Cold region orthopedics intelligent diagnosis and treatment rehabilitation system

Abstract

The application provides a cold region orthopedic intelligent diagnosis and treatment rehabilitation system, comprising: a postoperative evaluation module, configured to perform postoperative evaluation according to postoperative data and preoperative planning scheme to obtain postoperative evaluation results; a personalized rehabilitation scheme generation module, configured to generate a personalized rehabilitation scheme of a patient according to the postoperative evaluation results, patient attribute information and medical data; wherein the personalized rehabilitation scheme comprises an in-hospital rehabilitation scheme and an out-of-hospital rehabilitation scheme, and the out-of-hospital rehabilitation scheme comprises a cold region specific rehabilitation suggestion; a discharge evaluation module, configured to simulate joint activity indicators and pain indicators of the patient under cold region environmental conditions according to in-hospital rehabilitation evaluation data of the patient, so as to evaluate whether the patient meets the discharge standard under the cold region environment according to the simulation results; and a rehabilitation evaluation module, configured to perform rehabilitation evaluation according to out-of-hospital rehabilitation evaluation data of the patient to obtain rehabilitation evaluation results, so as to update the out-of-hospital rehabilitation scheme according to the rehabilitation evaluation results.

Description

Technical Field

[0001] This application belongs to the field of digital orthopedic diagnosis and treatment, and in particular relates to a digital orthopedic diagnosis and rehabilitation system for cold regions. Background Technology

[0002] Traditional orthopedic surgery rehabilitation management is usually completed by resident physicians and nurses. Patients often need to communicate with different medical staff on matters such as postoperative effect assessment, discharge assessment, and rehabilitation plan development. These matters usually require medical staff to provide corresponding solutions based on their own experience. The same patient may receive different solutions from different medical staff, which makes it difficult to meet the rehabilitation management needs of different patients. Summary of the Invention

[0003] This application provides a digitalized diagnosis and rehabilitation system for orthopedics in cold regions, which can realize postoperative assessment, personalized rehabilitation plan generation, discharge assessment, and rehabilitation assessment, achieving full-process rehabilitation management and meeting the rehabilitation management needs of patients.

[0004] In a first aspect, embodiments of this application provide a digital intelligent diagnosis and rehabilitation system for orthopedics in cold regions, comprising: The postoperative assessment module is used to conduct postoperative assessments based on the patient's postoperative data and preoperative planning, and to obtain postoperative assessment results. The personalized rehabilitation plan generation module is used to generate a personalized rehabilitation plan for the patient based on the postoperative assessment results, patient attribute information, and medical data. The personalized rehabilitation plan includes an in-hospital rehabilitation plan and an out-of-hospital rehabilitation plan, and the out-of-hospital rehabilitation plan includes cold-region-specific rehabilitation suggestions. The discharge assessment module is used to simulate the patient's joint activity and pain indicators under cold environment conditions based on the patient's in-hospital rehabilitation assessment data, so as to assess whether the patient meets the discharge criteria under cold environment conditions based on the simulation results. The rehabilitation assessment module is used to conduct rehabilitation assessments based on the patient's outpatient rehabilitation assessment data, obtain rehabilitation assessment results, and update the outpatient rehabilitation plan based on the rehabilitation assessment results.

[0005] Optionally, the postoperative assessment module is specifically used for: By comparing the patient's postoperative medical imaging data with the preoperative planning scheme, an assessment of the expected surgical completion rate was obtained; and... Postoperative evaluation is conducted based on the patient's postoperative clinical indicators to obtain the surgical outcome assessment results.

[0006] Optionally, the personalized rehabilitation plan generation module is specifically used for: Based on postoperative assessment results, patient attribute information, and medical records, an in-hospital rehabilitation plan tailored to the patient is generated; among which, The in-hospital rehabilitation program includes in-hospital nursing care, physical therapy, nutrition, exercise, and discharge criteria.

[0007] Optionally, the personalized rehabilitation plan generation module is specifically used for: Based on postoperative assessment results, patient attribute information, and medical records, a matching outpatient rehabilitation plan is generated; among which, The outpatient rehabilitation program includes outpatient nursing care, physical therapy, nutrition, exercise, and cold-weather-specific rehabilitation recommendations.

[0008] Optionally, the discharge assessment module 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.

[0009] Optionally, the rehabilitation assessment module is further used for: The rehabilitation assessment results are obtained based on medical images taken after rehabilitation training, exercise verification videos, and multimodal rehabilitation assessment models.

[0010] 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, an intelligent robot system, and a remote 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 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 end; 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 collaboration for pre-hospital emergency care.

[0011] Optionally, the intelligent assisted evaluation system is specifically used for: Patient medical data and cold-region-specific bone and joint data are input into a pre-trained intelligent analysis model for osteoarthritis to obtain an intelligent analysis report of osteoarthritis output by the intelligent analysis model for sports injuries. 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.

[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 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.

[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] Secondly, embodiments of this application provide a rehabilitation method for digital intelligent diagnosis and treatment of orthopedics in cold regions, the rehabilitation method being used to realize the functions of the digital intelligent diagnosis and treatment rehabilitation system for orthopedics in cold regions as described in any embodiment of the first aspect.

[0015] 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 digital diagnosis and rehabilitation system for orthopedics in cold regions.

[0016] 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 diagnosis and rehabilitation system for orthopedics in cold regions.

[0017] The digital diagnosis and rehabilitation system, method, equipment, and computer-readable storage medium for orthopedics in cold regions according to the embodiments of this application include a postoperative assessment module, a personalized rehabilitation plan generation module, a discharge assessment module, and a rehabilitation assessment module, realizing full-process rehabilitation management and meeting the rehabilitation management needs of patients. Attached Figure Description

[0018] 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.

[0019] Figure 1 This is a schematic diagram of the architecture of a digital diagnosis and rehabilitation system for orthopedics in cold regions provided in one embodiment of this application; Figure 2 This is an architecture diagram of the intelligent diagnosis and rehabilitation system for orthopedics in cold regions, provided in one embodiment of this application, which manages the entire rehabilitation process based on a digital model library of bones for people in cold regions. Figure 3 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 4 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0020] 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.

[0021] 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.

[0022] To address the problems of existing technologies, this application provides a digital intelligent diagnosis and rehabilitation system for orthopedics in cold regions. The following is a description of the digital intelligent diagnosis and rehabilitation system for orthopedics in cold regions provided by this application. Figure 1 This is a schematic diagram of the architecture of a digital intelligent diagnosis and rehabilitation system for orthopedics in cold regions, provided in one embodiment of this application. The system includes a postoperative assessment module, a personalized rehabilitation plan generation module, a discharge assessment module, and a rehabilitation assessment module. The postoperative assessment module is used to conduct postoperative assessments based on the patient's postoperative data and preoperative planning, and to obtain postoperative assessment results. The personalized rehabilitation plan generation module is used to generate a personalized rehabilitation plan for the patient based on the postoperative assessment results, patient attribute information, and medical data. The personalized rehabilitation plan includes an in-hospital rehabilitation plan and an out-of-hospital rehabilitation plan, and the out-of-hospital rehabilitation plan includes cold-region-specific rehabilitation suggestions. The discharge assessment module is used to simulate the patient's joint activity and pain indicators under cold environment conditions based on the patient's in-hospital rehabilitation assessment data, so as to assess whether the patient meets the discharge criteria under cold environment conditions based on the simulation results. The rehabilitation assessment module is used to conduct rehabilitation assessments based on the patient's outpatient rehabilitation assessment data, obtain rehabilitation assessment results, and update the outpatient rehabilitation plan based on the rehabilitation assessment results.

[0023] 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.

[0024] Figure 2 This is an architecture diagram of the intelligent diagnosis and rehabilitation system for orthopedics in cold regions, provided in one embodiment of this application, which manages the entire rehabilitation process based on a digital model library of bones for people in cold regions.

[0025] 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.

[0026] 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.

[0027] In some embodiments, the postoperative assessment module is specifically used for: The expected completion rate of the surgery is assessed by comparing the patient's postoperative medical imaging data with the preoperative planning scheme; and the surgical outcome is assessed by evaluating the patient's postoperative clinical indicators.

[0028] In the postoperative evaluation based on the patient's postoperative clinical indicators, similar cases in the cold region orthopedic diagnosis and treatment and postoperative recovery cases in the cold region population bone digital model library can be used to obtain the surgical effect evaluation results.

[0029] Specifically, matching can be performed based on patient attribute information and medical data to identify target cases that match the patient, and the surgical effect can be evaluated according to the postoperative clinical indicators included in the target cases.

[0030] In some embodiments, the personalized rehabilitation plan generation module is specifically used for: Based on postoperative assessment results, patient attribute information, and medical records, an in-hospital rehabilitation plan tailored to the patient is generated; among which, The in-hospital rehabilitation program includes in-hospital nursing care, physical therapy, nutrition, exercise, and discharge criteria.

[0031] In some embodiments, the personalized rehabilitation plan generation module is specifically used for: Based on postoperative assessment results, patient attribute information, and medical records, a matching outpatient rehabilitation plan is generated; among which, The outpatient rehabilitation program includes outpatient nursing care, physical therapy, nutrition, exercise, and cold-weather-specific rehabilitation recommendations.

[0032] In some embodiments, postoperative assessment results, patient attribute information, and medical data can be input into a pre-trained personalized rehabilitation plan generation model to obtain in-hospital rehabilitation plans and out-of-hospital rehabilitation plans output by the personalized rehabilitation plan generation model. The personalized rehabilitation plan generation model includes an attribute feature extraction module, a medical visit feature extraction module, a feature fusion module, an in-hospital rehabilitation plan output branch, and an out-of-hospital rehabilitation plan output branch.

[0033] In some embodiments, the attribute feature extraction module included in the personalized rehabilitation plan generation model can be composed of two MLP layers, with the hidden layer sizes set to 64 and 32 respectively, the L2 weight decay of the output stage being 0.001 to improve generalization, the activation function being set to the ReLU function, the training batch being set to 32 to balance memory and efficiency, and the output being a 32-dimensional feature vector.

[0034] In some embodiments, the personalized rehabilitation plan generation model includes a patient visit feature extraction module, which may consist of a text encoder, an image encoder, a laboratory data MLP, a medication record MLP, and a fusion submodule. The input to the text encoder can be a text-based assessment / surgical record (and postoperative assessment results), and the text encoder can be the encoder in a pre-trained medical BERT model, with an output dimension of 768. The input to the image encoder can be a medical image, and the image encoder type can be a pre-trained ResNet network with fully connected layers removed (e.g., ResNet-50, ResNet-10). The output dimension of the image encoder is set to 2048 (e.g., 1). The test data MLP can be composed of two MLP layers with hidden layer sizes of 64 and 32 respectively, and the activation function is set to LeakyReLU, outputting a 32-dimensional feature vector. The medication record MLP can also be composed of two MLP layers with hidden layer sizes of 64 and 32 respectively, and the activation function is set to LeakyReLU, outputting a 32-dimensional feature vector. The fusion submodule is used to concatenate the outputs of the text encoder, image encoder, test data MLP, and medication record MLP, and perform dimensionality reduction through the MLP fusion layer, outputting a 128-dimensional feature vector.

[0035] In some embodiments, the feature fusion module included in the personalized rehabilitation plan generation model takes as input a 32-dimensional feature vector output by the attribute feature extraction module and a 128-dimensional feature vector output by the medical visit feature extraction module, and then passes them through a concatenation process, a fully connected layer, and a Swish activation layer to obtain the output 128-dimensional fused feature vector.

[0036] Furthermore, the output branch of the in-hospital rehabilitation program includes multiple output modules, each of which is used to output in-hospital nursing plans, physiotherapy plans, nutrition plans, in-hospital exercise plans, and discharge criteria; the output branch of the out-of-hospital rehabilitation program includes multiple output modules, each of which is used to output out-of-hospital nursing plans, physiotherapy plans, nutrition plans, out-of-hospital exercise plans, and cold-region-specific rehabilitation suggestions.

[0037] Among them, the physiotherapy plan and nutrition plan can share the same output module, and the nursing plan and exercise plan can add some plan content based on cold-region-specific data on the same output module (the in-hospital rehabilitation plan branch adds the relevant in-hospital content, and the out-of-hospital rehabilitation plan branch adds the relevant out-of-hospital content) to achieve the reuse of the corresponding output modules (nursing plan and exercise plan).

[0038] In some embodiments, the personalized rehabilitation plan generation model includes a nutrition plan output module, which takes a fused feature vector as input and outputs a nutrition plan containing dietary recommendations. The model architecture of the nutrition plan output module includes an MLP classifier, and the number of output categories can be set to 20, and the number of hidden layers can be set to 64.

[0039] In some embodiments, when training a personalized rehabilitation plan generation model, the loss functions corresponding to each output module can be weighted to obtain a weighted total loss, and training can be performed based on the weighted total loss. According to clinical importance, the weight ratios corresponding to the output branches of in-hospital rehabilitation plans and out-of-hospital rehabilitation plans can be set to 3:2.

[0040] In some embodiments, the discharge assessment module 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.

[0041] In the process of conducting home rehabilitation assessments based on patients' home temperature data and in-hospital rehabilitation assessment data, similar cases from the cold-region orthopedic diagnosis and treatment and postoperative recovery cases in the cold-region population skeletal digital model database can be used to conduct postoperative assessments in order to obtain home rehabilitation assessment results.

[0042] Specifically, the patient's home temperature data and in-hospital rehabilitation assessment data can be matched to identify target cases that match the patient, and home rehabilitation assessments can be conducted according to the home rehabilitation recovery status included in the target cases.

[0043] In some embodiments, the rehabilitation assessment module is further configured to: The rehabilitation assessment results are obtained based on medical images taken after rehabilitation training, exercise verification videos, and multimodal rehabilitation assessment models.

[0044] The multimodal rehabilitation assessment model includes an adapter module, a text encoder, a feature alignment and fusion module, and a task decoding module. The adapter module dynamically generates modality-specific parameters based on the modality of the input medical image, enabling the image encoder to process the corresponding modality of the medical image. The feature alignment and fusion module constructs a joint embedding space to align text features and image features at the semantic level, and then fuses the aligned text features and image features. The text encoder extracts features from the image labels corresponding to the input medical image. These image labels can be labels obtained after processing the medical image, such as subjective conclusions drawn by doctors after reviewing the medical image.

[0045] Figure 3 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 auxiliary assessment system, an intelligent surgical planning system, an intelligent robot system, and a remote 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 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 end; 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 collaboration for pre-hospital emergency care.

[0047] In some embodiments, the intelligent assisted assessment system is specifically used for: Patient medical data and cold-region-specific bone and joint data are input into a pre-trained intelligent analysis model for osteoarthritis to obtain an intelligent analysis report of osteoarthritis output by the intelligent analysis model for sports injuries. 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.

[0048] 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.

[0049] The intelligent robot system is used to control surgical robots, such as embodied intelligent robots, used to perform surgery.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.).

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] Figure 4 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 401 and a memory 402 storing computer program instructions.

[0068] Specifically, the processor 401 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 402 may include mass storage for data or instructions. For example, and not limitingly, memory 402 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 402 may include removable or non-removable (or fixed) media. Where appropriate, memory 402 may be internal or external to an electronic device. In a particular embodiment, memory 402 may be a non-volatile solid-state memory.

[0070] In one embodiment, memory 402 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 401 reads and executes computer program instructions stored in the memory 402 to implement the functions of the digital diagnosis and rehabilitation system for orthopedics in cold regions described in any of the above embodiments.

[0072] In one example, the electronic device may also include a communication interface 403 and a bus 410. For example, Figure 4 As shown, the processor 401, memory 402, and communication interface 403 are connected through bus 410 and complete communication with each other.

[0073] The communication interface 403 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0074] Bus 410 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 410 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 may 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 digitalized diagnosis and rehabilitation system for orthopedic diseases in cold regions 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 rehabilitation system for orthopedics in cold regions, characterized in that, include: The postoperative assessment module is used to conduct postoperative assessments based on the patient's postoperative data and preoperative planning, and to obtain postoperative assessment results. The personalized rehabilitation plan generation module is used to generate a personalized rehabilitation plan for the patient based on the postoperative assessment results, patient attribute information, and medical data. The personalized rehabilitation plan includes an in-hospital rehabilitation plan and an out-of-hospital rehabilitation plan, and the out-of-hospital rehabilitation plan includes cold-region-specific rehabilitation suggestions. The discharge assessment module is used to simulate the patient's joint activity and pain indicators under cold environment conditions based on the patient's in-hospital rehabilitation assessment data, so as to assess whether the patient meets the discharge criteria under cold environment conditions based on the simulation results. The rehabilitation assessment module is used to conduct rehabilitation assessments based on the patient's outpatient rehabilitation assessment data, obtain rehabilitation assessment results, and update the outpatient rehabilitation plan based on the rehabilitation assessment results.

2. The digital intelligent diagnosis and rehabilitation system for orthopedics in cold regions according to claim 1, characterized in that, The postoperative assessment module is specifically used for: By comparing the patient's postoperative medical imaging data with the preoperative planning scheme, an assessment of the expected surgical completion rate was obtained; and... Postoperative evaluation is conducted based on the patient's postoperative clinical indicators to obtain the surgical outcome assessment results.

3. The digital intelligent diagnosis and rehabilitation system for orthopedics in cold regions according to claim 1 or 2, characterized in that, The personalized rehabilitation plan generation module is specifically used for: Based on postoperative assessment results, patient attribute information, and medical records, an in-hospital rehabilitation plan tailored to the patient is generated; among which, The in-hospital rehabilitation program includes in-hospital nursing care, physical therapy, nutrition, exercise, and discharge criteria.

4. The digital intelligent diagnosis and rehabilitation system for orthopedics in cold regions according to claim 3, characterized in that, The personalized rehabilitation plan generation module is specifically used for: Based on postoperative assessment results, patient attribute information, and medical records, a matching outpatient rehabilitation plan is generated; among which, The outpatient rehabilitation program includes outpatient nursing care, physical therapy, nutrition, exercise, and cold-weather-specific rehabilitation recommendations.

5. The digital intelligent diagnosis and rehabilitation system for orthopedics in cold regions according to claim 1, characterized in that, The discharge assessment module 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.

6. The digital intelligent diagnosis and rehabilitation system for orthopedics in cold regions according to claim 5, characterized in that, The rehabilitation assessment module is also used for: The rehabilitation assessment results are obtained based on medical images taken after rehabilitation training, exercise verification videos, and multimodal rehabilitation assessment models.

7. The digital intelligent diagnosis and rehabilitation system for 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, an intelligent robot system, and a remote 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 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 end; 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 collaboration for pre-hospital emergency care.

8. The digital intelligent diagnosis and rehabilitation system for 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-specific bone and joint data are input into a pre-trained intelligent analysis model for osteoarthritis to obtain an intelligent analysis report of osteoarthritis output by the intelligent analysis model for sports injuries. 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.

9. The digital intelligent diagnosis and rehabilitation system for orthopedics in cold regions according to claim 1, 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 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.

10. The digital intelligent diagnosis and rehabilitation system for orthopedics in cold regions according to claim 1 or 9, 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.

Images