Innovative cardiology department clinical remote consultation system

Abstract

The invention discloses an innovative cardiology department clinical remote consultation system. The system comprises the following steps: step 1, demand analysis and system design; 2, selecting equipment and technology; 3, designing a system architecture; 4, developing an edge calculation module; 5, AI intelligent analysis integration is carried out; 6, testing and optimizing the system; and step 7, system deployment. By introducing an edge computing technology, real-time data processing can be realized, and the delay of data transmission is reduced. Therefore, the electrocardiogram data can be analyzed in real time, the response speed to emergency situations such as arrhythmia is greatly improved, and the patient can be ensured to obtain timely medical intervention at a critical moment; the AI intelligent analysis technology can effectively integrate data from different devices, and the analysis accuracy of the electrocardiogram and other physiological parameters is improved. In this way, omissions possibly occurring in complicated data by clinicians are reduced, more reliable decision support is provided for the clinicians, and therefore the overall medical quality is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cardiology consultations, and specifically provides an innovative clinical remote consultation system for cardiology. Background Art

[0002] The background of clinical remote consultation technology in cardiology involves multiple aspects. First of all, cardiovascular diseases are the main cause of death globally. With aging and lifestyle changes, the incidence rate continues to rise, and there is an urgent need to improve the accessibility and efficiency of medical services. Traditional consultations usually require patients to go to the hospital in person, which may cause delays in patients in geographically remote or mobility-impaired areas, and the uneven distribution of expert resources limits patients' access to the best medical care. With the progress of information technology, telemedicine has gradually emerged, using methods such as video calls, emails, and mobile applications for consultations, improving the accessibility of medical services and reducing patients' medical costs and time. The advantages of cardiology remote consultations are to improve efficiency, quickly obtain expert opinions, optimize the utilization of medical resources, and enhance accuracy through the sharing of real-time data such as electronic health records and electrocardiograms. At the same time, modern remote consultation systems integrate advanced technologies such as video conferencing platforms, data transmission security protocols, and AI-assisted diagnosis tools to ensure their feasibility and reliability. With the popularization of telemedicine, legal and ethical issues have gradually emerged. For example, the protection of patient privacy and the legality of cross-regional practice need to be emphasized. In the future, cardiology remote consultations will combine technologies such as artificial intelligence and blockchain to improve security and efficiency. With the increasing public acceptance and the improvement of policies and infrastructure, its development prospects are broad. Such a background provides a good foundation and opportunity for the application of related patents and technology research and development.

[0003] However, there are still significant deficiencies in the existing technologies, such as:

[0004] In the existing remote consultation system, electrocardiogram data is transmitted from the patient-side device to the hospital's server, then processed, and finally returned to the doctor and the patient. This process may cause a long delay. Such a delay may particularly affect the timely diagnosis and treatment of diseases such as arrhythmia in case of emergency, thus affecting the patient's life safety. Traditional remote consultation systems often face the problem of data islands, that is, data from different devices cannot be effectively integrated, resulting in incomplete information and inaccurate analysis. Summary of the Invention

[0005] The purpose of the present invention is to provide an innovative clinical remote consultation system for cardiology to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] An innovative clinical remote consultation system for cardiology includes the following steps:

[0008] Step 1: Requirement Analysis and System Design;

[0009] Step 2: Select Devices and Technologies;

[0010] Step 3: System Architecture Design;

[0011] Step 4: Development of Edge Computing Module;

[0012] Step 5: Integration of AI Intelligent Analysis;

[0013] Step 6: System Testing and Optimization;

[0014] Step 7: System Deployment.

[0015] Preferably, Step 1: Requirement Analysis and System Design specifically includes:

[0016] Determine user requirements: By interviewing cardiologists and patients, collect the problems and requirements they encounter when using the existing remote consultation system;

[0017] Function design: According to the requirement analysis, design the core functions of the system, including real-time electrocardiogram monitoring, AI intelligent analysis, emergency alerts, video consultations, and data report generation.

[0018] Preferably, Step 2: Select Devices and Technologies specifically includes:

[0019] Device selection: Select wearable devices with built-in edge computing capabilities to ensure that they can collect electrocardiograms and other physiological data in real time;

[0020] Determine the technology stack: Select appropriate AI algorithms for electrocardiogram analysis to ensure high accuracy and low latency.

[0021] Preferably, Step 3: System Architecture Design specifically includes:

[0022] Architecture construction: Design the system architecture, including patient-side devices, edge computing modules, cloud service platforms, and doctor-side applications. The edge computing module is responsible for preliminary data processing, the cloud service platform is responsible for data storage and management, and the doctor-side application is used for remote consultations and data access;

[0023] Data flow design: Establish a data flow diagram to clarify the transfer process of data from wearable devices to edge computing modules, then to cloud services and doctor sides.

[0024] Preferably, Step 4: Development of Edge Computing Module specifically includes:

[0025] Real-time data processing: Develop an edge computing module to ensure that it can process electrocardiogram data locally on the device in real time and screen out abnormal waveforms;

[0026] Generate an alarm: When an abnormal waveform is detected, the edge computing module immediately generates an alarm and pushes it to the patient and the doctor through the application.

[0027] Preferably, step five: AI intelligent analysis integration, specifically including:

[0028] Model training: Use the historical electrocardiogram dataset to train the AI model and optimize its ability to identify abnormal electrocardiograms;

[0029] Real-time analysis: Integrate the trained model into the edge computing module to ensure that it can analyze immediately after data collection and give accurate results.

[0030] Preferably, step six: System testing and optimization, specifically including:

[0031] Function testing: Conduct comprehensive testing on the system, including device connection, data transmission, accuracy of AI analysis, and user interface friendliness;

[0032] User feedback: Invite cardiologists and patients to try out, collect feedback, and make necessary adjustments and optimizations.

[0033] Preferably, step seven: System deployment, specifically including:

[0034] System deployment: Deploy the system within the hospital, including installing software, configuring devices, and cloud services.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] 1. By introducing edge computing technology, real-time data processing can be achieved, reducing data transmission latency. This means that electrocardiogram data can be analyzed immediately, greatly improving the response speed to emergencies such as arrhythmia and ensuring that patients can receive timely medical intervention at critical moments;

[0037] 2. The integrated AI intelligent analysis technology can effectively integrate data from different devices, improving the analysis accuracy of electrocardiograms and other physiological parameters. This not only reduces possible omissions by clinicians in complex data but also provides more reliable decision-making support for doctors, thus improving the overall medical quality;

[0038] 3. Through the application of edge computing and AI analysis technologies, doctors can obtain accurate data faster, thereby reducing unnecessary repeated examinations and the number of patient visits. This not only optimizes the doctor's workflow, improves medical efficiency, but also enables more reasonable allocation and utilization of medical resources. Brief Description of the Drawings

[0039] Figure 1 It is a schematic flowchart of the present invention. Detailed implementation manners

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0041] Please refer to Figure 1 , the present invention provides a technical solution:

[0042] An innovative clinical remote consultation system for cardiology department, including the following steps:

[0043] Step 1: Requirement analysis and system design;

[0044] Step 2: Select equipment and technology;

[0045] Step 3: System architecture design;

[0046] Step 4: Edge computing module development;

[0047] Step 5: AI intelligent analysis integration;

[0048] Step 6: System testing and optimization;

[0049] Step 7: System deployment.

[0050] Step 1: Requirement analysis and system design, specifically including:

[0051] Determine user requirements: By interviewing cardiologists and patients, collect the problems and requirements they encounter when using the existing remote consultation system;

[0052] Formulate an interview outline, clarify the interview objectives, including understanding the specific problems encountered by users in the existing remote consultation system, expected functions and usage experiences, etc., determine the interview objects, including cardiologists, nurses and patients, ensure sample diversity, ask doctors about the difficulties encountered in remote consultation, such as data integration problems, real-time monitoring information, accuracy of patient health data, etc., understand doctors' expectations for system functions, such as stronger real-time monitoring capabilities, intuitive data analysis tools, convenient patient communication, etc., ask patients about their experiences when using the existing system, including operation complexity, information acquisition convenience, understanding ability of health data, etc., and collect patients' requirements for the future system, such as what health information they hope to obtain, expectations for video consultations, responses to emergency alerts, etc.

[0053] Functional Design: Based on the requirements analysis, design the core functions of the system, including real-time electrocardiogram (ECG) monitoring, AI intelligent analysis, emergency alerts, video consultations, and data report generation.

[0054] Design the system to facilitate the real-time collection of patients' ECG data and ensure the accuracy and continuity of the data. Integrate advanced AI algorithms to perform intelligent analysis on the ECG data, automatically identify potential abnormalities such as arrhythmias, set relevant criteria, and once an anomaly is detected, the system automatically sends alerts to doctors and patients to ensure timely response. Design a simple video consultation interface to ensure that doctors and patients can conveniently communicate face-to-face. Design a function to automatically generate health reports for patients and doctors to facilitate subsequent medical decision-making.

[0055] Through requirements analysis, clearly identify the pain points and expectations of users in the existing system to ensure a clear direction for subsequent design and development work. In terms of functional design, based on user feedback, more accurately develop core functions that meet market needs and enhance the competitiveness of the product.

[0056] Step 2: Select Devices and Technologies, specifically including:

[0057] Device Selection: Select wearable devices with built-in edge computing capabilities to ensure that they can collect electrocardiograms and other physiological data in real time;

[0058] Determine the Technology Stack: Select appropriate AI algorithms for electrocardiogram analysis to ensure high accuracy and low latency.

[0059] Research wearable devices on the current market, especially products focused on ECG monitoring, such as smartwatches, ECG monitoring belts, etc. Confirm whether the selected device has edge computing capabilities and can process data locally without relying on a cloud platform. Check the processor performance, memory, and storage capacity of the device to ensure that it can quickly process electrocardiogram data and perform preliminary analysis. Ensure that the device can collect electrocardiograms, heart rates, motion states, and other key physiological data such as blood oxygen saturation in real time. Verify whether the device can maintain the accuracy of data collection under different conditions, such as during exercise, rest, and sleep states, and select a device that meets the above conditions;

[0060] Determine to use deep learning models such as convolutional neural networks (CNNs) because of their excellent performance in image recognition and signal processing. Compare different deep learning architectures (such as LSTM, GRU, etc.) and evaluate their applicability in electrocardiogram analysis. Select the most suitable one, collect and organize a large-scale electrocardiogram dataset for model training and validation, ensure the diversity and representativeness of the dataset, and if necessary, consider using data augmentation techniques to increase the diversity of training data.

[0061] Use the selected deep learning model to train the collected electrocardiogram data, optimize the accuracy and inference speed of the model, and use techniques such as cross-validation to adjust the hyperparameters of the model to achieve the best performance. After training, use an independent test set to evaluate the model to ensure that its accuracy and low-latency performance meet the requirements. Analyze the prediction results of the model to ensure that it has a low false positive and false negative rate to increase the reliability of clinical applications.

[0062] Through the selection of devices and technologies, ensure that the system has efficient and accurate basic support, providing a solid technical guarantee for the implementation of subsequent functions. The combination of edge computing and deep learning significantly improves the real-time processing ability and intelligent analysis level of the system, providing real-time and effective health monitoring for patients and doctors. The clear technology selection provides a direction for subsequent version updates and function expansions, facilitating the integration of more health monitoring functions and data analysis capabilities in the future.

[0063] Step 3: System architecture design, specifically including:

[0064] Architecture construction: Design the system architecture, including the patient-side device, edge computing module, cloud service platform, and doctor-side application. The edge computing module is responsible for preliminary data processing, the cloud service platform is responsible for data storage and management, and the doctor-side application is used for remote consultation and data access;

[0065] Data flow design: Establish a data flow diagram to clarify the transfer process of data from the wearable device to the edge computing module, and then to the cloud service and the doctor side.

[0066] Integrate sensors through the wearable device to collect electrocardiogram data and other physiological information in real time. Integrate the edge computing module in the patient-side device, which is responsible for preliminary processing and analysis of the collected electrocardiogram data. This module will use AI algorithms to analyze the data in real time and can generate preliminary reports. Establish a cloud service platform, which is responsible for storing and managing the patient's historical data and analysis results. The cloud platform also supports big data analysis and machine learning to continuously optimize algorithms and models. Develop a doctor-side application that allows doctors to access patient data in real time, conduct remote consultations, view electrocardiograms and analysis reports, and communicate with patients. Patient-side device: Collect electrocardiogram data in real time, display health monitoring information, and receive doctor feedback and notifications. Edge computing module: Quickly process electrocardiogram data, identify abnormalities, and generate alarm information. Cloud service platform: Store the patient's health data, support data analysis and report generation, and provide a comprehensive historical record for doctors. Doctor-side application: Provide a health monitoring view, support video consultations and information sharing, and be able to provide intelligent decision support;

[0067] Data Flow Diagram Establishment: Design a data flow diagram to show the data flow from the patient-side device to the edge computing module, then to the cloud service platform and the doctor-side application.

[0068] Data Acquisition: The device worn by the patient collects electrocardiogram and other physiological data in real time.

[0069] Edge Computing Processing: The data is sent to the edge computing module for real-time analysis to generate analysis results and alerts.

[0070] Data Upload: The processed data and analysis results are uploaded to the cloud service platform for long-term storage.

[0071] Doctor Access: Doctors access the cloud platform through the doctor-side application to obtain the patient's historical data and real-time monitoring information for remote consultation.

[0072] Design a data flow diagram containing the following elements: patient-side device → edge computing module → cloud service platform → doctor-side application, mark the data flow with arrows, and use symbols to represent different data processing stages such as data acquisition, processing, storage, and access.

[0073] Through the system architecture design, the functions and responsibilities of each module are clearly defined, providing a clear blueprint for subsequent development. The optimized data flow design ensures the efficient flow of information from acquisition to analysis to access, providing good real-time performance and reliability for the system. Through the design of the cloud service platform, data security measures can be implemented to ensure the protection of patient privacy and compliance with medical data regulations.

[0074] Step 4: Edge Computing Module Development, specifically including:

[0075] Real-time Data Processing: Develop the edge computing module to ensure that it can process electrocardiogram data locally on the device in real time and screen out abnormal waveforms;

[0076] Generate Alerts: When abnormal waveforms are detected, the edge computing module immediately generates alerts and pushes them to the patient and doctor through the application;

[0077] Module Architecture Design: Design the architecture of the edge computing module, including four main functional modules: data acquisition, preprocessing, real-time analysis, and anomaly detection. Determine the hardware requirements for the module to run and ensure efficient operation on the patient's wearable device.

[0078] Data Acquisition: Obtain real-time electrocardiogram data from the patient-side device, ensure a high-frequency sampling rate and accuracy of the data, and design a data transmission interface to ensure fast and stable data transmission from the sensor to the edge computing module.

[0079] Data preprocessing: Perform data preprocessing, including denoising, normalization, and feature extraction, to improve the accuracy of subsequent analysis. Use a filtering algorithm (such as a Butterworth filter) to filter the signal and remove environmental interference and device noise.

[0080] Real-time analysis and anomaly detection: Select a suitable deep learning model such as a convolutional neural network to perform real-time analysis on the preprocessed electrocardiogram. Train the model to identify abnormal waveforms such as arrhythmias to ensure its stable operation in various situations such as during exercise.

[0081] Performance optimization: Optimize the model to ensure that its response time in an edge computing environment is below a set threshold such as 100 milliseconds to achieve real-time feedback.

[0082] Generate alerts, including anomaly waveform recognition: When the edge computing module detects abnormal waveforms (such as atrial fibrillation, bradycardia, or tachycardia, etc.) in the electrocardiogram, immediately record the relevant data and its characteristics.

[0083] Set reasonable thresholds and rules in the algorithm to ensure a balance between the sensitivity and specificity of anomaly detection, reducing false alarms and missed detections.

[0084] Alert generation: Once an abnormal waveform is detected, immediately trigger the alert mechanism to generate alert information, including the type of anomaly, timestamp, and relevant data analysis results. Format the alert information into an easy-to-understand report that contains necessary health advice or suggestions for the next steps.

[0085] Push notifications: Push alert information to patients and doctors through the application to ensure that they can receive important health information in a timely manner. Set the priority and notification methods (such as sound, vibration, pop-up window) of the push to ensure that important information is not overlooked.

[0086] The development of the edge computing module enables the system to achieve true real-time monitoring, enhancing patient safety. It can timely identify and handle potential health crises. Introducing AI-driven anomaly detection algorithms adds the ability of intelligent analysis to the entire system, providing data support for subsequent medical decisions. The real-time generated alerts and health advice can not only help patients handle health problems in a timely manner but also provide key information for doctors to support them in making more rapid and effective clinical decisions. The design of the edge computing module ensures that the system can still operate normally even in the case of unstable network, improving the overall reliability of the system.

[0087] Step Five: AI intelligent analysis integration, specifically including:

[0088] Model training: Use historical electrocardiogram datasets to train the AI model and optimize its ability to identify abnormal electrocardiograms;

[0089] Real-time analysis: Integrate the trained model into the edge computing module to ensure that it can analyze the data immediately after collection and give accurate results.

[0090] Dataset preparation: Collect and organize a large number of historical electrocardiogram datasets, ensuring that they contain samples of various arrhythmias such as atrial fibrillation, bradycardia, tachycardia, etc. Label the data to ensure that each sample is attached with accurate diagnostic information for the effective conduct of supervised learning.

[0091] Select model architecture: Select a deep learning model suitable for electrocardiogram analysis, such as a convolutional neural network (CNN), as it performs well in image and signal processing. Consider using models suitable for time series data such as long short-term memory networks (LSTM) to capture the temporal features in the electrocardiogram signal.

[0092] Model training: Divide the organized dataset into training set, validation set, and test set, conduct model training and evaluation, use techniques such as cross-validation and early stopping to optimize the hyperparameters of the model, and ensure the maximization of the model's ability to identify abnormal electrocardiograms.

[0093] Model evaluation and optimization: Evaluate the performance of the model on the test set, pay attention to indicators such as accuracy, sensitivity, and specificity to ensure that it can effectively identify various abnormal waveforms. Analyze the model output, optimize the model architecture, and try different regularization techniques to prevent overfitting.

[0094] Model saving and deployment: Save the trained model in a format that can be called by the edge computing module to ensure that it can be quickly loaded and run in the local environment.

[0095] Real-time analysis operation steps:

[0096] Model integration: Load the trained AI model into the edge computing module to ensure that it can analyze quickly after data collection. Perform necessary programming and interface design (techniques well-known to those skilled in the art) to ensure efficient data interaction between the edge computing module and the model.

[0097] Real-time data processing:

[0098] After data collection is achieved, the edge computing module immediately transmits the data to the AI model for analysis.

[0099] The model processes the electrocardiogram data, real-time identifies abnormal waveforms, and generates analysis results.

[0100] Result feedback: Feed back the output results of the model (such as abnormal types and possible health risks) to the edge computing module, generate alarm information, and realize the visualization of the results to facilitate patients and doctors to understand the analysis results.

[0101] The integration of AI intelligent analysis brings a higher level of intelligence to the system, enabling the device to make accurate judgments even in complex situations, enhancing the practicality of the system. The feedback of real-time analysis results enables doctors to respond more quickly to the health status of patients, thus improving the quality and efficiency of medical services. By analyzing the real-time data of patients, the system can provide personalized health management suggestions for patients, enhancing patients' trust and satisfaction.

[0102] Step Six: System Testing and Optimization, specifically including:

[0103] Function Testing: Conduct comprehensive testing on the system, including device connection, data transmission, the accuracy of AI analysis, and the friendliness of the user interface.

[0104] User Feedback: Invite cardiologists and patients to conduct trials, collect feedback, and make necessary adjustments and optimizations.

[0105] Function Testing Operation Steps:

[0106] Device Connection Testing: Ensure that all hardware devices (such as wearable sensors, edge computing modules) can be successfully connected and can stably collect data. Test the connection stability in various situations, including different network conditions (such as Wi-Fi, mobile data) and multiple operating environments.

[0107] Data Transmission Testing: Verify the transmission efficiency of data from the device to the edge computing module, ensure that the data arrives within the set latency range, test the speed and accuracy of uploading data to the cloud service platform, and ensure data integrity and consistency.

[0108] AI Analysis Accuracy Testing: Evaluate the performance of the integrated AI model on different test data sets, including real cases and simulated cases. Use indicators such as confusion matrices and ROC curves to quantify the accuracy, sensitivity, and specificity of the model.

[0109] Comprehensive System Performance Testing: Conduct stress testing on the system, simulate the scenario of a large number of users using it simultaneously, evaluate the stability and response speed of the system, and test the performance of the system after long-term operation to ensure that there will be no problems such as memory leaks or crashes in actual use.

[0110] User Feedback Operation Steps:

[0111] Trial Invitation: Invite cardiologists and some patients to participate in the trial of the system, introduce the functions and operation procedures of the system, ensure that the participants can use the system in a real environment, and collect their feelings and opinions during the trial.

[0112] Feedback collection: Design questionnaires or interview forms that cover multiple aspects, including system functions, user experience, data accuracy, and response speed, etc. Encourage users to provide specific examples to gain a deeper understanding of their usage experiences and areas for expected improvement.

[0113] Data analysis and adjustment: Analyze the collected feedback, identify common problems and the priorities of user needs. For the functional defects or poor user experience mentioned in the feedback, formulate specific improvement plans and implement adjustments.

[0114] Iterative development: Conduct system iteration based on the feedback, optimize the user interface, fix functional defects, improve system performance, and conduct re - testing to ensure that the modified system can meet user needs, forming a good feedback loop.

[0115] The combination of functional testing and user feedback ensures the stability and reliability of the system in actual use, improves the security of medical services. By paying attention to user feedback, the system can better adapt to user needs, enhance the satisfaction of patients and doctors, increase the usage rate of the system. After being fully tested and optimized, the system can enter the market at a higher quality level, improving the success rate of market promotion. The establishment of the feedback mechanism provides a basis for subsequent technology iteration, promotes the continuous update and upgrade of the system, and enhances the industry competitiveness.

[0116] Step 7: System deployment, specifically including:

[0117] System deployment: Deploy the system within the hospital, including installing software, configuring devices, and cloud services.

[0118] System deployment operation steps:

[0119] Plan preparation: Determine the deployment environment: Collaborate with the hospital's IT department to evaluate whether the existing hardware, software, and network infrastructure meet the requirements for system operation, and formulate a detailed deployment plan, including a schedule, personnel arrangements, and necessary training content.

[0120] Install software: Install the required software on the server and each terminal device, including: edge computing modules, data management platforms, and user - side applications for doctors and patients. Ensure that the software versions are consistent and perform necessary environment configurations such as database connection and user permission settings.

[0121] Configure devices: Connect and configure all data - collection devices such as wearable electrocardiogram monitors to ensure their normal interaction with the edge - computing module, and conduct preliminary device testing to ensure that data can be transmitted stably and accurately.

[0122] Cloud service configuration: Deploy a cloud service platform to ensure the ability to store and process data, including user data, AI models, and analysis results. Configure data security mechanisms such as encryption and access control to ensure the privacy and security of patient data.

[0123] System integration testing: After the system deployment is completed, conduct a comprehensive round of integration testing to ensure that all modules such as data collection, edge computing, cloud services, and client applications can cooperate seamlessly. Conduct stress testing to ensure that the system can still operate stably under high load conditions.

[0124] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An innovative cardiology clinical remote consultation system, characterized by: The steps include: Step 1: Requirements analysis and system design; Step 2: Select equipment and technology; Step 3: System architecture design; Step 4: Edge computing module development; Step 5: AI intelligent analysis integration; Step 6: System testing and optimization; Step 7: System deployment.

2. The innovative cardiology clinical teleconsultation system according to claim 1 is characterized by: The step 1: demand analysis and system design, specifically includes: Determine user needs: Interview cardiologists and patients to collect their problems and needs when using existing teleconsultation systems; Functional design: Based on demand analysis, design the core functions of the system, including real-time ECG monitoring, AI intelligent analysis, emergency alarm, video consultation and data report generation.

3. The innovative cardiology clinical teleconsultation system according to claim 2 is characterized by: The step 2: selecting equipment and technology, specifically includes: Device selection: Choose wearable devices with built-in edge computing capabilities to ensure they can collect ECG and other physiological data in real time; Technology stack determination: Select appropriate AI algorithms for ECG analysis to ensure high accuracy and low latency.

4. The innovative cardiology clinical teleconsultation system according to claim 3 is characterized by: The step three: system architecture design, specifically includes: Architecture construction: Design the system architecture, including patient-side devices, edge computing modules, cloud service platforms, and doctor-side applications. The edge computing module is responsible for preliminary data processing, the cloud service platform is responsible for data storage and management, and the doctor-side application is used for remote consultation and data access; Data flow design: Create a data flow diagram to clarify the flow of data from wearable devices to edge computing modules, and then to cloud services and doctors.

5. The innovative cardiology clinical teleconsultation system according to claim 1 is characterized by: Step 4: edge computing module development, specifically including: Real-time data processing: Develop edge computing modules to ensure that they can process ECG data in real time locally on the device and screen out abnormal waveforms; Generate alerts: When an abnormal waveform is detected, the edge computing module immediately generates an alert and pushes it to patients and doctors through the application.

6. The innovative cardiology clinical teleconsultation system according to claim 1 is characterized by: The step 5: AI intelligent analysis integration, specifically includes: Model training: Use historical ECG datasets to train AI models and optimize their ability to identify abnormal ECGs; Real-time analysis: Integrate the trained model into the edge computing module to ensure that it can analyze data immediately after collection and provide accurate results.

7. The innovative cardiology clinical teleconsultation system according to claim 1 is characterized by: The step six: system testing and optimization, specifically includes: Functional testing: Comprehensive testing of the system, including device connection, data transmission, accuracy of AI analysis, and user interface friendliness; User feedback: Invite cardiologists and patients to try out the system, collect feedback, and make necessary adjustments and optimizations.

8. The innovative cardiology clinical teleconsultation system according to claim 1 is characterized by: The step seven: system deployment, specifically includes: System deployment: Deploy the system within the hospital, including installing software, configuring devices and cloud services.

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