In another embodiment, the information processing system receives the patient's biometric information and information on the patient's blood flow characteristics, and outputs the pathological information in the carotid artery. The artificial neural network model 700 may be trained through supervised learning using information and learning data of pathological information in the patient's carotid artery.

According to an embodiment, an input variable of the artificial neural network model 700 may be information about blood flow characteristics in the carotid artery. For example, an input variable input to the input layer 720 of the artificial neural network model 700 may be a vector 710 representing or characterizing information on blood flow characteristics in the patient's carotid artery. In this case, in response to input of information on blood flow characteristics in the patient's carotid artery, an output variable output from the output layer 740 of the artificial neural network model 700 represents or characterizes pathological information in the patient's carotid artery. vector 750.

In another embodiment, the input variable of the artificial neural network model 700 may be information about blood flow characteristics in the carotid artery. For example, an input variable input to the input layer 720 of the artificial neural network model 700 may be a vector 710 representing or characterizing the result of a particle-based simulation in the patient's carotid artery. . In this case, in response to the input of the result of the particle-based simulation in the patient's carotid artery, the output variable output from the output layer 740 of the artificial neural network model 700 represents information about blood flow characteristics in the carotid artery of the patient. or a characterizing vector 750.

In another embodiment, the input variable of the artificial neural network model 700 may be information about blood flow characteristics in the carotid artery. For example, an input variable input to the input layer 720 of the artificial neural network model 700 may be a vector 710 representing or characterizing biometric information of a patient and information on blood flow characteristics of the patient. can Here, the vector 710 may be one vector representing the patient's biometric information and information on the patient's blood flow characteristics, or may be composed of two vectors representing each of the two pieces of information. In this case, an output variable output from the output layer 740 of the artificial neural network model 700 represents pathological information within the patient's carotid artery in response to input of the patient's biometric information and information on the blood flow characteristics of the patient. or a characterizing vector 750.

In this way, a plurality of output variables corresponding to a plurality of input variables are matched in the input layer 720 and the output layer 740 of the artificial neural network model 700, respectively, and the input layer 720, the hidden layers 730_1 to 730_n and By adjusting the synaptic value between nodes included in the output layer 740, learning can be performed so that a correct output corresponding to a specific input can be extracted. Through this learning process, it is possible to grasp the characteristics hidden in the input variables of the artificial neural network model 700, and the nodes of the artificial neural network model 700 so that the error between the output variable calculated based on the input variable and the target output is reduced. You can adjust the synaptic values (or weights) between them.

8 is a diagram illustrating an example of a method of pre-processing a 3D image before performing a particle-based simulation according to an embodiment of the present disclosure. A processor (eg, a processor of an information processing system and/or a processor of a user terminal) may pre-process the shape of the 3D image before performing a simulation using a plurality of particles in the 3D image corresponding to the carotid artery. According to an embodiment, the processor may extend at least a partial area of the carotid artery included in the 3D image and add a structure node outside the carotid artery.

According to an embodiment, when particle-based simulation is performed using the 3D image 810 including the carotid artery, flow occurs inside and outside the blood vessels included in the carotid artery, so calculation time may increase rapidly. In order to solve this problem, the processor may perform a pre-processing process on the 3D image 810.

As shown, the processor may generate a shape 830 of the carotid artery in which the length of a branch of the carotid artery is extended, among the shapes 820 of the carotid artery included in the 3D image 810 . Then, in order to match the generated carotid artery shape 830 to a 3D image frame having a predetermined size, the processor generates a carotid artery shape 840 in which a part of the generated carotid artery shape 830 is cut, and the cut carotid artery shape 840 is cut. The interior of carotid artery shape 840 may create closed carotid artery shape 850 . Then, the processor may create the empty carotid artery shape 860 by removing at least some of the objects included in the closed carotid artery shape 850 . The processor may then generate carotid artery shape 870 with the inside of empty carotid artery shape 860 closed. Through this preprocessing process, the processor can quickly interpret the shape of the carotid artery when a particle-based simulation of the preprocessed carotid artery shape is performed.

The processor may then generate a shape 880 of the carotid artery filled with structures outside the vessel wall of shape 870 of the carotid artery closed on the inside. By performing such processing, since external flow of the carotid artery is not generated, calculation time is reduced when performing particle-based simulation of the shape 880 of the carotid artery, and furthermore, required resources (eg, memory capacity) are reduced. can

9 is an exemplary diagram illustrating a process of 3D particle-based simulation according to an embodiment of the present disclosure. A processor (eg, a processor of an information processing system and/or a processor of a user terminal) may perform a simulation using a plurality of particles in a 3D image corresponding to the patient's carotid artery. For example, the processor may perform the simulation by putting a plurality of particles into the carotid artery so as to be movable with respect to the contracted or expanded carotid artery according to the patient's heartbeat.

The processor may analyze the movement of a plurality of particles moving in the carotid artery based on the simulation result and interpret information about blood flow characteristics in the carotid artery in real time. For example, as shown, since the processor performs a particle-based simulation within a 3D image corresponding to the carotid artery, the first 3D image 910 generated by real-time analysis of low blood pressure during expansion of the carotid artery and The second 3D image 920 generated by analyzing the high blood pressure when the carotid artery is constricted in real time may be output. The first 3D image 910 and the second 3D image 920 thus generated may be output or displayed on the display device of the user terminal according to time.

10 is a flowchart illustrating an example of a method for analyzing blood flow characteristics in a carotid artery according to an embodiment of the present disclosure. According to an embodiment, the method 1000 for analyzing blood flow characteristics in a carotid artery may be performed by an information processing system and/or at least one processor of a user terminal. As shown, the method 1000 may begin with a processor acquiring an image of the patient's carotid artery (S1010).

The processor may generate a 3D image that simulates the shape of the carotid artery based on the image of the carotid artery (S1020). Also, the processor may receive information about blood flow in the patient (S1030). Then, the processor may perform a simulation using a plurality of particles in the 3D image corresponding to the carotid artery based on the received information on the patient's blood flow (S1040). Information on blood flow characteristics in the carotid artery may be determined based on the result of the simulation performed in this way (S1050).

The above method may be provided as a computer program stored in a computer readable recording medium to be executed on a computer. The medium may continuously store programs executable by a computer or temporarily store them for execution or download. In addition, the medium may be various recording means or storage means in the form of a single or combined hardware, but is not limited to a medium directly connected to a certain computer system, and may be distributed on a network. Examples of the medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROM and DVD, magneto optical media such as floptical disks, and Anything configured to store program instructions may include a ROM, RAM, flash memory, or the like. In addition, examples of other media include recording media or storage media managed by an app store that distributes applications, a site that supplies or distributes various other software, and a server.

The methods, acts or techniques of this disclosure may be implemented by various means. For example, these techniques may be implemented in hardware, firmware, software, or combinations thereof. Those skilled in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchange of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and the design requirements imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementations should not be interpreted as causing a departure from the scope of the present disclosure.

In a hardware implementation, the processing units used to perform the techniques may include one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs) ), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, and other electronic units designed to perform the functions described in this disclosure. , a computer, or a combination thereof.

Accordingly, the various illustrative logical blocks, modules, and circuits described in connection with this disclosure may be incorporated into a general-purpose processor, DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or may be implemented or performed in any combination of those designed to perform the functions described in A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, eg, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other configuration.

In firmware and/or software implementation, the techniques include random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), PROM ( on a computer readable medium, such as programmable read-only memory (EPROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, compact disc (CD), magnetic or optical data storage device, or the like. It can also be implemented as stored instructions. Instructions may be executable by one or more processors and may cause the processor(s) to perform certain aspects of the functionality described in this disclosure.

If implemented in software, the techniques may be stored on or transmitted over as one or more instructions or code on a computer readable medium. Computer readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of non-limiting example, such computer readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or desired program code in the form of instructions or data structures. It can be used for transport or storage to and can include any other medium that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium.

For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, the coaxial cable , fiber optic cable, twisted pair, digital subscriber line, or wireless technologies such as infrared, radio, and microwave are included within the definition of medium. Disk and disc as used herein include CD, laser disc, optical disc, digital versatile disc (DVD), floppy disk, and blu-ray disc, where disks are usually magnetic data is reproduced optically, whereas discs reproduce data optically using a laser. Combinations of the above should also be included within the scope of computer readable media.

A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium can be coupled to the processor such that the processor can read information from or write information to the storage medium. Alternatively, the storage medium may be integral to the processor. The processor and storage medium may reside within an ASIC. An ASIC may exist within a user terminal. Alternatively, the processor and storage medium may exist as separate components in a user terminal.

Although the embodiments described above have been described as utilizing aspects of the presently-disclosed subject matter in one or more stand-alone computer systems, the disclosure is not limited thereto and may be implemented in conjunction with any computing environment, such as a network or distributed computing environment. . Further, aspects of the subject matter in this disclosure may be implemented in a plurality of processing chips or devices, and storage may be similarly affected across multiple devices. These devices may include PCs, network servers, and portable devices.

Although the present disclosure has been described in relation to some embodiments in this specification, various modifications and changes may be made without departing from the scope of the present disclosure that can be understood by those skilled in the art. Moreover, such modifications and variations are intended to fall within the scope of the claims appended hereto.

Claims (21)

A method for analyzing blood flow characteristics in a carotid artery through particle-based simulation, performed by at least one processor, comprising: Obtaining an image of the patient's carotid artery; generating a 3D image simulating a shape of the carotid artery based on the image of the carotid artery; receiving information about blood flow in the patient; performing a simulation using a plurality of particles in the 3D image based on the received information on the patient's blood flow; and Determining information on blood flow characteristics in the carotid artery based on a result of the performed simulation Including, a method for analyzing blood flow characteristics in the carotid artery. According to claim 1, Wherein the determining of information on the determined blood flow characteristics in the carotid artery comprises determining pathological information in the carotid artery. According to claim 2, The pathological information includes at least one of information about at least one stenotic region in the carotid artery, information about wall shear stress given to an inner wall of the carotid artery, or dynamics in the carotid artery. A method for analyzing blood flow characteristics in the carotid artery. According to claim 2, Determining information on blood flow characteristics in the carotid artery, Inferring pathological information in the carotid artery from the determined blood flow characteristics in the carotid artery using a first machine learning model; The first machine learning model is learned to output reference pathological information of a plurality of reference patients from a plurality of reference blood flow characteristics of a plurality of reference patients. A method for analyzing blood flow characteristics in the carotid artery. According to claim 2, Determining information on blood flow characteristics in the carotid artery, Outputting information on blood flow characteristics in the carotid artery from a result of the performed simulation using a second machine learning model; Determining the pathological information in the carotid artery, receiving biometric information of the patient; and Outputting pathological information in the carotid artery from the received biometric information of the patient and the output information on blood flow characteristics using a third machine learning model, A method for analyzing blood flow characteristics in the carotid artery. According to claim 1, The information on the blood flow in the patient includes at least one of blood flow velocity, blood flow amount, or blood pressure in a specific region in the patient's carotid artery. According to claim 5, At least one of blood flow velocity, blood flow, or blood pressure in a specific region in the carotid artery of the patient is set to have a value within a predetermined region, A method for analyzing blood flow characteristics in the carotid artery. According to claim 1, The step of generating the 3D image, extending at least a portion of the carotid artery; and Adding a structure node outside of the carotid artery, A method for analyzing blood flow characteristics in the carotid artery. According to claim 1, The step of performing a simulation using a plurality of particles in the 3D image corresponding to the carotid artery may include inserting a plurality of particles into the carotid artery so as to be movable with respect to the contracted or expanded carotid artery according to the patient's heartbeat, Including performing a simulation, A method for analyzing blood flow characteristics in the carotid artery. According to claim 9, Determining information on blood flow characteristics in the carotid artery, Analyzing the movement of a plurality of particles moving in the carotid artery and interpreting information on blood flow characteristics in the carotid artery in real time, A method for analyzing blood flow characteristics in the carotid artery. A computer program stored in a computer-readable recording medium to execute the method according to any one of claims 1 to 10 on a computer. In the system for analyzing blood flow characteristics in the carotid artery, a memory that stores one or more instructions; and By executing one or more instructions in the memory, acquiring an image of the carotid artery of the patient, generating a 3D image that simulates the shape of the carotid artery based on the image of the carotid artery, receiving information about blood flow in the patient, , Based on the received information on the patient's blood flow, a simulation using a plurality of particles is performed in the 3D image corresponding to the carotid artery, and information on blood flow characteristics in the carotid artery is obtained based on the result of the simulation performed. at least one processor configured to determine Including, a system for analyzing blood flow characteristics in the carotid artery. According to claim 12, the processor, Further configured to determine pathological information within the carotid artery. A system for analyzing blood flow characteristics in the carotid artery. According to claim 13, The pathological information includes at least one of information about at least one stenotic region in the carotid artery, information about wall shear stress given to the inner wall of the carotid artery, or dynamics in the carotid artery, A system for analyzing blood flow characteristics in the carotid artery. According to claim 12, the processor, configured to infer pathological information in the carotid artery from the determined blood flow characteristics in the carotid artery using a first machine learning model; The first machine learning model is learned to output reference pathological information of a plurality of reference patients from a plurality of reference blood flow characteristics of a plurality of reference patients. A system for analyzing blood flow characteristics in the carotid artery. According to claim 12, the processor, Information on blood flow characteristics in the carotid artery is output from the results of the performed simulation using a second machine learning model, biometric information of the patient is received, and biometric information of the patient is received using a third machine learning model. And configured to output pathological information in the carotid artery from the biometric information and the output information on the blood flow characteristics. A system for analyzing blood flow characteristics in the carotid artery. According to claim 16, The information on the blood flow in the patient includes at least one information of blood flow rate, blood flow amount, or blood pressure in a specific region in the patient's carotid artery. A system for analyzing blood flow characteristics in the carotid artery. According to claim 17, At least one of blood flow velocity, blood flow, or blood pressure in a specific region in the carotid artery of the patient is set to have a value within a predetermined region, A system for analyzing blood flow characteristics in the carotid artery. According to claim 18, the processor, Extending at least a portion of the carotid artery, and configured to add a structure node to the exterior of the carotid artery. Including, a system for analyzing blood flow characteristics in the carotid artery. According to claim 12, the processor, Configured to perform the simulation by movably inserting a plurality of particles into the carotid artery with respect to the contracted or dilated carotid artery according to the patient's heartbeat, A system for analyzing blood flow characteristics in the carotid artery. According to claim 20, the processor, It is configured to analyze the movement of a plurality of particles moving in the carotid artery to interpret information on blood flow characteristics in the carotid artery in real time, A system for analyzing blood flow characteristics in the carotid artery.

Images