WO2025057095A1

WO2025057095A1 - A system and a method for remotely analysing an injury of a patient

Info

Publication number: WO2025057095A1
Application number: PCT/IB2024/058869
Authority: WO
Inventors: Sucheshnadevi Patil
Original assignee: Individual
Current assignee: Individual
Priority date: 2023-09-14
Filing date: 2024-09-12
Publication date: 2025-03-20
Anticipated expiration: 2026-03-14

Abstract

A system and a method for remotely analysing an injury of a patient is disclosed The system includes a data collection engine to receive health related information of the patient to generate a corresponding profile and an image of an area surrounding the injury. The system includes a profile creation module to generate questions for the patient using a machine learning model. The system includes a template generation module to generate templates and calibrate the templates by using a reference of the area in the absence of the injury. The system includes a categorization module to classify the injury based on parameters. The system also includes a virtual injury screening, monitoring, and analysis module to virtually diagnose the injury as one of a surgical injury or a non-surgical injury on a plurality of factors.

Description

A SYSTEM AND A METHOD FOR REMOTELY ANALYSING AN INJURY OF A PATIENT

EARLIEST PRIORITY DATE

This Application claims priority from a provisional patent application filed in India having Patent Application No. 202321062032 filed on 14th day of September 2023 and titled A SYSTEM AND A METHOD FOR VIRTUAL TELEHEALTH DIAGNOSIS, SCREENING AND MONITORING INJURIES.

FIELD OF INVENTION

Embodiments of the present disclosure relate to the field of diagnosis and screening of injuries, and more particularly to a system and method for remotely analysing an injury of a patient.

BACKGROUND

During telehealth visits, doctors can only talk to patients. They can discuss symptoms, provide guidance, and prescribe medication, but they cannot physically examine or analyse a patient's condition. This lack of direct examination makes it challenging for doctors to accurately diagnose or assess certain conditions, especially those related to musculoskeletal injuries, which often require physical tests, measurements, or visual assessments.

Another drawback in telehealth is the inability to perform manual tests. For musculoskeletal injuries, doctors often use manual tests, such as applying pressure to specific areas, measuring joint angles, or performing movements to assess pain, range of motion, and functionality. Without these manual tests, it is difficult for doctors to understand the full extent of an injury or condition. This limitation affects the accuracy of the diagnosis and the effectiveness of the treatment plan.

Further, in a telehealth setting, doctors heavily rely on patients to accurately describe their symptoms, pain levels, and history. However, patients might struggle to communicate effectively or may not notice certain symptoms that a doctor would identify in person. This can lead to incomplete or inaccurate information, affecting the doctor’s ability to make a well-informed diagnosis or treatment decision.

Furthermore, monitoring a patient's progress or response to treatment can be challenging without in-person visits. For musculoskeletal conditions, regular assessment of recovery (such as changes in mobility or pain levels) is often necessary. Telehealth makes it harder to track these changes over time, potentially delaying necessary adjustments to the treatment plan or missing early signs of complications.

Hence, there is a need for an improved system and method for remotely analysing an injury of a patient which addresses the aforementioned issue(s).

OBJECTIVE OF THE INVENTION

An objective of the present invention is to allow telehealth doctors to analyse patients virtually and remotely with musculoskeletal injuries or conditions to better understand the nature and extent of an injury.

Another objective of the present invention is to analyse the injury through a camera to analyse patients and further screen and analyse injuries for a particular musculoskeletal condition or not.

Yet another objective of the present invention is to provide screening and diagnosis to verify if the injury is chronic or acute.

Yet another objective of the present invention is to provide a live virtual telehealth visit with a doctor to help analyse and provide decision support for treatment or prescription for over-the counter medication or physiotherapy or to see a surgeon for further appointments.

BRIEF DESCRIPTION

In accordance with an embodiment of the present disclosure, a system for remotely analysing an injury of a patient is provided. The system includes a processing subsystem hosted on a server, wherein the processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The system includes a data collection engine configured to receive health related information of the patient to generate a corresponding profile and an image of an area surrounding the injury. The system includes a profile creation module operatively coupled to the data collection engine wherein the profile creation module is configured to generate a plurality of questions for the patient based on the health related information using a machine learning model. The system includes a template generation module operatively coupled to the profile creation module wherein the template generation module is configured to generate one or more templates in response to screening of the image. The template generation module is also configured to calibrate the one or more templates based on the area by using a reference of the area in the absence of the injury. The system includes a categorization module operatively coupled to the template generation module wherein the categorization module is configured to classify the injury based on a plurality of parameters. The system also includes a virtual injury screening, monitoring and analysis module operatively coupled to the categorization module wherein the virtual injury screening, monitoring and analysis module is configured to screen and analyse the injury as one of a surgical injury or a non-surgical injury on a plurality of factors virtually and remotely.

In accordance with another embodiment of the present disclosure, a method remotely analysing an injury of a patient is provided. The method includes receiving, by a data collection engine, health related information of the patient to generate a corresponding profile and an image of an area surrounding the injury. The method includes generating, by the profile creation module, a plurality of questions for the patient based on the health related information using a machine learning model. The method includes generating, by a template generation module, one or more templates in response to screening of the image. The method includes calibrating, by the template generation module, the one or more templates based on the area by using a reference of the area in the absence of the injury. The method includes classifying, by a categorization module, the injury based on a plurality of parameters. The method includes virtually and remotely screen and analyse , by a diagnosis module, the injury as one of a surgical injury or a non-surgical injury on a plurality of factors.

To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:

FIG. l is a block diagram representation of a system for remotely analysing an injury of a patient, in accordance with an embodiment of the present disclosure;

FIG. 2 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure; and

FIG. 3 illustrates a flow chart representing the steps involved in a method for remotely analysing an injury of a patient in accordance with an embodiment of the present disclosure.

Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.

DETAILED DESCRIPTION For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.

The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or subsystems or elements, structures, or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.

In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.

In accordance with an embodiment of the present disclosure, a system for remotely analysing an injury of a patient is provided. The system includes a processing subsystem hosted on a server, wherein the processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The system includes a data collection engine configured to receive health related information of the patient to generate a corresponding profile and an image of an area surrounding the injury. The system includes a profile creation module operatively coupled to the data collection engine wherein the profile creation module is configured to generate a plurality of questions for the patient based on the health related information using a machine learning model. The system includes a template generation module operatively coupled to the profile creation module wherein the template generation module is configured to generate one or more templates in response to screening of the image. The template generation module is also configured to calibrate the one or more templates based on the area by using a reference of the area in the absence of the injury. The system includes a categorization module operatively coupled to the template generation module wherein the categorization module is configured to classify the injury based on a plurality of parameters. The system also includes a virtual injury screening, monitoring and analysis module operatively coupled to the categorization module wherein the virtual injury screening, monitoring and analysis module is configured to virtually and remotely screen and analyse the injury as one of a surgical injury or a non-surgical injury on a plurality of factors.

FIG. l is a block diagram representation of a system for remotely analysing an injury of a patient, in accordance with an embodiment of the present disclosure. The system (100) includes a processing subsystem (105) hosted on a server (108). In one embodiment, the server (108) may include a cloud server. In another embodiment, the server (108) may include a local server. The processing subsystem (105) is configured to execute on a network (110) to control bidirectional communications among a plurality of modules. In a preferred embodiment, the network (110) may include a Wireless local area network (WLAN) network, a cellular network, and a Low-power wide-area (LPWA) network. In one embodiment, the network (110) may also include a wired network such as local area network (LAN), Wi-Fi, Bluetooth, Zigbee, near field communication (NFC), infra-red communication (RFID) or the like. Further, the plurality of modules includes a data collection engine (115), a profile creation module (120), a template generation module (125), a categorization module (130) and a virtual injury screening, monitoring, and analysis module (135). The data collection engine (115) is configured to receive health related information of the patient to generate a corresponding profile and an image of an area surrounding the injury. The data collection engine (115) is configured to collect health-related information about the patient. This information includes but is not limited to previous health conditions (such as, allergies, ongoing medications, past injuries, and surgeries), patient-reported symptoms (such as pain level, swelling, bruising, range of motion, or any other relevant details), age, gender, and lifestyle factors. It must be noted that the injury refers to musculoskeletal injury that affects the muscles, bones, ligaments, tendons, and nerves in the patient. Specific physiological positions and movements of the patient is analysed to identify abnormalities or injuries. It involves monitoring how body parts are positioned or moving to detect signs of musculoskeletal damage. In one embodiment, the injury includes health human physiological injuries as per bone and muscle movement motion analysis.

Based on the health-related information collected, the data collection engine creates a profile for the patient. This profile serves as a comprehensive record of all the patient's relevant health data, which can be used for further analysis, diagnosis, and treatment planning.

The data collection engine (115) is also configured to receive or capture an image of the area surrounding the injury. This could be a photograph or a scan of the injured body part, providing visual information about the injury, such as visible wounds, swelling, or discoloration. The image complements the health-related data by offering a visual representation of the injury, which is critical for remote assessment.

The profile creation module (120) is operatively coupled to the data collection engine (115) wherein the profile creation module (120) is configured to generate a plurality of questions for the patient based on the health related information using a machine learning model. In one embodiment, each of the plurality of questions is generated based on an answer for a preceding question. In other words, the questions dynamically change depending on the patient’s responses. As the patient answers each question, the model analyses the response in real-time to determine what additional information is needed. It then generates follow-up questions that are directly related to the previous answers, creating an adaptive dialogue. This adaptive approach ensures that the information gathered is more accurate and relevant. For example, if a patient indicates severe pain in response to the first question, subsequent questions may delve deeper into the location, nature, and triggers of the pain.

The machine learning model is trained with past diagnosis of injuries. Typically, the machine learning model guides the generation of these questions. This model uses algorithms that have been trained with data from past diagnoses of injuries. The machine learning model is trained on historical data that includes various types of injuries and their diagnoses. As it is exposed to more data over time, the model can continuously improve its accuracy in generating relevant questions. This training allows the model to recognize patterns and relationships between certain symptoms, answers, and injury types.

The template generation module (125) is operatively coupled to the profile creation module (120) wherein the template generation module (125) is configured to generate one or more templates in response to screening of the image. Screening the image involves a series of steps to extract relevant features and identify key aspects of the injury.

The first step involves obtaining the image of the injured area. This image could be captured using a camera, smartphone, or medical imaging devices like X-rays or MRIs, depending on the system's capabilities. The image is then prepared for analysis by adjusting brightness, contrast, or resolution to enhance the visibility of important features.

The second step involves feature extraction. Techniques like the Canny edge detector can outline the boundaries of objects or anomalies. Subsequently, specific features like fractures or torn ligaments are identified using pattern recognition methods. Additionally, the size, shape, or extent of the injury is measured. For example, estimating the area of swelling or the displacement of a bone.

The third step involves pattern recognition and classification. The extracted features are compared with predefined templates or models of common injuries. This helps in identifying the type of injury based on known patterns. The machine learning model is used to recognize patterns associated with specific injuries. The model analyses the features extracted from the image and predict the injury type or severity based on historical data. Convolutional Neural Networks (CNNs) and other deep learning models can classify the injury by learning from a vast dataset of annotated images. Further, deviations from normal anatomical patterns are detected to identify unusual features indicative of an injury.

The fourth step involves calibration and adjustment to ensure measurements and comparisons are accurate. Further, using reference images of healthy anatomy to calibrate the screening results. This helps in understanding how the injury deviates from the normal state.

The fifth and final step involves integration with patient data. The screened image data is integrated with other patient information (e.g., symptoms, medical history) to provide a comprehensive view of the injury. This helps in refining the diagnosis and treatment plan.

Additionally, the template generation module (125) is configured to calibrate the one or more templates based on the area by using a reference of the area in the absence of the injury. Calibration is the process of adjusting these templates to ensure they accurately reflect the patient's specific anatomy. To achieve this, the template generation module (125) uses a reference image of the same area in its normal, uninjured state. This reference could come from pre-existing images or models of a healthy body part from medical databases or from an earlier image of the patient’s body part before the injury occurred. The comparison of the injured area with the reference image allows the template generation module (125) to adjust the templates to accurately match the patient's unique anatomy. This helps in identifying deviations or changes caused by the injury, such as shifts in bone alignment, tissue damage, or abnormal swelling.

The categorization module (130) is operatively coupled to the template generation module (125) wherein the categorization module (130) is configured to classify the injury based on a plurality of parameters. The term "plurality of parameters" refers to the different types of data and inputs that the categorization module (130) considers when making a classification. Examples of the plurality of parameters includes, but is not limited to, images, photographs, templates, questionnaires, range of motion and position specific movement. The images are visual data from the patient’s injury area, such as photographs or scans, which provide information about the injury’s appearance. The range of motion provides data on how well the patient can move the injured body part, which can indicate the severity of the injury. Further, the position specific movement provides observations of how the injury affects movements in specific positions or during specific activities.

Further, the categorization module (130) classifies the injury at multiple stages based on images, questionnaires, templates and position-specific determined movements and motions. This means that the categorization module (130) iteratively refines its classification based on different types of data and at different points in the assessment process.

The virtual injury screening, monitoring, and analysis module (135) operatively coupled to the categorization module (140) wherein the virtual injury screening, monitoring, and analysis module (135) is configured to screen and analyse the injury as one of a surgical injury or a non-surgical injury on a plurality of factors virtually and remotely. A surgical injury refers to one that typically requires surgical intervention whereas a non-surgical injury is one that can usually be treated with non- invasive methods. The plurality of factors includes but is not limited to, X-Ray, MRI images, photographs, injury monitoring, range of motion, pain level, intensity, occurrence, looks of injury, position-specific analysis, software tests, patient background information and questionnaire, templates, and telehealth virtual visit. Further, the virtual and remote screening and analysis leads to precise data capture by proprietary AIML and data analytics algorithm leading of 60-70 % clinical decision support further leading to diagnosis by a health professional.

In one embodiment, the virtual injury screening, monitoring, and analysis module (135) is configured to detect swelling and pain at a specific position of the injury along with calibration of a non-injured body part as a part of the diagnosis. The virtual injury screening, monitoring, and analysis module (135) can specifically detect swelling and pain at a particular location of the injury. This involves analysing both physical signs (e.g., swelling) and patient-reported symptoms (e.g., pain levels) remotely. The diagnosis also includes calibrating or comparing the injured area with a non-injured body part. This helps in understanding deviations from normal anatomical conditions and assessing the injury's impact. It must be noted that the diagnosis is 60-70 % leaving and 30-40% in person provider check up for final diagnosis. This a clinician decision making process of virtual analysis and proprietary algorithmic output categorizing injury screening up to 60-70%.

Consider a non -limiting example wherein, a patient ‘X’ experiences a sharp pain in their right knee after twisting it while playing soccer. The injury causes swelling and limits the range of motion. Patient ‘X’ lives in a rural area far from the nearest orthopaedic specialist. Patient ‘X’ books a telehealth appointment with an orthopaedic doctor. During the virtual visit, the doctor asks patient ‘X’ to describe their symptoms and the circumstances of the injury. The doctor requests patient ‘X’ to upload images of the injured knee taken from different angles using a smartphone. These images are uploaded to the system’s platform. The data collection engine (115) receives the health-related information (symptoms, injury details) and images of the knee. The profile creation module (120) generates a set of personalized questions for Alex, using a machine learning model trained on past injury data. The questions may be, "On a scale of 1-10, how severe is your pain?", "Does the pain increase with specific movements?" or Have you experienced any previous knee injuries?". The answers to these questions help refine the diagnostic process. The template generation module (125) screens the uploaded images to identify visible signs of injury, such as swelling, bruising, or misalignment. The system (100) compares these images with pre-defined templates of normal and injured knees to identify deviations and abnormalities. The template generation module (125) calibrates these templates using reference images of a healthy knee to detect specific issues, such as ligament tears or meniscus damage. The categorization module (130) analyses the collected data (images, questionnaire responses, range of motion tests) and classifies the injury based on several parameters. Subsequently, the template generation module (125) classifies the injury as a potential Anterior Cruciate Ligament (ACL) tear and assigns it as a “moderate to severe” injury category. The virtual injury screening, monitoring, and analysis module (135) uses the categorized data to virtually diagnose the injury. It evaluates whether the injury is surgical or non-surgical by considering several factors. Specifically, the virtual injury screening, monitoring, and analysis module (135) determines that the injury is a surgical injury (a tom ACL that often requires surgery for full recovery). The system generates a preliminary treatment plan for patient ‘X’. The system also schedules regular telehealth follow-ups to monitor patient ‘X’ ‘s condition and adjust the treatment plan based on new data (e.g., changes in pain levels or range of motion). Further, the system continuously monitors patient ‘X’ ‘s progress through regular virtual check-ins, allowing the healthcare team to make real-time adjustments to the treatment plan. Thereby, patient ‘X’ receives a comprehensive initial diagnosis remotely, saving time and travel.

FIG. 2 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure. The server (110) includes processor(s) (210), and memory (220) operatively coupled to the bus (230). The processor(s) (210), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.

The memory (220) includes several subsystems stored in the form of executable program which instructs the processor (210) to perform the method steps illustrated in FIG. 1. The memory (220) includes a processing subsystem (105) of FIG.1. The processing subsystem (105) includes a plurality of modules. The plurality of modules includes a data collection engine (115), a profile creation module (120), a template generation module (125), a categorization module (130) and a virtual injury screening, monitoring, and analysis module (135).

The data collection engine (115) is configured to receive health related information of the patient to generate a corresponding profile and an image of an area surrounding the injury. The profile creation module (120) is operatively coupled to the data collection engine (115) wherein the profile creation module (120) is configured to generate a plurality of questions for the patient based on the health related information using a machine learning model. The template generation module (125) is operatively coupled to the profile creation module (120) wherein the template generation module (125) is configured to generate one or more templates in response to screening of the image. The template generation module (125) is also configured to calibrate the one or more templates based on the area by using a reference of the area in the absence of the injury. The categorization module (130) is operatively coupled to the template generation module (125) wherein the categorization module (130) is configured to classify the injury based on a plurality of parameters. The virtual injury screening, monitoring, and analysis module (135) is operatively coupled to the categorization module (140) wherein the virtual injury screening, monitoring, and analysis module (135) is configured to virtually diagnose the injury as one of a surgical injury or a non- surgical injury on a plurality of factors.

The bus (230) as used herein refers to be internal memory channels or computer network that is used to connect computer components and transfer data between them. The bus (230) includes a serial bus or a parallel bus, wherein the serial bus transmits data in bit-serial format and the parallel bus transmits data across multiple wires. The bus (230) as used herein, may include but not limited to, a system bus, an internal bus, an external bus, an expansion bus, a frontside bus, a backside bus, and the like.

FIG. 3 illustrates a flow chart representing the steps involved in a method for remotely analysing an injury of a patient in accordance with an embodiment of the present disclosure. The method (300) includes receiving health related information of the patient to generate a corresponding profile and an image of an area surrounding the injury in step 305. Examples of the health related information includes, but is not limited to, medical history, symptoms, patient demographics, and other relevant health data. In addition to this information, an image of the area surrounding the injury is also obtained. This could be a photograph or scan captured using a smartphone, camera, or other imaging device. The collected information (health data and images) is used to generate a corresponding profile for the patient. This profile includes the injury's location, appearance, and context, as well as other medical data necessary for diagnosis.

Further, creating a profile helps in systematically organizing of the information, which can be used by healthcare providers or an Al system to assess the injury, suggest diagnoses, and determine the next steps for treatment or monitoring.

The method (300) includes generating a plurality of questions for the patient based on the health related information using a machine learning model in step 310. Personalized questions for the patient are created based on the health-related information collected earlier. These questions are tailored to the patient's specific health information and injury details. This is done to gather more specific details about the patient's condition, which will help in understanding the injury more thoroughly and accurately.

The questions are created using the machine learning model. This model analyses the initial health data (like medical history, symptoms, and images) to determine what additional information is needed for a more accurate diagnosis or assessment. The machine learning model customizes the questions to the patient's unique situation. For example, if the patient's profile indicates a specific type of injury (like a sprained ankle), the model might generate questions focusing on pain levels, range of motion, and recent activities that might have caused the injury.

The method (300) includes generating one or more templates in response to screening of the image in step 315. The image of the area surrounding the injury, which was collected earlier, is screened or analysed. This process may involve using various image processing techniques or algorithms to identify key features of the injury, such as swelling, bruising, cuts, or deformities. In response to the results of the image screening, the method generates one or more templates. These templates are standardized formats or reference models that help in categorizing or understanding the injury. They could represent common patterns of injuries, anatomical landmarks, or classifications of injury types. Typically, the templates serve as a guide for further analysis or comparison. For example, they might be used to compare the patient's injury to a known set of injury types or to assist in documenting the findings consistently.

The method (300) includes calibrating the one or more templates based on the area by using a reference of the area in the absence of the injury in step 320. Calibration means adjusting the templates created in the previous step (315) so that they accurately correspond to the patient's specific anatomical features. To perform this calibration, the method uses a reference image of the same body part or area when it was in a healthy, uninjured state. This reference could come from a database of standard anatomical images or might be a previous image of the patient taken before the injury. By comparing the image of the injured area with the reference image, the method identifies deviations or changes caused by the injury. These differences are used to fine-tune the templates, ensuring they accurately match the patient's specific condition and anatomy.

The method (300) includes classifying the injury based on a plurality of parameters in step 325.

The method (300) includes virtually and remotely screening and analysing the injury as one of a surgical injury or a non-surgical injury on a plurality of factors in step 330. Classification refers to sorting or categorizing the injury into specific types or categories. For example, an injury could be classified as a sprain, fracture, muscle tear, or dislocation, among others.

The classification is performed based on the plurality of factors that includes, observations from the image, information gathered from the patient, insights from the calibrated templates created earlier which may highlight deviations from the normal anatomy and input from machine learning models that may identify patterns consistent with specific types of injuries.

Various embodiments of the present disclosure provides a system and method for remotely analysing an injury of a patient. By incorporating multiple types of data — images, templates, questionnaires, and physical assessments — the system (100) provides a well-rounded view of the injury, leading to more accurate diagnoses. The use of machine learning models helps in recognizing paterns and improving diagnostic accuracy based on historical data and real-time inputs. The machine learning model and continuous data collection allows the system to improve its diagnostic capabilities over time. Feedback and new data can refine algorithms and enhance accuracy. Patients can receive detailed assessments and diagnoses from the comfort of their homes, reducing the need for in-person visits and making care more accessible, especially for those in remote areas. Remote consultations save time and resources for both patients and healthcare providers. Patients can avoid the inconvenience of traveling and waiting for appointments. The system generates personalized questions and assessments based on the patient’s specific data, leading to a treatment plan that is customized to their unique condition. By evaluating the impact of the injury on specific movements and positions, the system helps in creating more targeted and effective treatment plans. Further, the use of standardized templates and systematic data analysis reduces the likelihood of errors that can occur with less structured diagnostic approaches.

It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.

While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.

The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.

Claims

I CLAIM:

1. A system (100) for remotely analysing an injury of a patient comprising: characterized in that, a processing subsystem (105) hosted on a server (108), wherein the processing subsystem (105) is configured to execute on a network (110) to control bidirectional communications among a plurality of modules comprising: a data collection engine (115) configured to receive health related information of the patient to generate a corresponding profile and an image of an area surrounding the injury; a profile creation module (120) operatively coupled to the data collection engine (115) wherein the profile creation module (120) is configured to generate a plurality of questions for the patient based on the health related information using a machine learning model; a template generation module (125) operatively coupled to the profile creation module (120) wherein the template generation module (125) is configured to: generate one or more templates in response to remote screening of the image; and calibrate the one or more templates based on the area by using a reference of the area in the absence of the injury; a categorization module (130) operatively coupled to the template generation module (125) wherein the categorization module (130) is configured to classify the injury based on a plurality of parameters; and a virtual injury screening, monitoring, and analysis module (135) operatively coupled to the categorization module (140) wherein the virtual injury screening, monitoring, and analysis module (135) is configured to virtually and remotely screen and analyse the injury as one of a surgical injury or a non-surgical injury on a plurality of factors.

2. The system (100) as claimed in claim 1, wherein the plurality of parameters comprises images, photographs, templates, questionnaires, range of motion and position specific movement.

3. The system (100) as claimed in claim 1, wherein the categorization module (130) classifies the injury at multiple stages based on images, questionnaires, templates and position-specific determined movements and motions.

4. The system (100) as claimed in claim 1, wherein the plurality of factors comprises X-Ray, MRI images, photographs, injury monitoring, range of motion, pain level, intensity, occurrence, looks of injury, position-specific analysis, software tests, patient background information and questionnaire, templates, and telehealth virtual visit.

5. The system (100) as claimed in claim 1, wherein the virtual injury screening, monitoring, and analysis module (135) is configured to detect swelling and pain at a specific position of the injury along with calibration of a non-injured body part as a part of the diagnosis.

6. The system (100) as claimed in claim 1, wherein each of the plurality of questions is generated based on an answer for a preceding question.

7. The system (100) as claimed in claim 1, wherein the machine learning model is trained with past diagnosis of injuries.

8. A method (300) for remotely analysing an injury of a patient comprising: characterized in that, receiving, by a data collection engine, health related information of the patient to generate a corresponding profile and an image of an area surrounding the injury; (305) generating, by the profile creation module, a plurality of questions for the patient based on the health related information using a machine learning model; (310) generating, by a template generation module, one or more templates in response to remotely screening of the image; (315) calibrating, by the template generation module, the one or more templates based on the area by using a reference of the area in the absence of the injury; (320) classifying, by a categorization module, the injury based on a plurality of parameters; (325) and virtually and remotely screen and analyse, by a diagnosis module, the injury as one of a surgical injury or a non-surgical injury on a plurality of factors. (330)