Cold region orthopedics department digital intelligence diagnosis and treatment intelligent robot system

Abstract

The invention provides a cold region orthopedics department digital intelligence diagnosis and treatment intelligent robot system, which comprises an intraoperative interaction response module used for responding to an intraoperative interaction demand and carrying out equipment control and voice interaction according to the intraoperative interaction demand; the intraoperative instant prompt and operation suggestion module is used for performing intraoperative instant prompt according to the planned operation scheme; and generating a surgical operation suggestion in real time during the surgery, and updating the surgical scheme in real time according to a response result of the surgical operation suggestion to obtain an updated real-time surgical scheme, the intraoperative intelligent control module is used for controlling the surgical robot to assist in performing surgical operation according to the planned surgical scheme and the intraoperative sensing data; and updating the control mode of the surgical robot in real time.

Description

Technical Field

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

[0002] Traditional orthopedic surgeries are often performed independently by doctors, and surgical planning and operation rely too heavily on the doctor's experience, which may increase surgical risks and result in low precision and stability.

[0003] Therefore, computer software-based planning systems can assist doctors in planning, but the intraoperative procedures still rely on the doctor's surgical experience, resulting in low surgical efficiency and stability. Summary of the Invention

[0004] This application provides an intelligent robot system for digital diagnosis and treatment of orthopedics in cold regions, which can realize intraoperative interactive response, real-time prompts and operation suggestions in the book, and intelligent control during the operation, thereby improving surgical efficiency and surgical results.

[0005] In a first aspect, embodiments of this application provide an intelligent robot system for digital diagnosis and treatment of orthopedics in cold regions, comprising: The intraoperative interaction response module is used to respond to intraoperative interaction needs and perform equipment control and voice interaction based on these needs. The intraoperative real-time prompt and operation suggestion module is used to provide real-time prompts during the operation based on the planned surgical plan; and to 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 the updated real-time surgical plan. The intraoperative intelligent control module is used to control the surgical robot to assist in the execution of surgical operations based on the planned surgical plan and intraoperative sensing data; and to update the control mode of the surgical robot in real time.

[0006] Optionally, the intraoperative intelligent control module is specifically used for: Based on intraoperative perception data, the planned surgical procedure, and a pre-trained surgical robot decision-making and execution model, the system generates real-time surgical robot operation decisions and control commands; among which, 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 the planned surgical plan. The robot execution branch is used to generate surgical robot control commands based on the surgical robot operation decisions.

[0007] Optionally, the intraoperative intelligent control module is specifically used for: Based on the control modes and intraoperative perception data corresponding to each stage of the surgery, 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.

[0008] Optionally, the intraoperative intelligent control module 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.

[0009] Optionally, the intraoperative real-time prompt and operation suggestion module 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.

[0010] Optionally, the intraoperative real-time prompt and operation suggestion module is specifically used for: The intraoperative perception data and the planned surgical plan 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.

[0011] Optionally, the intelligent system used in the comprehensive digital diagnosis and treatment of orthopedics in cold regions also includes an intelligent auxiliary assessment system, an intelligent surgical planning system, a remote system, and a rehabilitation system; among which, The intelligent auxiliary assessment system is used to conduct auxiliary assessments of all orthopedic diseases in cold regions based on patient medical data. The intelligent surgical planning system is used to plan hip and knee joint surgeries, spinal surgeries, sports medicine surgeries, and trauma surgeries based on patient medical data. 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 plans, obtaining postoperative assessment results; and to generate personalized rehabilitation plans for patients based on postoperative assessment results, patient attribute information, and medical records. These personalized rehabilitation plans include in-hospital and out-of-hospital rehabilitation plans, with the out-of-hospital plan including cold-climate-specific rehabilitation recommendations. 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, it conducts rehabilitation assessments based on the patient's out-of-hospital rehabilitation assessment data to obtain rehabilitation assessment results, which are then used to update the out-of-hospital rehabilitation plan.

[0012] Optionally, the intelligent assisted evaluation system 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.

[0013] Optionally, the remote system is specifically used for: A rehabilitation assessment is conducted based on the received remote rehabilitation data from the patient to determine the patient's current rehabilitation progress; wherein, the remote rehabilitation data from the patient includes exercise verification videos, medical imaging data, medical test data, and rehabilitation progress description information; Based on the deviation between the patient's current recovery progress and the estimated recovery progress, the patient's recovery plan is updated in real time in conjunction with cold-region environmental data.

[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 a control method for a digital intelligent robot system for orthopedic diagnosis and treatment in cold regions, wherein the intelligent method is used to implement the functions of the digital intelligent robot system for orthopedic diagnosis and treatment 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 realizes the functions of the intelligent robot 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 an intelligent robot system for digital diagnosis and treatment of orthopedic diseases in cold regions.

[0018] The intelligent robot system, control method, device, and computer-readable storage medium for digital diagnosis and treatment of orthopedics in cold regions, as described in this application, provide an intraoperative interaction response module, an intraoperative real-time prompt and operation suggestion module, and an intraoperative intelligent control module. These modules can control the device and perform voice interaction according to intraoperative interaction needs; provide real-time intraoperative prompts according to the planned 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. Furthermore, based on the planned surgical plan and intraoperative perception data, the modules can control the surgical robot to assist in performing surgical operations and update the control mode of the surgical robot in real-time, thereby achieving active and passive intelligent control of the surgical robot during surgery and improving surgical efficiency and outcomes. 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 robot 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 architecture of the surgical robot decision-making and execution model in the intelligent robot 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 intelligent control decision-making for surgical robots in a digital intelligent diagnosis and treatment system for orthopedics in cold regions 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 robot system for digital diagnosis and treatment of orthopedics in cold regions. The intelligent robot system for digital diagnosis and treatment of orthopedics in cold regions provided in this application will be described below. Figure 1 This is a schematic diagram of the architecture of an intelligent robot system for digital diagnosis and treatment of orthopedics in cold regions, provided in one embodiment of this application. The intelligent robot system for digital diagnosis and treatment of orthopedics in cold regions includes: wherein, The intraoperative interaction response module is used to respond to intraoperative interaction needs and perform equipment control and voice interaction based on these needs. The intraoperative real-time prompt and operation suggestion module is used to provide real-time prompts during the operation based on the planned surgical plan; and to 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 the updated real-time surgical plan. The intraoperative intelligent control module is used to control the surgical robot to assist in the execution of surgical operations based on the planned surgical plan and intraoperative sensing data; and to update the control mode of the surgical robot in real time.

[0024] In some embodiments, the intraoperative intelligent control module is specifically used for: Based on intraoperative perception data, the planned surgical procedure, and a pre-trained surgical robot decision-making and execution model, the system generates real-time surgical robot operation decisions and control commands; among which, 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 the planned surgical plan. The robot execution branch is used to generate surgical robot control commands based on the surgical robot operation decisions.

[0025] Figure 2 This is a schematic diagram of the architecture of the surgical robot decision-making and execution model in the intelligent robot system for digital diagnosis and treatment of orthopedics in cold regions provided in one embodiment of this application.

[0026] 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.

[0027] 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 a feature extraction network 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.

[0028] 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.

[0029] 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.

[0030] In some embodiments, the intraoperative intelligent control module is specifically used for: Based on the control modes and intraoperative perception data corresponding to each stage of the surgery, 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.

[0031] Specifically, during the 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.

[0032] In some embodiments, the intraoperative intelligent control module 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.

[0033] Figure 3 This is a schematic diagram illustrating the intelligent control decision-making of the surgical robot in a digital intelligent diagnosis and treatment system for orthopedics in cold regions, provided in one embodiment of this application.

[0034] 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.

[0035] In some embodiments, the intraoperative real-time prompting and operation suggestion module 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.

[0036] In some embodiments, the intraoperative real-time prompting and operation suggestion module is specifically used for: The intraoperative perception data and the planned surgical plan 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.

[0037] 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.).

[0038] 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.

[0039] 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.

[0040] 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.

[0041] In some embodiments, the current surgical progress can be determined based on intraoperative sensing 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.

[0042] 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.

[0043] In some embodiments, the intelligent system used in the comprehensive digital diagnosis and treatment of orthopedics in cold regions further includes an intelligent auxiliary assessment system, an intelligent surgical planning system, a remote system, and a rehabilitation system; wherein... The intelligent auxiliary assessment system is used to conduct auxiliary assessments of all orthopedic diseases in cold regions based on patient medical data. The intelligent surgical planning system is used to plan hip and knee joint surgeries, spinal surgeries, sports medicine surgeries, and trauma surgeries based on patient medical data. 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 plans, obtaining postoperative assessment results; and to generate personalized rehabilitation plans for patients based on postoperative assessment results, patient attribute information, and medical records. These personalized rehabilitation plans include in-hospital and out-of-hospital rehabilitation plans, with the out-of-hospital plan including cold-climate-specific rehabilitation recommendations. 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, it conducts rehabilitation assessments based on the patient's out-of-hospital rehabilitation assessment data to obtain rehabilitation assessment results, which are then used to update the out-of-hospital rehabilitation plan.

[0044] In some embodiments, the intelligent assisted assessment system 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.

[0045] In some embodiments, the remote system is specifically used for: A rehabilitation assessment is conducted based on the received remote rehabilitation data from the patient to determine the patient's current rehabilitation progress; wherein, the remote rehabilitation data from the patient includes exercise verification videos, medical imaging data, medical test data, and rehabilitation progress description information; Based on the deviation between the patient's current recovery progress and the estimated recovery progress, the patient's recovery plan is updated in real time in conjunction with cold-region environmental data.

[0046] 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.

[0047] For intelligent surgical planning systems, 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 cold-region-specific 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] In some embodiments, when optimizing the initial preoperative planning scheme for knee replacement based on cold-region-specific data associated with total knee replacement, osteoporotic areas can be marked based on bone quality data of the femur and tibia (e.g., areas with HU < 150), and the osteotomy angle, prosthesis type and size can be adjusted according to the distribution of osteoporotic areas to obtain an optimized scheme.

[0049] In some embodiments, when performing a simulated surgery on the knee joint after the optimized preoperative planning scheme, three surgical schemes can be simulated simultaneously. The first surgical scheme is the initial preoperative planning scheme for knee replacement without optimization; the second surgical scheme is the planning scheme optimized by the above parameters (osteotomy angle, prosthesis type and size, etc.); and the third surgical scheme is the reference surgical scheme obtained by surgical planning after osteoporosis fixation using bone cement or other means. By simulating and comparing the above three surgical schemes, the target preoperative planning scheme most suitable for the patient can be obtained.

[0050] In the process of motion simulation, finite element analysis can be used to assess the stress on the prosthesis and the holding force of the screw in the osteoporotic area to evaluate the impact of osteoporosis on the placement of the prosthesis, and to adjust and optimize the parameters accordingly when optimizing the surgical plan in the future.

[0051] In this way, by generating the above three surgical planning schemes and verifying and comparing them through motion simulation, we can improve the final surgical outcome for patients by adjusting parameters for surgical planning schemes that do not consider the impact of osteoporosis, those that do consider the impact of osteoporosis, and those that are suitable for the actual bone condition of the patient.

[0052] In some embodiments, the intelligent surgical planning system is specifically used for: A three-dimensional model of the hip joint is obtained by performing three-dimensional reconstruction based on the acquired medical images of the hip joint. Preoperative planning is performed based on a three-dimensional model of the hip joint to obtain an initial preoperative planning scheme for hip replacement. Based on cold-region-specific data associated with total hip arthroplasty, the initial preoperative planning scheme for hip 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 hip joint was conducted to determine whether various hip joint motion simulations could achieve the corresponding normal joint range of motion. If at least one hip 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.

[0053] In some embodiments, when optimizing the initial preoperative planning scheme for knee replacement based on cold-region-specific data associated with total hip replacement, osteoporotic areas can be marked based on bone quality data of the femur and acetabulum (e.g., areas with HU < 150), and the press-fit area, prosthesis type and size can be adjusted according to the distribution of osteoporotic areas to obtain an optimized scheme.

[0054] In some embodiments, when performing postoperative motion simulation of the hip joint after a simulated surgery based on an optimized preoperative planning scheme, three surgical schemes can be simulated simultaneously. The first surgical scheme is the initial preoperative planning scheme for hip replacement without optimization; the second surgical scheme is the planning scheme optimized by the above parameters (press fit area, prosthesis type and size, etc.); and the third surgical scheme is the reference surgical scheme obtained by surgical planning after osteoporosis fixation using bone cement or other means. By performing motion simulation and comparing the above three surgical schemes, the target preoperative planning scheme most suitable for the patient can be obtained.

[0055] In the process of motion simulation, finite element analysis can be used to assess the stress on the prosthesis and the holding force of the screw in the osteoporotic area to evaluate the impact of osteoporosis on the placement of the prosthesis, and to adjust and optimize the parameters accordingly when optimizing the surgical plan in the future.

[0056] In this way, by generating the above three surgical planning schemes and verifying and comparing them through motion simulation, we can make a horizontal comparison of the three surgical planning approaches: not considering the impact of osteoporosis, adjusting parameters to consider the impact of osteoporosis, and fixing osteoporosis before surgical planning. This will result in a surgical planning scheme that is more suitable for the patient's actual bone condition, thereby improving the patient's final surgical outcome.

[0057] 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 was conducted based on a three-dimensional model of the knee joint to obtain an initial preoperative planning scheme for unicompartmental arthroplasty. Based on cold-region-specific data associated with unicompartmental arthroplasty, the initial preoperative planning scheme for unicompartmental arthroplasty was optimized to obtain the optimized preoperative planning scheme. After performing a simulated surgery based on the optimized preoperative planning, postoperative motion simulation of the knee joint was conducted to determine whether various motion simulations could achieve the corresponding normal joint range of motion. If at least one of the motion simulations 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 satisfies the motion simulation requirements is obtained.

[0058] In some embodiments, when optimizing the initial preoperative planning scheme for unicompartmental arthroplasty based on cold-region-specific data associated with unicompartmental arthroplasty, osteoporotic areas can be marked based on tibial bone quality data (e.g., areas with HU < 150), and the osteotomy angle, prosthesis type, and size can be adjusted according to the distribution of osteoporotic areas to obtain an optimized scheme.

[0059] In some embodiments, after a simulated surgery based on an optimized preoperative planning scheme, when performing postoperative motion simulation on the knee joint, three surgical schemes can be simulated simultaneously. The first surgical scheme is the initial preoperative planning scheme for unicompartmental arthroplasty without optimization; the second surgical scheme is the planning scheme optimized by the above parameters (osteotomy angle, prosthesis type and size, etc.); and the third surgical scheme is the reference surgical scheme obtained by surgical planning after osteoporosis fixation using bone cement or other means. By performing motion simulation and comparing the above three surgical schemes, the target preoperative planning scheme most suitable for the patient can be obtained.

[0060] In the process of motion simulation, finite element analysis can be used to assess the stress on the prosthesis and the holding force of the screw in the osteoporotic area to evaluate the impact of osteoporosis on the placement of the prosthesis, and to adjust and optimize the parameters accordingly when optimizing the surgical plan in the future.

[0061] In this way, by generating the above three surgical planning schemes and verifying and comparing them through motion simulation, we can make a horizontal comparison of the three surgical planning approaches: not considering the impact of osteoporosis, adjusting parameters to consider the impact of osteoporosis, and fixing osteoporosis before surgical planning. This will result in a surgical planning scheme that is more suitable for the patient's actual bone condition, thereby improving the patient's final surgical outcome.

[0062] In some embodiments, the intelligent surgical planning system is specifically used for: A three-dimensional model of the hip joint is obtained by performing three-dimensional reconstruction based on the acquired medical images of the hip joint. Preoperative planning was performed based on the three-dimensional model of the hip joint to obtain an initial preoperative planning scheme for periacetabular osteotomy. Based on cold-region-specific data associated with periacetabular osteotomy, the initial preoperative planning scheme for knee replacement 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 hip joint was conducted to determine whether various motion simulations could achieve the corresponding normal joint range of motion. If at least one of the motion simulations 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 satisfies the motion simulation requirements is obtained.

[0063] In some embodiments, when optimizing the initial preoperative planning scheme for periacetabular osteotomy based on cold-region-specific data associated with periacetabular osteotomy, osteoporotic areas can be marked based on hip joint bone data (e.g., areas with HU < 150), and the osteotomy angle, correction angle, and internal fixation screw trajectory can be adjusted according to the distribution of osteoporotic areas to obtain an optimized scheme.

[0064] In some embodiments, when simulating postoperative movement of the hip joint after a simulated surgery based on an optimized preoperative planning scheme, three surgical schemes can be simulated simultaneously. The first surgical scheme is the initial preoperative planning scheme of periacetabular osteotomy without optimization; the second surgical scheme is the planning scheme optimized by the above parameters (osteotomy angle, correction angle, internal fixation screw trajectory); and the third surgical scheme is the reference surgical scheme obtained by surgical planning after osteoporosis fixation using bone cement or other means. By simulating and comparing the above three surgical schemes, the target preoperative planning scheme most suitable for the patient can be obtained.

[0065] In the process of motion simulation, finite element analysis can be used to assess the screw holding force in osteoporotic areas to evaluate the impact of osteoporosis on the trajectory of internal fixation screws after correction, and parameters can be adjusted and optimized accordingly when optimizing the surgical plan.

[0066] In this way, by generating the above three surgical planning schemes and verifying and comparing them through motion simulation, we can make a horizontal comparison of the three surgical planning approaches: not considering the impact of osteoporosis, adjusting parameters to consider the impact of osteoporosis, and fixing osteoporosis before surgical planning. This will result in a surgical planning scheme that is more suitable for the patient's actual bone condition, thereby improving the patient's final surgical outcome.

[0067] In some embodiments, the intelligent surgical planning system is specifically used for: Three-dimensional reconstruction is performed on the acquired medical images of the joint to obtain a three-dimensional model of the joint; Preoperative planning is carried out based on the three-dimensional model of the joint to obtain the initial preoperative planning scheme for sports medicine. Based on cold-region-specific data associated with sports medicine, the initial preoperative planning scheme for sports medicine was optimized to obtain an optimized preoperative planning scheme. After performing a simulated surgery based on the optimized preoperative planning scheme, postoperative motion simulation of the joint was conducted to determine whether various motion simulations could achieve the corresponding normal joint range of motion. If at least one of the motion simulations 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 satisfies the motion simulation requirements is obtained.

[0068] In some embodiments, when optimizing the preoperative planning scheme for sports medicine based on cold-region-specific data associated with sports medicine, osteoporotic areas can be marked based on bone quality data of the femur and tibia (e.g., areas with HU < 150), and the location, angle, and diameter of the femoral / tibial tunnel can be adjusted according to the distribution of osteoporotic areas to obtain the optimized scheme.

[0069] In some embodiments, when performing a simulated surgery based on an optimized preoperative planning scheme and then simulating postoperative joint movement, three surgical plans can be simulated simultaneously. The first surgical plan is the initial preoperative planning scheme in sports medicine that has not been optimized; the second surgical plan is the planning scheme optimized by the above parameters (femoral / tibial tunnel position, angle, and diameter); and the third surgical plan is the reference surgical plan obtained by surgical planning after osteoporosis fixation using bone cement or other means. By simulating and comparing the above three surgical plans, the target preoperative planning scheme most suitable for the patient can be obtained.

[0070] In the process of motion simulation, finite element analysis can be used to assess the biomechanics (such as graft tension at different bending angles) in osteoporotic areas to evaluate the impact of osteoporosis on graft tension, and parameters can be adjusted and optimized accordingly when optimizing surgical plans in the future.

[0071] In this way, by generating the above three surgical planning schemes and verifying and comparing them through motion simulation, we can make a horizontal comparison of the three surgical planning approaches: not considering the impact of osteoporosis, adjusting parameters to consider the impact of osteoporosis, and fixing osteoporosis before surgical planning. This will result in a surgical planning scheme that is more suitable for the patient's actual bone condition, thereby improving the patient's final surgical outcome.

[0072] In some embodiments, the intelligent surgical planning system is specifically used for: A three-dimensional model of the spine is obtained by performing three-dimensional reconstruction based on the acquired medical images of the spine. Preoperative planning is performed based on a three-dimensional model of the spine to obtain an initial preoperative planning scheme for the spine. Based on cold-region-specific data associated with the spine, the initial preoperative planning scheme for the spine 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 spine was conducted to determine whether various motion simulations could achieve the corresponding normal joint range of motion. If at least one of the motion simulations 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 satisfies the motion simulation requirements is obtained.

[0073] In some embodiments, when optimizing the initial preoperative planning scheme for the spine based on cold-region-specific data associated with the spine, osteoporotic areas can be marked based on the bone quality data of the spine (e.g., areas with HU < 150), and the screw trajectory, bone graft volume, and nerve root protection area can be adjusted according to the distribution of osteoporotic areas to obtain an optimized scheme.

[0074] In some embodiments, when performing postoperative motion simulation of the spine after a simulated surgery based on an optimized preoperative planning scheme, three surgical schemes can be simulated simultaneously. The first surgical scheme is the initial preoperative planning scheme of the spine without optimization; the second surgical scheme is the planning scheme optimized by the above parameters (screw trajectory, bone graft volume, nerve root protection area); and the third surgical scheme is the reference surgical scheme obtained by surgical planning after osteoporosis fixation using bone cement or other means. By performing motion simulation and comparing the above three surgical schemes, the target preoperative planning scheme most suitable for the patient can be obtained.

[0075] In the process of motion simulation, finite element analysis can be used to assess screw holding force and nerve root pressure in osteoporotic areas to evaluate the impact of osteoporosis on screw trajectory and nerve root pressure, and to adjust and optimize parameters accordingly when optimizing surgical plans in the future.

[0076] In this way, by generating the above three surgical planning schemes and verifying and comparing them through motion simulation, we can make a horizontal comparison of the three surgical planning approaches: not considering the impact of osteoporosis, adjusting parameters to consider the impact of osteoporosis, and fixing osteoporosis before surgical planning. This will result in a surgical planning scheme that is more suitable for the patient's actual bone condition, thereby improving the patient's final surgical outcome.

[0077] In some embodiments, the intelligent surgical planning system is specifically used for: Based on the registration of at least two identical medical images of the acquired trauma site, a registered medical image of the trauma site is obtained. An intraoperative planning interface containing each registered medical image of the trauma site is presented. The intraoperative planning interface includes an osteoporosis risk area generated based on bone data of the trauma site. In response to a screw placement planning operation for any medical image, the demonstration effect of the screw placement planning operation in each medical image is presented collaboratively in the intraoperative planning interface to determine a trauma surgery planning scheme including screw placement planning.

[0078] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown.

[0079] The electronic device may include a processor 501 and a memory 502 storing computer program instructions.

[0080] 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.

[0081] 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 suitable, memory 502 may include removable or non-removable (or fixed) media. Where suitable, 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.

[0082] 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.

[0083] The processor 501 reads and executes computer program instructions stored in the memory 502 to implement the functions of the intelligent robot system for digital diagnosis and treatment of orthopedics in cold regions as described in any of the above embodiments.

[0084] 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.

[0085] The communication interface 503 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0086] 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-X) 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.

[0087] 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 robot system for digital diagnosis and treatment of orthopedic diseases in cold regions as described in any of the above embodiments.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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 diagnostic and treatment robot system for orthopedics in cold regions, characterized in that, include: The intraoperative interaction response module is used to respond to intraoperative interaction needs and perform equipment control and voice interaction based on these needs. The intraoperative real-time prompts and operation suggestions module is used to provide real-time prompts during the operation based on the planned surgical procedure; Furthermore, it generates surgical operation suggestions in real time during the operation and updates the surgical plan in real time based on the response results of the surgical operation suggestions to obtain the updated real-time surgical plan. The intraoperative intelligent control module is used to control the surgical robot to assist in the execution of surgical operations based on the planned surgical plan and intraoperative sensing data; and to update the control mode of the surgical robot in real time.

2. The intelligent robot system for digital diagnosis and treatment of orthopedics in cold regions according to claim 1, characterized in that, The intraoperative intelligent control module is specifically used for: Based on intraoperative perception data, the planned surgical procedure, and a pre-trained surgical robot decision-making and execution model, the system generates real-time surgical robot operation decisions and control commands; among which, 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 the planned surgical plan. The robot execution branch is used to generate surgical robot control commands based on the surgical robot operation decisions.

3. The intelligent robot system for digital diagnosis and treatment of orthopedics in cold regions according to claim 1 or 2, characterized in that, The intraoperative intelligent control module is specifically used for: Based on the control modes and intraoperative perception data corresponding to each stage of the surgery, 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.

4. The intelligent robot system for digital diagnosis and treatment of orthopedics in cold regions according to claim 1 or 2, characterized in that, The intraoperative intelligent control module 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.

5. The intelligent robot system for digital diagnosis and treatment of orthopedics in cold regions according to claim 1 or 2, characterized in that, The intraoperative real-time prompts and operation suggestions module 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.

6. The intelligent robot system for digital diagnosis and treatment of orthopedics in cold regions according to claim 1, characterized in that, The intraoperative real-time prompts and operation suggestions module is specifically used for: The intraoperative perception data and the planned surgical plan 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.

7. The intelligent robot 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 auxiliary assessment system, an intelligent surgical planning system, a remote system, and a rehabilitation system; among them, The intelligent auxiliary assessment system is used to conduct auxiliary assessments of all orthopedic diseases in cold regions based on patient medical data. The intelligent surgical planning system is used to plan hip and knee joint surgeries, spinal surgeries, sports medicine surgeries, and trauma surgeries based on patient medical data. 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 plans, obtaining postoperative assessment results; and to generate personalized rehabilitation plans for patients based on postoperative assessment results, patient attribute information, and medical records. These personalized rehabilitation plans include in-hospital and out-of-hospital rehabilitation plans, with the out-of-hospital plan including cold-climate-specific rehabilitation recommendations. 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, it conducts rehabilitation assessments based on the patient's out-of-hospital rehabilitation assessment data to obtain rehabilitation assessment results, which are then used to update the out-of-hospital rehabilitation plan.

8. The intelligent robot system for digital diagnosis and treatment of orthopedics in cold regions according to claim 1 or 7, characterized in that, The intelligent assisted evaluation system 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.

9. The intelligent robot system for digital diagnosis and treatment of orthopedics in cold regions according to claim 1 or 7, characterized in that, The remote system is specifically used for: A rehabilitation assessment is conducted based on the received remote rehabilitation data from the patient to determine the patient's current rehabilitation progress; wherein, the remote rehabilitation data from the patient includes exercise verification videos, medical imaging data, medical test data, and rehabilitation progress description information; Based on the deviation between the patient's current recovery progress and the estimated recovery progress, the patient's recovery plan is updated in real time in conjunction with data on the cold environment.

10. The intelligent robot system for digital diagnosis and treatment of orthopedics in cold regions according to claim 1 or 7, 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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