Cold ground orthopedics intelligent diagnosis and treatment intelligent auxiliary evaluation system

Abstract

The application provides a cold region orthopedic intelligent diagnosis and treatment intelligent auxiliary evaluation system, comprising: an orthopedic trauma auxiliary evaluation module, which is used for intelligently analyzing orthopedic trauma of a patient and generating an orthopedic trauma auxiliary evaluation report; a sports injury intelligent analysis module, which is used for intelligently analyzing the degree of sports injury of the patient and generating a sports injury intelligent analysis report; an osteoporosis auxiliary evaluation module, which is used for auxiliary evaluation of the degree of osteoporosis of the patient and generating an osteoporosis auxiliary evaluation report; an osteoarthritis intelligent analysis module, which is used for intelligently analyzing the degree of osteoarthritis of the patient and generating an osteoarthritis intelligent analysis report; a traditional Chinese medicine orthopedic auxiliary evaluation module, which is used for traditional Chinese medicine syndrome differentiation of symptoms of the patient and generating a traditional Chinese medicine orthopedic auxiliary evaluation report; and an orthopedic health management module, which is used for orthopedic health management suggestion for the patient and generating an orthopedic health management scheme.

Description

Technical Field

[0001] This application belongs to the field of intelligent auxiliary assessment for orthopedics in cold regions, and particularly relates to an intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions. Background Technology

[0002] With the rapid development of artificial intelligence technology, more and more models with different uses have emerged in the medical field, which can provide patients with corresponding auxiliary diagnosis and treatment services.

[0003] In related technologies, medical large-scale models are developing towards generality, but lack medical auxiliary diagnosis and treatment models for specific application scenarios, making it difficult to meet the personalized needs of patients in specific application scenarios such as orthopedic patients in cold regions. Summary of the Invention

[0004] This application provides an intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedic diseases in cold regions. It can perform auxiliary assessment of orthopedic trauma, intelligent analysis of sports injuries, auxiliary assessment of osteoporosis, intelligent analysis of osteoarthritis, auxiliary assessment of traditional Chinese medicine orthopedics, and orthopedic health management. It can meet the personalized diagnosis and treatment needs of orthopedic patients in cold regions and provide them with comprehensive and personalized diagnosis and treatment services.

[0005] In a first aspect, embodiments of this application provide an intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions, comprising: The orthopedic trauma auxiliary assessment module is used to intelligently analyze patients' orthopedic trauma and generate orthopedic trauma auxiliary assessment reports. The intelligent sports injury analysis module is used to intelligently analyze the degree of sports injury in patients and generate intelligent sports injury analysis reports. The osteoporosis auxiliary assessment module is used to assist in assessing the degree of osteoporosis in patients and generate an osteoporosis auxiliary assessment report; The osteoarthritis intelligent analysis module is used to intelligently analyze the degree of osteoarthritis in patients and generate an intelligent analysis report on osteoarthritis. The Traditional Chinese Medicine Orthopedics Auxiliary Assessment Module is used to conduct TCM syndrome differentiation of patients' symptoms and generate TCM orthopedics auxiliary assessment reports. The orthopedic health management module is used to provide patients with orthopedic health management suggestions and generate orthopedic health management plans.

[0006] Optionally, the sports injury intelligent analysis module is specifically used for: Patient medical data and cold-region sports injury-specific data are input into a pre-trained intelligent sports injury analysis model to obtain an intelligent sports injury analysis report output by the model. The sports injury intelligent analysis report includes the injury type, injury severity, risk assessment results, assessment recommendations, rehabilitation recommendations, and cold-weather-specific recommendations.

[0007] Optionally, the osteoporosis assessment module is specifically used for: Patient medical data and cold-region-specific osteoporosis data are input into a pre-trained osteoporosis assessment model to obtain an osteoporosis auxiliary assessment report output by the osteoporosis assessment model. The osteoporosis auxiliary assessment report includes osteoporosis risk level, osteoporosis type prediction, fracture risk prediction, and personalized intervention suggestions for cold regions.

[0008] Optionally, the osteoarthritis intelligent analysis module is specifically used for: Patient medical data and cold-region-specific osteoarthritis data are input into a pre-trained intelligent osteoarthritis analysis model to obtain an intelligent osteoarthritis analysis report output by the model. The intelligent analysis report on osteoarthritis includes osteoarthritis auxiliary assessment results, degree of joint degeneration, progression risk assessment, cold environment impact assessment, and personalized intervention suggestions for cold regions.

[0009] Optionally, the TCM orthopedic auxiliary assessment module is specifically used for: Patient visit data and cold-region TCM-specific data are input into a pre-trained TCM orthopedic auxiliary assessment model to obtain a TCM orthopedic auxiliary assessment report output by the TCM orthopedic auxiliary assessment model. The TCM orthopedic auxiliary assessment report includes syndrome differentiation, pathogenesis analysis, disease severity, disease progression trend, prescription recommendations, physiotherapy recommendations, lifestyle adjustments, and special suggestions for cold regions.

[0010] Optionally, the orthopedic health management module is specifically used for: Patient visit data and cold-region-specific orthopedic health data are input into a pre-trained orthopedic health management model to obtain an orthopedic health management plan output by the model. The orthopedic health management plan includes orthopedic disease risk assessment results, exercise plan, nutrition plan, and cold-region orthopedic disease prevention plan.

[0011] Optionally, the intelligent system used in the comprehensive digital diagnosis and treatment of orthopedics in cold regions also includes an intelligent preoperative planning system, an intelligent robot system, an intraoperative remote system, and a rehabilitation system; among which, The intelligent surgical planning system is used to plan preoperative procedures for hip and knee joint surgery, spinal surgery, sports medicine surgery, and trauma surgery based on patient medical data. The intelligent robot system is used to respond to intraoperative interaction needs, control equipment and conduct voice interaction based on these needs; provide real-time intraoperative prompts based on the preoperative surgical plan; generate surgical operation suggestions in real time during the operation and update the surgical plan in real time based on the response results of the surgical operation suggestions to obtain an updated real-time surgical plan; control the surgical robot to assist in performing surgical operations based on the preoperative surgical plan and intraoperative perception data; and update the control mode of the surgical robot in real time. The remote system is used to conduct preliminary assessments based on received remote medical data from users and to feed the preliminary assessment results back to the user's terminal; to conduct rehabilitation assessments based on received remote rehabilitation data from patients, and to update the patient's rehabilitation plan in real time based on the rehabilitation assessment results and cold environment data; to conduct live surgical demonstrations and automatically generate surgical teaching videos based on the live broadcast content; to control surgical robots according to received remote surgical instructions to realize remote surgery; and to conduct remote multidisciplinary consultations, intraoperative medical data retrieval, and remote pre-hospital emergency care collaboration. The rehabilitation system is used to conduct postoperative assessments based on patient postoperative data and preoperative planning, obtaining postoperative assessment results. Based on the postoperative assessment results, patient attribute information, and medical records, a personalized rehabilitation plan is generated for the patient. This personalized rehabilitation plan includes both in-hospital and out-of-hospital rehabilitation plans, with the out-of-hospital plan including cold-climate-specific rehabilitation suggestions. The system simulates joint mobility and pain indicators under cold-climate conditions based on the patient's in-hospital rehabilitation assessment data to assess whether the patient meets the discharge criteria for cold-climate environments. Finally, the system performs rehabilitation assessments based on the patient's out-of-hospital rehabilitation assessment data, obtaining rehabilitation assessment results, and updates the out-of-hospital rehabilitation plan accordingly.

[0012] Optionally, the intelligent surgical planning system is specifically used for: A three-dimensional model of the knee joint is obtained by performing three-dimensional reconstruction based on the acquired medical images of the knee joint. Preoperative planning is performed based on a three-dimensional model of the knee joint to obtain an initial preoperative planning scheme for knee replacement. Based on bone data associated with total knee arthroplasty, the initial preoperative planning scheme for knee arthroplasty was optimized to obtain the optimized preoperative planning scheme. After performing a simulated surgery based on the optimized preoperative planning scheme, postoperative motion simulation of the knee joint was conducted to determine whether various knee joint motion simulations could achieve the corresponding normal joint range of motion. If at least one knee joint motion simulation fails to achieve the corresponding normal joint range of motion, the optimized preoperative planning scheme will be adjusted until a target preoperative planning scheme that meets the motion simulation requirements is obtained.

[0013] Optionally, the intelligent robot system is specifically used for: Based on intraoperative perception data, preoperative surgical plans, and pre-trained surgical robot decision-making and execution models, surgical robot operation decisions and control commands are generated in real time; among them, The surgical robot decision and execution model includes a surgical decision branch and a robot execution branch. The surgical decision branch is used to generate surgical robot operation decisions in real time based on intraoperative perception data and preoperative surgical plans. The robot execution branch is used to generate surgical robot control commands based on the surgical robot operation decisions.

[0014] Optionally, the rehabilitation system is specifically used for: Based on the patient's in-hospital rehabilitation assessment data, simulate the patient's joint activity index and pain index under cold environment conditions, and assess whether the patient meets the discharge criteria under cold environment conditions based on the simulation results; If the patient meets the matching discharge criteria, a home rehabilitation assessment is conducted based on the patient's home temperature data and in-hospital rehabilitation assessment data to determine whether the patient meets the conditions for home rehabilitation.

[0015] Secondly, embodiments of this application provide an intelligent auxiliary assessment method for digital diagnosis and treatment of orthopedics in cold regions. The intelligent auxiliary assessment method is used to realize the functions of the intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions as described in any embodiment of the first aspect.

[0016] Thirdly, embodiments of this application provide an electronic device, which includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the functions of the intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the functions of a digital intelligent diagnosis and treatment system for orthopedics in cold regions.

[0018] The intelligent auxiliary assessment system, method, device, and computer-readable storage medium for digital diagnosis and treatment of orthopedics in cold regions, as described in this application, can perform auxiliary assessment of orthopedic trauma, intelligent analysis of sports injuries, auxiliary assessment of osteoporosis, intelligent analysis of osteoarthritis, auxiliary assessment of traditional Chinese medicine orthopedics, and orthopedic health management. It can meet the personalized diagnosis and treatment needs of orthopedic patients in cold regions and provide them with comprehensive and personalized diagnosis and treatment services. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the architecture of an intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions provided in one embodiment of this application; Figure 2 This is a schematic diagram of the overall architecture of the intelligent auxiliary assessment model in the intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions provided in one embodiment of this application; Figure 3 This is a schematic diagram of the architecture of the cold-region population skeletal digital model library in the intelligent auxiliary assessment system for digital diagnosis and treatment of cold-region orthopedics provided in one embodiment of this application; Figure 4 This is a schematic diagram of the architecture of a digital intelligent diagnosis and treatment system for all orthopedics in cold regions provided in one embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0021] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0022] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0023] To address the problems of existing technologies, this application provides an intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions. The following is a description of the intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions provided in this application. Figure 1 This is a schematic diagram of the architecture of an intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions, provided in one embodiment of this application. The system includes an orthopedic trauma auxiliary assessment module, a sports injury intelligent analysis module, an osteoporosis auxiliary assessment module, an osteoarthritis intelligent analysis module, a traditional Chinese medicine orthopedic auxiliary assessment module, and an orthopedic health management module. The orthopedic trauma auxiliary assessment module is used to intelligently analyze patients' orthopedic trauma and generate orthopedic trauma auxiliary assessment reports. The intelligent sports injury analysis module is used to intelligently analyze the degree of sports injury in patients and generate intelligent sports injury analysis reports. The osteoporosis auxiliary assessment module is used to assist in assessing the degree of osteoporosis in patients and generate an osteoporosis auxiliary assessment report; The osteoarthritis intelligent analysis module is used to intelligently analyze the degree of osteoarthritis in patients and generate an intelligent analysis report on osteoarthritis. The Traditional Chinese Medicine Orthopedics Auxiliary Assessment Module is used to conduct TCM syndrome differentiation of patients' symptoms and generate TCM orthopedics auxiliary assessment reports. The orthopedic health management module is used to provide patients with orthopedic health management suggestions and generate orthopedic health management plans.

[0024] Figure 2 This is a schematic diagram of the overall architecture of the intelligent auxiliary assessment model in the intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions provided in one embodiment of this application.

[0025] The intelligent auxiliary assessment model can be a model for specific purposes, such as an orthopedic trauma auxiliary assessment model, a sports injury intelligent analysis model, an osteoporosis assessment model, an osteoarthritis intelligent analysis model, a traditional Chinese medicine orthopedic auxiliary assessment model, or an orthopedic health management model. The model architecture of the intelligent auxiliary assessment model can be modularly adjusted according to specific purposes.

[0026] In some embodiments, the intelligent auxiliary assessment model can process the input patient visit data and cold-region-specific data accordingly to obtain the corresponding output results.

[0027] The patient's medical data may include historical assessment records, medical imaging data, historical medical test data, historical surgical data, historical medication data, and patient attribute information. The patient attribute information may include age, gender, weight, height, BMI, occupation, history of underlying diseases, and allergies. The historical assessment records may include assessment descriptions, such as "left knee arthritis." The medical imaging data may include, for example, X-rays, MRIs, and CT scans of the knee joint. The historical medical test data may include complete blood count data, blood biochemistry data, and urinalysis results. The historical surgical records may include the type of surgery, surgery time, rehabilitation records, and postoperative complication records. The historical medication records may include the name, dosage, frequency, and time of medication use.

[0028] Based on the model's usage requirements, the cold-region-specific data can be categorized into cold-region sports injury-specific data, cold-region osteoporosis-specific data, cold-region bone and joint-specific data, cold-region traditional Chinese medicine-specific data, cold-region orthopedic health-specific data, and cold-region orthopedic trauma-specific data. The cold-region-specific data includes bone mineral density data, vitamin D levels, blood circulation data, seasonal physiological changes data, temperature and humidity data, calcium and phosphorus metabolism indicators, and bone turnover markers. The specific data for cold-region sports injury, osteoporosis, bone and joint-specific data, and traditional Chinese medicine-specific data can be further categorized into cold-region sports injury-specific data, cold-region osteoporosis-specific data, cold-region bone and joint-specific data, and cold-region traditional Chinese medicine-specific data. The specific data types contained in orthopedic health-specific data and cold-region orthopedic trauma-specific data can be obtained through the cold-region population skeletal digital model library. The cold-region population skeletal digital model library contains skeletal characteristics of cold-region populations, norms of cold-region orthopedic diseases, cold-region-specific bone metabolism data, skeletal health behavior data, cold-region orthopedic diagnosis and treatment and postoperative recovery cases, and skeletal health risk assessment and intervention programs. The cold-region population skeletal digital model library is used for the digital diagnosis and treatment of all orthopedics in cold regions. It can store various data related to the diagnosis and treatment of bones in cold-region populations and participate in the entire process of digital diagnosis and treatment of all orthopedics in cold regions.

[0029] Figure 3 This is a schematic diagram of the architecture of the cold-region population skeletal digital model library in the intelligent auxiliary assessment system for digital diagnosis and treatment of cold-region orthopedics provided in one embodiment of this application.

[0030] The cold-region population skeletal digital model library includes skeletal characteristics of cold-region populations, norms of orthopedic diseases in cold regions, cold-region-specific bone metabolism data, skeletal health behavior data, cases of orthopedic diagnosis and treatment and postoperative recovery in cold regions, and skeletal health risk assessment and intervention programs.

[0031] The cold-region population skeletal digital model library can consist of data such as orthopedic patient cases, diagnosis and rehabilitation guidelines, patient rehabilitation records, and expert suggestions selected by experts. When building the cold-region population skeletal digital model library based on the above data, the above data can be converted into structured data and stored centrally to obtain a database-formatted cold-region population skeletal digital model library, which can be used for online diagnosis and treatment, personalized rehabilitation program generation, and orthopedic health management.

[0032] In some embodiments, the cold-region population skeletal digital model library can serve as a knowledge graph to participate in the model processing of the intelligent assisted assessment model, so that the output of the intelligent assisted assessment model is more consistent with the user profile of the cold-region population skeletal digital model library.

[0033] In some embodiments, the sports injury intelligent analysis module is specifically used for: Patient medical data and cold-region sports injury-specific data are input into a pre-trained intelligent sports injury analysis model to obtain an intelligent sports injury analysis report output by the model. The sports injury intelligent analysis report includes the injury type, injury severity, risk assessment results, assessment recommendations, rehabilitation recommendations, and cold-weather-specific recommendations.

[0034] The intelligent sports injury analysis model includes a multi-dimensional feature extraction module, a cross-modal feature alignment module, a content recommendation engine based on a digital model library of bones of people in cold regions, and a sports injury analysis engine. In some embodiments, the multidimensional feature extraction module may include a text feature extraction layer and an image feature extraction layer; the text feature extraction layer is configured as a BERT-based fine-tuned model, capable of extracting text features from input medical records and other related texts; the image feature extraction layer is configured as a ResNet-50 pre-trained model, capable of extracting features from input medical images and other images; the cross-modal feature alignment module includes a multimodal alignment network and an adaptive feature mapping layer; the multimodal alignment network uses contrastive learning and includes multiple projection heads (for patient consultation data and cold-region sports injury-specific data, respectively); the adaptive feature mapping layer is based on Transformer-based adaptive mapping, which can dynamically adjust the weights of different modal features, and the contrastive learning loss uses the InfoNCE loss function; the content recommendation engine and sports injury analysis engine based on the cold-region population skeletal digital model library can output corresponding content based on a graph neural network; the graph neural network can use a graph attention network (GAT) to process the cold-region population skeletal digital model library for sports injury analysis (injury type, injury degree, risk assessment results) and content recommendation (assessment suggestions, rehabilitation suggestions, cold-region-specific suggestions).

[0035] In some embodiments, the osteoporosis assessment module is specifically used for: Patient medical data and cold-region-specific osteoporosis data are input into a pre-trained osteoporosis assessment model to obtain an osteoporosis auxiliary assessment report output by the osteoporosis assessment model. The osteoporosis auxiliary assessment report includes osteoporosis risk level, osteoporosis type prediction, fracture risk prediction, and personalized intervention suggestions for cold regions.

[0036] The osteoporosis assessment model includes a multi-dimensional feature extraction module, a cross-modal feature alignment module, a content recommendation engine based on a digital model library of bones from cold-region populations, and an osteoporosis assessment engine. In some embodiments, the multidimensional feature extraction module may include a text feature extraction layer and an image feature extraction layer; the text feature extraction layer is configured as a BERT-based fine-tuned model, capable of extracting text features from input case data and other relevant texts; the image feature extraction layer is configured as a ResNet-50 pre-trained model, capable of extracting features from input medical images and other images; the cross-modal feature alignment module includes a multimodal alignment network and an adaptive feature mapping layer; the multimodal alignment network uses contrastive learning and includes multiple projection heads (for patient consultation data and cold-region osteoporosis-specific data, respectively); the adaptive feature mapping layer is based on Transformer-based adaptive mapping, which can dynamically adjust the weights of different modal features, and the contrastive learning loss uses the InfoNCE loss function; the content recommendation engine and osteoporosis assessment engine based on the cold-region population skeletal digital model library can output corresponding content based on a graph neural network; the graph neural network can use a graph attention network (GAT) to process the cold-region population skeletal digital model library for osteoporosis assessment (osteoporosis risk level, osteoporosis type prediction, fracture risk prediction) and content recommendation (personalized intervention suggestions for cold regions).

[0037] In some embodiments, the osteoarthritis intelligent analysis module is specifically used for: Patient medical data and cold-region-specific osteoarthritis data are input into a pre-trained intelligent osteoarthritis analysis model to obtain an intelligent osteoarthritis analysis report output by the model. The intelligent analysis report on osteoarthritis includes osteoarthritis auxiliary assessment results, degree of joint degeneration, progression risk assessment, cold environment impact assessment, and personalized intervention suggestions for cold regions.

[0038] The intelligent analysis model for osteoarthritis includes a multi-dimensional feature extraction module, a cross-modal feature alignment module, a content recommendation engine based on a digital model library of bones of people in cold regions, and an intelligent analysis engine for osteoarthritis. In some embodiments, the multidimensional feature extraction module may include a text feature extraction layer and an image feature extraction layer; the text feature extraction layer is configured as a BERT-based fine-tuned model, capable of extracting text features from input case data and other relevant texts; the image feature extraction layer is configured as a ResNet-50 pre-trained model, capable of extracting features from input medical images and other images; the cross-modal feature alignment module includes a multimodal alignment network and an adaptive feature mapping layer; the multimodal alignment network uses contrastive learning and includes multiple projection heads (for patient consultation data and cold-region osteoarthritis-specific data, respectively); the adaptive feature mapping layer is based on Transformer-based adaptive mapping, which can dynamically adjust the weights of different modal features, and the contrastive learning loss uses the InfoNCE loss function; the content recommendation engine and osteoarthritis intelligent analysis engine based on the cold-region population skeletal digital model library can output corresponding content based on a graph neural network; the graph neural network can use a graph attention network (GAT) to process the cold-region population skeletal digital model library for intelligent analysis of osteoarthritis (osteoarthritis auxiliary assessment results, joint degeneration degree, progression risk assessment, cold-region environmental impact assessment) and content recommendation (cold-region personalized intervention suggestions).

[0039] In some embodiments, the traditional Chinese medicine orthopedic auxiliary assessment module is specifically used for: Patient visit data and cold-region TCM-specific data are input into a pre-trained TCM orthopedic auxiliary assessment model to obtain a TCM orthopedic auxiliary assessment report output by the TCM orthopedic auxiliary assessment model. The TCM orthopedic auxiliary assessment report includes syndrome differentiation, pathogenesis analysis, disease severity, disease progression trend, prescription recommendations, physiotherapy recommendations, lifestyle adjustments, and special suggestions for cold regions.

[0040] The TCM orthopedic auxiliary assessment model includes a multi-dimensional feature extraction module, a cross-modal feature alignment module, a content recommendation engine based on a digital model library of bones of people in cold regions, and a TCM orthopedic auxiliary assessment engine. In some embodiments, the multidimensional feature extraction module may include a text feature extraction layer and an image feature extraction layer; the text feature extraction layer architecture is set to a BERT-based fine-tuned model, which can extract text features from input medical records and other related texts; the image feature extraction layer architecture is set to a ResNet-50 pre-trained model, which can extract features from input medical images and other images; the cross-modal feature alignment module includes a multimodal alignment network and an adaptive feature mapping layer, wherein the multimodal alignment network uses contrastive learning and includes multiple projection heads (respectively for patient consultation data and cold-region TCM-specific data); The adaptive feature mapping layer is based on Transformer-based adaptive mapping, which can dynamically adjust the weights of different modal features. The contrastive learning loss uses the InfoNCE loss function. The content recommendation engine and TCM orthopedic auxiliary assessment engine based on the cold-region population skeletal digital model library can output corresponding content based on graph neural networks. The graph neural network can use the graph attention network GAT to process the cold-region population skeletal digital model library to perform TCM orthopedic auxiliary assessment (syndrome differentiation, pathogenesis analysis, disease severity) and content recommendation (disease development trend, prescription recommendation, physiotherapy plan recommendation, lifestyle adjustment recommendation, cold-region-specific suggestions).

[0041] In some embodiments, the orthopedic health management module is specifically used for: Patient visit data and cold-region-specific orthopedic health data are input into a pre-trained orthopedic health management model to obtain an orthopedic health management plan output by the model. The orthopedic health management plan includes orthopedic disease risk assessment results, exercise plan, nutrition plan, and cold-region orthopedic disease prevention plan.

[0042] The orthopedic health management model includes a multi-dimensional feature extraction module, a cross-modal feature alignment module, a content recommendation engine based on a digital model library of bones of people in cold regions, and an orthopedic health management engine. In some embodiments, the multidimensional feature extraction module may include a text feature extraction layer and an image feature extraction layer; the text feature extraction layer is configured as a BERT-based fine-tuned model, capable of extracting text features from input medical records and other related texts; the image feature extraction layer is configured as a ResNet-50 pre-trained model, capable of extracting features from input medical images and other images; the cross-modal feature alignment module includes a multimodal alignment network and an adaptive feature mapping layer; the multimodal alignment network uses contrastive learning and includes multiple projection heads (for patient consultation data and cold-region orthopedic health-specific data, respectively); the adaptive feature mapping layer is based on Transformer-based adaptive mapping, which can dynamically adjust the weights of different modal features, and the contrastive learning loss uses the InfoNCE loss function; the content recommendation engine and orthopedic health management engine based on the cold-region population skeletal digital model library can output corresponding content based on a graph neural network; the graph neural network can use a graph attention network (GAT) to process the cold-region population skeletal digital model library for orthopedic health management (orthopedic disease risk assessment results) and content recommendation (exercise programs, nutrition programs, and cold-region orthopedic disease prevention programs).

[0043] In some embodiments, the orthopedic trauma auxiliary assessment module is specifically used for: Patient visit data and cold-region-specific orthopedic trauma data are input into a pre-trained orthopedic trauma auxiliary assessment model to obtain an orthopedic trauma auxiliary assessment report output by the model. The orthopedic trauma auxiliary assessment report includes the type of orthopedic trauma, the orthopedic trauma coping plan, rehabilitation suggestions, and cold-region-specific suggestions.

[0044] The orthopedic trauma auxiliary assessment model includes a multi-dimensional feature extraction module, a cross-modal feature alignment module, a content recommendation engine based on a digital model library of bones of people in cold regions, and an orthopedic trauma auxiliary assessment engine. In some embodiments, the multidimensional feature extraction module may include a text feature extraction layer and an image feature extraction layer; the text feature extraction layer is configured as a BERT-based fine-tuned model, capable of extracting text features from input medical records and other related texts; the image feature extraction layer is configured as a ResNet-50 pre-trained model, capable of extracting features from input medical images and other images; the cross-modal feature alignment module includes a multimodal alignment network and an adaptive feature mapping layer; the multimodal alignment network uses contrastive learning and includes multiple projection heads (for patient consultation data and cold-region orthopedic trauma-specific data, respectively); the adaptive feature mapping layer is based on Transformer-based adaptive mapping, which can dynamically adjust the weights of different modal features, and the contrastive learning loss uses the InfoNCE loss function; the content recommendation engine and orthopedic trauma auxiliary assessment engine based on the cold-region population skeletal digital model library can output corresponding content based on a graph neural network; the graph neural network can use a graph attention network (GAT) to process the cold-region population skeletal digital model library for orthopedic trauma auxiliary assessment (orthopedic trauma type, orthopedic trauma coping strategy) and content recommendation (rehabilitation suggestions, cold-region-specific suggestions).

[0045] Figure 4 This is a schematic diagram of the architecture of a digital intelligent diagnosis and treatment system for all orthopedics in cold regions provided in one embodiment of this application.

[0046] In some embodiments, the intelligent system used in the comprehensive digital diagnosis and treatment of orthopedics in cold regions further includes an intelligent surgical planning system, an intelligent robot system, a remote system, and a rehabilitation system; wherein, The intelligent surgical planning system is used to plan preoperative procedures for hip and knee joint surgery, spinal surgery, sports medicine surgery, and trauma surgery based on patient medical data. The intelligent robot system is used to respond to intraoperative interaction needs, control equipment and conduct voice interaction based on these needs; provide real-time intraoperative prompts based on the preoperative surgical plan; generate surgical operation suggestions in real time during the operation and update the surgical plan in real time based on the response results of the surgical operation suggestions to obtain an updated real-time surgical plan; control the surgical robot to assist in performing surgical operations based on the preoperative surgical plan and intraoperative perception data; and update the control mode of the surgical robot in real time. The remote system is used to conduct preliminary assessments based on received remote medical data from users and to feed the preliminary assessment results back to the user's terminal; to conduct rehabilitation assessments based on received remote rehabilitation data from patients, and to update the patient's rehabilitation plan in real time based on the rehabilitation assessment results and cold environment data; to conduct live surgical demonstrations and automatically generate surgical teaching videos based on the live broadcast content; to control surgical robots according to received remote surgical instructions to realize remote surgery; and to conduct remote multidisciplinary consultations, intraoperative medical data retrieval, and remote pre-hospital emergency care collaboration. The rehabilitation system is used to conduct postoperative assessments based on patient postoperative data and preoperative planning, obtaining postoperative assessment results. Based on the postoperative assessment results, patient attribute information, and medical records, a personalized rehabilitation plan is generated for the patient. This personalized rehabilitation plan includes both in-hospital and out-of-hospital rehabilitation plans, with the out-of-hospital plan including cold-climate-specific rehabilitation suggestions. The system simulates joint mobility and pain indicators under cold-climate conditions based on the patient's in-hospital rehabilitation assessment data to assess whether the patient meets the discharge criteria for cold-climate environments. Finally, the system performs rehabilitation assessments based on the patient's out-of-hospital rehabilitation assessment data, obtaining rehabilitation assessment results, and updates the out-of-hospital rehabilitation plan accordingly.

[0047] In some embodiments, the intelligent surgical planning system is specifically used for: A three-dimensional model of the knee joint is obtained by performing three-dimensional reconstruction based on the acquired medical images of the knee joint. Preoperative planning is performed based on a three-dimensional model of the knee joint to obtain an initial preoperative planning scheme for knee replacement. Based on bone data associated with total knee arthroplasty, the initial preoperative planning scheme for knee arthroplasty was optimized to obtain the optimized preoperative planning scheme. After performing a simulated surgery based on the optimized preoperative planning scheme, postoperative motion simulation of the knee joint was conducted to determine whether various knee joint motion simulations could achieve the corresponding normal joint range of motion. If at least one knee joint motion simulation fails to achieve the corresponding normal joint range of motion, the optimized preoperative planning scheme will be adjusted until a target preoperative planning scheme that meets the motion simulation requirements is obtained.

[0048] The intelligent robot system is used to control surgical robots, such as embodied intelligent robots, used to perform surgery.

[0049] In some embodiments, the intelligent robot system is specifically used for: Based on intraoperative perception data, preoperative surgical plans, and pre-trained surgical robot decision-making and execution models, surgical robot operation decisions and control commands are generated in real time; among them, The surgical robot decision and execution model includes a surgical decision branch and a robot execution branch. The surgical decision branch is used to generate surgical robot operation decisions in real time based on intraoperative perception data and preoperative surgical plans. The robot execution branch is used to generate surgical robot control commands based on the surgical robot operation decisions.

[0050] The surgical robot decision-making and execution model includes a multimodal feature extraction module, a surgical plan semantic encoder, a feature fusion module, a surgical decision branch, a robot execution branch, and a safety monitoring module.

[0051] The multimodal feature extraction module is used to extract features from intraoperative perception data, which includes patient physiological data, real-time audio data, real-time video data, surgical instrument tracking data, force sensor data, surgical robot status data, etc. For intraoperative perception data of different modalities, a modality-matching feature extractor can be used for feature extraction. For example, video data can be extracted using feature extraction networks with visual feature extraction capabilities, such as MobileNetV3-Small. The surgical plan semantic encoder can be extracted using a feature extractor with structured data feature extraction capabilities, and its model architecture can be, for example, GNN.

[0052] The feature fusion module can employ a network model with contextual semantic fusion capabilities. Its input consists of the output features of the surgical plan semantic encoder and the multimodal feature extraction module, which fuse the surgical plan and intraoperative perception data to output a 512-dimensional context vector. The surgical decision branch can be structured as a 2-layer BiLSTM + classification head + regression head, with the fused context vector as its input. It is used to output the action category and its confidence level, and can simultaneously output an attention heatmap and decision basis text to enhance the interpretability and safety of the surgical robot operation. The robot execution branch receives the robot's current state (including pose and other state data), an environmental obstacle map (the current operating room environment), and the target pose (i.e., the next motion target) corresponding to the action category output by the surgical decision branch. It outputs control commands, which may include 10 frames of look-ahead trajectory points (each point containing joint angles and end-effector operation parameters such as tool opening and closing) plus real-time velocity commands. The robot execution branch may include an obstacle perception module, a trajectory planner, a neural IK solver, and an adaptive controller. The obstacle perception module can be based on a lightweight PointNet architecture to detect obstacles in the surgical field of interest. For perception, the trajectory planner can adopt ConditionalVAE, with both its encoding structure and decoding results using LSTM. Its input is the current pose + moving target + obstacle, and the output is the planned look-ahead trajectory points. The neural IK solver architecture can adopt MLP, which converts the Cartesian pose in Cartesian coordinates into joint angles to obtain joint angle parameters for controlling the movement of the robotic arm. The adaptive controller can adopt LSTM-PID, which is used to perform adaptive control based on the current joint angle, target angle, error integral, and force feedback data, and outputs joint velocity correction to achieve adaptive adjustment based on real-time intraoperative situational awareness.

[0053] The safety monitoring module is embedded in the overall architecture through hard constraints. It can implement safety constraints for the surgical robot through real-time collision detection of trajectory points, joint velocity / acceleration limiting, and force control mode (resistance control is triggered when the force exceeds a threshold). The safety monitoring module can be implemented with independent hardware to ensure the safety of surgical robot operation. For example, safety monitoring can be implemented through FPGA + independent MCU to physically isolate it from the surgical decision branch and robot execution branch. The safety monitoring module can send hard interrupt signals to the surgical decision branch and robot execution branch to achieve safety protection when an anomaly is detected. The robot execution branch can provide real-time feedback on the execution status to the surgical decision branch so that the surgical decision branch can be aware of the robot's execution status in real time.

[0054] In some embodiments, the intelligent robot system is specifically used for: Based on the control modes corresponding to each surgical stage planned in the preoperative plan and the intraoperative sensing data, the surgical robot is controlled to assist in performing surgical operations according to the corresponding control modes. The control modes of surgical robots include local control, remote control, and autonomous control.

[0055] Specifically, during the preoperative planning stage, control modes corresponding to each surgical stage can be planned based on the surgeon's proficiency and success rate in each surgical procedure. During the operation, the surgical robot is controlled according to the corresponding control mode as it progresses to the corresponding surgical stage.

[0056] In some embodiments, the intelligent robot system is specifically used for: In response to meeting the conditions for updating the control mode of the surgical robot, the control mode of the surgical robot is updated in real time; wherein... Entering the target surgical stage, performing the target surgical procedure, remote connection interrupted, receiving a control mode switching command.

[0057] Specifically, when entering the target surgical stage, the system can switch between local control and remote or autonomous control, between remote control and local or autonomous control, and between autonomous control and local or remote control; when performing the target surgical operation, the system can switch between local control and remote or autonomous control, between remote control and local or autonomous control, and between autonomous control and local or remote control; in the event of a remote connection interruption, the system can switch between remote control and local or autonomous control; and upon receiving a control mode switching command, the system can switch between local control and remote or autonomous control, between remote control and local or autonomous control, and between autonomous control and local or remote control.

[0058] In some embodiments, the intelligent robot system is specifically used for: Determine the current surgical stage based on intraoperative sensory data; Based on the surgical procedure guidelines corresponding to the current surgical stage, provide surgical procedure guidelines.

[0059] In some embodiments, the intelligent robot system is specifically used for: Intraoperative perception data and preoperative surgical plans are input into a pre-trained intelligent surgical collaborative decision-making model to obtain real-time surgical operation suggestions output by the intelligent surgical collaborative decision-making model. Intraoperative sensing data includes patient physiological data, real-time surgical audio and video data, and surgical instrument tracking data.

[0060] The intraoperative perception data includes patient physiological data, real-time surgical audio and video data, and surgical instrument tracking data. The intelligent surgical collaborative decision-making model is a generative model capable of processing real-time serialized data. The input of the intelligent surgical collaborative decision-making model includes serialized intraoperative perception data and prompt information. The prompt information may include intraoperative interaction requirements (such as confirming the current surgical progress, estimating the remaining surgical time, etc.).

[0061] The output of the intelligent surgical collaborative decision-making model includes real-time intraoperative prompts, intraoperative operation suggestions, and surgical progress predictions; among which... The model architecture of the intraoperative real-time alert module can be a CNN+BiLSTM+Attention mechanism. The number of CNN convolutional kernels can be 32 to extract local features of vital signs; the kernel size can be 3*3 to optimize feature extraction; the number of BiLSTM hidden units can be set to 64 to handle temporal data; the number of Attention heads can be set to 8 to optimize multi-dimensional feature fusion; the loss function can be set to a weighted cross-entropy loss function to focus on identifying high-risk events (high-risk surgical procedures, etc.); the optimizer can be set to AdamW to improve the convergence speed during training; and the learning rate can be set to 0.001 to control the learning speed.

[0062] The model architecture of the intraoperative operation suggestion module can be DQN + expert knowledge base. It generates initial surgical suggestions based on DQN and filters and sorts them according to the expert knowledge base to output high-value surgical operation suggestions. The state space dimension can be set to 50, which includes surgical stage, vital signs, risk level, etc.; the action space size can be set to 100, which is used to define the number of possible operation suggestions; the discount factor can be set to 0.95 to encourage future reward weights; the learning rate can be set to 0.001 to control the learning speed; the experience replay buffer size can be set to 100,000 to store historical experience; the batch size can be set to 64; and the target network update frequency can be set to 100 to balance training effect and training efficiency.

[0063] The model architecture for the surgical progress prediction module can be an LSTM + Attention mechanism, which can predict the remaining time based on historical data, adjust the prediction according to the current risk level, and generate a confidence interval. The LSTM hidden units can be set to 128 for temporal modeling; the number of Attention heads can be set to 4 for feature fusion; the sliding window size can be dynamically adjusted according to the surgical type, for example, it can be set to 30 minutes; the prediction step size can be set to 5 minutes; and the loss function can be set to MAE to evaluate the prediction accuracy.

[0064] In some embodiments, the intelligent robot system may also determine the current surgical progress based on intraoperative perception data in response to the fulfillment of surgical progress confirmation conditions; wherein, the surgical progress confirmation conditions include entering the target surgical stage, performing the target surgical operation, and receiving a surgical progress confirmation instruction.

[0065] In some embodiments, the rehabilitation system is specifically used for: Based on the patient's in-hospital rehabilitation assessment data, simulate the patient's joint activity index and pain index under cold environment conditions, and assess whether the patient meets the discharge criteria under cold environment conditions based on the simulation results; If the patient meets the matching discharge criteria, a home rehabilitation assessment is conducted based on the patient's home temperature data and in-hospital rehabilitation assessment data to determine whether the patient meets the conditions for home rehabilitation.

[0066] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown.

[0067] The electronic device may include a processor 501 and a memory 502 storing computer program instructions.

[0068] Specifically, the processor 501 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0069] Memory 502 may include mass storage for data or instructions. For example, and not limitingly, memory 502 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 502 may include removable or non-removable (or fixed) media. Where appropriate, memory 502 may be internal or external to an electronic device. In a particular embodiment, memory 502 may be a non-volatile solid-state memory.

[0070] In one embodiment, memory 502 may be read-only memory (ROM). In one embodiment, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0071] The processor 501 reads and executes computer program instructions stored in the memory 502 to implement the functions of the intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions as described in any of the above embodiments.

[0072] In one example, the electronic device may also include a communication interface 503 and a bus 510. Wherein, as... Figure 5 As shown, the processor 501, memory 502, and communication interface 503 are connected through bus 510 and complete communication with each other.

[0073] The communication interface 503 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0074] Bus 510 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-E) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 510 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0075] Alternatively, embodiments of this application can be implemented using a computer-readable storage medium. This computer-readable storage medium stores computer program instructions; when executed by a processor, these computer program instructions implement the functions of the intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedic diseases in cold regions as described in any of the above embodiments.

[0076] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0077] The functional modules shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0078] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0079] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0080] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A digital intelligent diagnosis and treatment system for orthopedics in cold regions, characterized in that, include: The orthopedic trauma auxiliary assessment module is used to intelligently analyze patients' orthopedic trauma and generate orthopedic trauma auxiliary assessment reports. The intelligent sports injury analysis module is used to intelligently analyze the degree of sports injury in patients and generate intelligent sports injury analysis reports. The osteoporosis auxiliary assessment module is used to assist in assessing the degree of osteoporosis in patients and generate an osteoporosis auxiliary assessment report; The osteoarthritis intelligent analysis module is used to intelligently analyze the degree of osteoarthritis in patients and generate an intelligent analysis report on osteoarthritis. The Traditional Chinese Medicine Orthopedics Auxiliary Assessment Module is used to conduct TCM syndrome differentiation of patients' symptoms and generate TCM orthopedics auxiliary assessment reports. The orthopedic health management module is used to provide patients with orthopedic health management suggestions and generate orthopedic health management plans.

2. The intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions according to claim 1, characterized in that, The sports injury intelligent analysis module is specifically used for: Patient medical data and cold-region sports injury-specific data are input into a pre-trained intelligent sports injury analysis model to obtain an intelligent sports injury analysis report output by the model. The sports injury intelligent analysis report includes the injury type, injury severity, risk assessment results, assessment recommendations, rehabilitation recommendations, and cold-weather-specific recommendations.

3. The intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions according to claim 1 or 2, characterized in that, The osteoporosis assessment module is specifically used for: Patient medical data and cold-region-specific osteoporosis data are input into a pre-trained osteoporosis assessment model to obtain an osteoporosis auxiliary assessment report output by the osteoporosis assessment model. The osteoporosis auxiliary assessment report includes osteoporosis risk level, osteoporosis type prediction, fracture risk prediction, and personalized intervention suggestions for cold regions.

4. The intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions according to claim 1 or 2, characterized in that, The osteoarthritis intelligent analysis module is specifically used for: Patient medical data and cold-region-specific osteoarthritis data are input into a pre-trained intelligent osteoarthritis analysis model to obtain an intelligent osteoarthritis analysis report output by the model. The intelligent analysis report on osteoarthritis includes osteoarthritis auxiliary assessment results, degree of joint degeneration, progression risk assessment, cold environment impact assessment, and personalized intervention suggestions for cold regions.

5. The intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions according to claim 1, characterized in that, The TCM orthopedic auxiliary assessment module is specifically used for: Patient visit data and cold-region TCM-specific data are input into a pre-trained TCM orthopedic auxiliary assessment model to obtain a TCM orthopedic auxiliary assessment report output by the TCM orthopedic auxiliary assessment model. The TCM orthopedic auxiliary assessment report includes syndrome differentiation, pathogenesis analysis, disease severity, disease progression trend, prescription recommendations, physiotherapy recommendations, lifestyle adjustments, and special suggestions for cold regions.

6. The intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions according to claim 1 or 2, characterized in that, The orthopedic health management module is specifically used for: Patient visit data and cold-region-specific orthopedic health data are input into a pre-trained orthopedic health management model to obtain an orthopedic health management plan output by the model. The orthopedic health management plan includes orthopedic disease risk assessment results, exercise plan, nutrition plan, and cold-region orthopedic disease prevention plan.

7. The intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions according to claim 1, characterized in that, The intelligent system used in the comprehensive digital diagnosis and treatment of orthopedics in cold regions also includes an intelligent surgical planning system, an intelligent robot system, a remote system, and a rehabilitation system; among which, The intelligent surgical planning system is used to plan preoperative procedures for hip and knee joint surgery, spinal surgery, sports medicine surgery, and trauma surgery based on patient medical data. The intelligent robot system is used to respond to intraoperative interaction needs, control equipment and conduct voice interaction based on these needs; provide real-time intraoperative prompts based on the preoperative surgical plan; generate surgical operation suggestions in real time during the operation and update the surgical plan in real time based on the response results of the surgical operation suggestions to obtain an updated real-time surgical plan; control the surgical robot to assist in performing surgical operations based on the preoperative surgical plan and intraoperative perception data; and update the control mode of the surgical robot in real time. The remote system is used to conduct preliminary assessments based on received remote medical data from users and to feed the preliminary assessment results back to the user's terminal; to conduct rehabilitation assessments based on received remote rehabilitation data from patients, and to update the patient's rehabilitation plan in real time based on the rehabilitation assessment results and cold environment data; to conduct live surgical demonstrations and automatically generate surgical teaching videos based on the live broadcast content; to control surgical robots according to received remote surgical instructions to realize remote surgery; and to conduct remote multidisciplinary consultations, intraoperative medical data retrieval, and remote pre-hospital emergency care collaboration. The rehabilitation system is used to conduct postoperative assessments based on patient postoperative data and preoperative planning, obtaining postoperative assessment results. Based on the postoperative assessment results, patient attribute information, and medical records, a personalized rehabilitation plan is generated for the patient. This personalized rehabilitation plan includes both in-hospital and out-of-hospital rehabilitation plans, with the out-of-hospital plan including cold-climate-specific rehabilitation suggestions. The system simulates joint mobility and pain indicators under cold-climate conditions based on the patient's in-hospital rehabilitation assessment data to assess whether the patient meets the discharge criteria for cold-climate environments. Finally, the system performs rehabilitation assessments based on the patient's out-of-hospital rehabilitation assessment data, obtaining rehabilitation assessment results, and updates the out-of-hospital rehabilitation plan accordingly.

8. The intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions according to claim 7, characterized in that, The intelligent surgical planning system is specifically used for: A three-dimensional model of the knee joint is obtained by performing three-dimensional reconstruction based on the acquired medical images of the knee joint. Preoperative planning is performed based on a three-dimensional model of the knee joint to obtain an initial preoperative planning scheme for knee replacement. Based on bone data associated with total knee arthroplasty, the initial preoperative planning scheme for knee arthroplasty was optimized to obtain the optimized preoperative planning scheme. After performing a simulated surgery based on the optimized preoperative planning scheme, postoperative motion simulation of the knee joint was conducted to determine whether various knee joint motion simulations could achieve the corresponding normal joint range of motion. If at least one knee joint motion simulation fails to achieve the corresponding normal joint range of motion, the optimized preoperative planning scheme will be adjusted until a target preoperative planning scheme that meets the motion simulation requirements is obtained.

9. The intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions according to claim 7 or 8, characterized in that, The intelligent robot system is specifically used for: Based on intraoperative perception data, preoperative surgical plans, and pre-trained surgical robot decision-making and execution models, surgical robot operation decisions and control commands are generated in real time; among them, The surgical robot decision and execution model includes a surgical decision branch and a robot execution branch. The surgical decision branch is used to generate surgical robot operation decisions in real time based on intraoperative perception data and preoperative surgical plans. The robot execution branch is used to generate surgical robot control commands based on the surgical robot operation decisions.

10. The intelligent auxiliary assessment system for digital diagnosis and treatment of orthopedics in cold regions according to claim 7 or 8, characterized in that, The rehabilitation system is specifically used for: Based on the patient's in-hospital rehabilitation assessment data, simulate the patient's joint activity index and pain index under cold environment conditions, and assess whether the patient meets the discharge criteria under cold environment conditions based on the simulation results; If the patient meets the matching discharge criteria, a home rehabilitation assessment is conducted based on the patient's home temperature data and in-hospital rehabilitation assessment data to determine whether the patient meets the conditions for home rehabilitation.

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