Self-adaptive cardiology department clinical operation auxiliary system

Abstract

The invention discloses a self-adaptive cardiology department clinical operation auxiliary system. The system comprises the following steps that 1, a sensor combination is selected; 2, acquiring and synchronizing sensor data; 3, data fusion and analysis; step 4, generating a decision support report; and 5, postoperative data analysis and feedback. By integrating various high-precision sensors, real-time monitoring of multi-dimensional physiological parameters of a patient is achieved, it is ensured that a doctor can comprehensively master the physiological state of the patient, and therefore the safety and effectiveness of an operation are improved, and by means of a dynamic data fusion algorithm and a machine learning model, the system can rapidly analyze real-time data and generate a decision support report; the system helps doctors to quickly respond, reduces risks caused by information lag, continuously improves operation schemes and monitoring strategies through a postoperative data analysis and feedback mechanism, improves the overall medical quality, and promotes recovery and long-term health of patients.

Description

Technical Field

[0001] The present invention relates to the technical field of cardiology clinical technology, and particularly to an adaptive cardiology clinical surgical assistance system. Background Technique

[0002] Cardiology clinical surgical assistance systems have now become an indispensable technical support tool in cardiac interventional surgeries. Existing technologies mainly include high-resolution imaging technologies (such as echocardiography, CT, and MRI) for providing real-time cardiac structure and function data to help doctors perform precise surgical planning. In addition, the application of data analysis and artificial intelligence technologies enables doctors to integrate patients' historical medical records and real-time physiological parameters for risk assessment and decision support. The system can also use machine learning algorithms to predict postoperative recovery and optimize surgical plans. During the surgery, real-time monitoring technologies can continuously track key indicators such as heart rate, blood pressure, and oxygen saturation to ensure that doctors can respond to emergencies in a timely manner. The combination of these technologies not only improves the safety and efficiency of surgeries but also promotes the rapid development of the cardiology field.

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

[0004] Traditional cardiology surgical assistance systems often rely on single or limited physiological monitoring means and cannot comprehensively and real-time capture changes in multiple physiological parameters of patients, resulting in an incomplete and untimely assessment of the patient's condition. Existing systems often have time delays in data processing and analysis and cannot generate effective decision support reports in real time, causing doctors to face problems of insufficient information and untimely responses during surgeries, which affects the safety and effectiveness of surgeries. Summary of the Invention

[0005] The purpose of the present invention is to provide an adaptive cardiology clinical surgical assistance system to solve the problems raised in the above background technique.

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

[0007] An adaptive cardiology clinical surgical assistance system includes the following steps:

[0008] Step 1: Select a sensor combination;

[0009] Step 2: Sensor data acquisition and synchronization;

[0010] Step 3: Data fusion and analysis;

[0011] Step 4: Generate a decision support report;

[0012] Step 5: Postoperative data analysis and feedback.

[0013] Preferably, Step 1: Selecting the sensor combination specifically includes:

[0014] Select sensors:

[0015] Photoelectric volume pulse wave sensor: used to monitor heart rate and pulse waveform in real time.

[0016] Respiration monitoring sensor: used to record respiratory rate and oxygen saturation.

[0017] Electrocardiogram monitor: records electrocardiogram signals in real time to evaluate cardiac function.

[0018] Preferably, Step 2: Sensor data acquisition and synchronization specifically includes:

[0019] Before the operation, all sensors are non-invasively installed on the patient, the data acquisition system is started, the sensors start to monitor physiological parameters in real time, and the data synchronization module is used to ensure that the data of all sensors are recorded with the same timestamp, avoiding data errors caused by time differences.

[0020] Preferably, Step 3: Data fusion and analysis specifically includes:

[0021] Adopt a dynamic data fusion algorithm to integrate data from different sensors. The system uses a machine learning model to analyze the real-time acquired data, identify the physiological change patterns of the patient, set thresholds, establish an early warning mechanism, monitor abnormal data in real time, and generate alarms.

[0022] Preferably, Step 4: Generating a decision support report specifically includes:

[0023] During the operation, the system will generate a decision support report in real time, summarizing the current physiological parameters, change trends and early warning information. Through the visualization interface, the report is presented to the operation team in the form of charts and key indicators to ensure the intuitiveness and usability of the information. Based on the evaluation of the report, the doctor can adjust the operation plan in a timely manner, such as changing the anesthesia dose or adjusting the operation steps when necessary.

[0024] Preferably, Step 5: Postoperative data analysis and feedback specifically includes:

[0025] After the operation, the system will analyze the data collected during the operation and generate a detailed postoperative report. The report content includes the change trends of the patient's physiological parameters, the records of early warning events and the doctor's treatment measures. Through subsequent data analysis, the experience during the operation is refined to provide reference for future operations.

[0026] Compared with the prior art, the beneficial effects of the present invention are:

[0027] 1. By integrating a variety of high-precision sensors, real-time monitoring of patients' multi-dimensional physiological parameters is achieved, ensuring that doctors can comprehensively grasp the physiological state of patients, thereby improving the safety and effectiveness of surgeries.

[0028] 2. Using dynamic data fusion algorithms and machine learning models, the system can quickly analyze real-time data and generate decision support reports to help doctors respond promptly and reduce the risks brought by information lag.

[0029] 3. Through postoperative data analysis and feedback mechanisms, the surgical plan and monitoring strategies are continuously improved to enhance the overall medical quality and promote the recovery and long-term health of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0031] The technical solutions in the embodiments of the present invention will be clearly and completely described below 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.

[0032] Please refer to Figure 1 , the present invention provides a technical solution:

[0033] An adaptive clinical surgical assistance system for cardiology includes the following steps:

[0034] Step 1: Select a sensor combination;

[0035] Step 2: Sensor data acquisition and synchronization;

[0036] Step 3: Data fusion and analysis;

[0037] Step 4: Generate a decision support report;

[0038] Step 5: Postoperative data analysis and feedback.

[0039] Step 1: Select a sensor combination, specifically including:

[0040] Select sensors:

[0041] Photoplethysmogram sensor: Used to monitor heart rate and pulse waveform in real time.

[0042] Respiratory monitoring sensor: Used to record respiratory rate and oxygen saturation.

[0043] Electrocardiogram monitor: Records electrocardiogram signals in real time to evaluate cardiac function.

[0044] For the selection of a photoplethysmogram sensor, it is necessary to ensure that the selected sensor uses red and near-infrared light wavelengths, can effectively penetrate the skin to obtain pulse signals, and ensure that the sensor has a low signal-to-noise ratio and a high sampling frequency (at least 100 samples per second) to capture heart rate changes in a timely manner.

[0045] For the selection of a respiratory monitoring sensor, select a sensor that can monitor respiratory rate and oxygen saturation in real time, and select a hybrid sensor with optical and impedance technologies that can monitor both respiratory rate and blood oxygen saturation (SpO2). Priority should be given to those sensors with a fast response time of less than 1 second to ensure the non-invasive design of the sensor and not affect the patient's comfort.

[0046] For the selection of an electrocardiogram monitor, ensure that the selected device has the function of 12 leads to comprehensively monitor cardiac electrical activities, conduct lead connection tests to ensure correct lead positions, and the data can accurately reflect the cardiac state. Regularly check the device performance to ensure its high precision in clinical use.

[0047] Comprehensive and accurate physiological data provides a richer information basis for the surgical team, helps doctors make more scientific decisions during the operation, and can monitor changes in physiological parameters in real time and issue alarms, enabling timely detection of potential risks, providing early warnings for doctors, and reducing surgical risks.

[0048] Step 2: Sensor data acquisition and synchronization, specifically including:

[0049] Before the operation, install all sensors non-invasively on the patient, start the data acquisition system, and the sensors start to monitor physiological parameters in real time. Use the data synchronization module to ensure that the data of all sensors are recorded with the same timestamp to avoid data errors caused by time differences.

[0050] Photoplethysmogram sensor: Install the sensor on the patient's finger or earlobe to ensure good contact and avoid data errors caused by poor blood circulation.

[0051] Respiratory monitoring sensor: Fix it on the patient's chest to ensure the correct position and not cause discomfort to the patient at the same time.

[0052] ECG electrodes: Attach at least 12 electrodes to the patient's chest according to the lead arrangement diagram and ensure good contact between the electrodes and the skin to obtain a clear electrocardiogram signal.

[0053] Before the operation starts, start the central monitoring system and connect all sensors.

[0054] Ensure that the data acquisition system can receive real-time data streams from each sensor.

[0055] Real-time monitoring:

[0056] Each sensor starts to collect physiological data in real time, such as heart rate, pulse waveform, respiratory rate, and blood oxygen saturation. The monitoring system displays all parameters on the real-time interface for the surgeon to view at any time;

[0057] Using the data synchronization module, ensure that the data of all sensors are recorded with the same timestamp to avoid data errors caused by time differences. The specific steps are as follows:

[0058] Data synchronization module configuration:

[0059] Configure the data synchronization module to ensure that it can timestamp the data from each sensor and support multiple data formats to adapt to the outputs of different sensors.

[0060] Real-time synchronization test:

[0061] Conduct a full-system test before the operation to ensure that the data of all sensors can be accurately synchronized. Record the test data, check whether the timestamps are consistent, and confirm that there is no data delay.

[0062] Monitoring and adjustment:

[0063] Continuously monitor the data synchronization status during the operation. If any abnormality is found, adjust it immediately to ensure that in any case, all physiological data are recorded and displayed with the same timestamp;

[0064] Existing technologies often rely on single or intermittent monitoring, which may lead to the omission or delay of important physiological parameters. This solution provides comprehensive real-time monitoring, enabling doctors to promptly understand the patient's physiological state and make quick responses. Through the synchronization module, ensure that the data of all sensors are recorded with the same timestamp to avoid data errors, which is particularly important in first aid and high-risk surgeries. It can reduce misdiagnosis or delayed treatment caused by data delay. The real-time collected and synchronized physiological data provide a comprehensive information basis for the surgical team, enhancing the understanding of the patient's state and improving the efficiency and accuracy of clinical decision-making.

[0065] Step 3: Data fusion and analysis, specifically including:

[0066] Adopt a dynamic data fusion algorithm to integrate the data from different sensors. The system uses a machine learning model to analyze the real-time obtained data, identify the physiological change patterns of the patient, set thresholds, establish an early warning mechanism, monitor abnormal data in real time, and generate alarms.

[0067] Select a suitable dynamic data fusion algorithm: Select dynamic data fusion algorithms such as Kalman filter, particle filter, or weighted average, which can effectively process the data from different sensors.

[0068] Data preprocessing: Preprocess the raw data collected from various sensors, including noise removal, normalization, etc., to ensure the accuracy of the data.

[0069] Data fusion: Use the selected dynamic data fusion algorithm to integrate the data provided by different sensors (PPG, respiratory monitoring, ECG) into a unified data stream. Through timestamp synchronization, the fused data can reflect the actual physiological state.

[0070] The system uses a machine learning model to analyze the real-time acquired data and identify the physiological change patterns of the patient. The specific steps are as follows:

[0071] Model selection and training: Select appropriate machine learning models such as decision trees, support vector machines, or deep learning models, and use historical data for training to ensure that the model can learn the characteristics corresponding to different physiological states such as rest, exercise, and pathological states.

[0072] Real-time data input: Input the fused real-time data into the trained machine learning model for online analysis to identify the change patterns of the patient's physiological state, such as heart rate changes and respiratory abnormalities.

[0073] Result output: Generate an evaluation report on the patient's physiological state based on the model analysis results. The report includes the change trends of key physiological indicators and potential risks.

[0074] Set thresholds, establish an early warning mechanism, monitor abnormal data in real time, and generate alarms:

[0075] Threshold setting: Set the normal ranges and abnormal thresholds of various physiological parameters (such as heart rate, respiratory rate, blood oxygen saturation) based on clinical standards and historical data analysis.

[0076] Monitoring mechanism: Implement real-time monitoring, continuously track the patient's physiological parameters, and ensure that data is continuously input into the monitoring system.

[0077] Generate alarms: When any physiological parameter is detected to exceed the set threshold, the system immediately triggers an alarm. The alarm information can be notified to the surgical team through various methods such as sound, graphical display, and information push to ensure timely handling.

[0078] Traditional systems often can only process the data of a single sensor, resulting in information silos. However, this solution integrates multiple physiological parameters into a unified data stream through a dynamic data fusion algorithm, enhancing the comprehensiveness of monitoring. Through dynamic data fusion and real-time analysis, the surgical team can more comprehensively understand the patient's physiological state, thus making more scientific clinical decisions, enhancing patient safety. The real-time monitoring and early warning mechanism effectively reduces the surgical risks caused by the failure to detect abnormal physiological parameters in a timely manner and can provide a safer surgical environment for patients.

[0079] Step 4: Generate a decision support report, specifically including:

[0080] During the operation, the system will generate a decision support report in real time, summarizing the current physiological parameters, trends, and warning information. Through a visual interface, the report will be presented to the surgical team in the form of charts and key indicators to ensure the intuitiveness and usability of the information. Based on the evaluation of the report, the doctor will adjust the surgical plan in a timely manner, such as changing the anesthesia dose or adjusting the surgical steps if necessary.

[0081] Development module, generating a decision support report based on real-time data, specific steps:

[0082] Module design: Develop a dedicated report generation module that can interface with the data acquisition system and the data analysis system to achieve real-time data acquisition, and ensure that the module has flexible configuration functions to generate corresponding reports according to different surgical types and patient needs.

[0083] Data extraction: Extract real-time physiological parameters from the output of dynamic data fusion and machine learning analysis, including key information such as heart rate, respiratory rate, and blood oxygen saturation. Collect trend data on various indicators and generate time series charts to more intuitively display changes in physiological status.

[0084] Report format: Design an easy-to-understand report format that includes charts and graphical displays, enabling medical staff to quickly obtain important information. Ensure that the report can be exported in multiple formats (such as PDF, Excel) for easy archiving and sharing.

[0085] The report content includes real-time physiological parameters, trends, and warning information, specific steps:

[0086] Real-time physiological parameters: List the currently monitored physiological parameters in the report, such as heart rate, respiratory rate, blood oxygen saturation, etc., and attach the latest values.

[0087] Trends: Include trend charts of physiological parameters to show the dynamic changes during the operation. Line charts, bar charts, etc. can be used for easy observation and analysis.

[0088] Warning information: Clearly indicate any physiological parameters that exceed the normal range and attach corresponding warning descriptions. Mark the time and severity of the alarm to help medical staff quickly identify risks.

[0089] The generated decision support report provides comprehensive and timely data support for doctors, helping them make more informed judgments and decisions during surgeries, improving the quality of medical services. The report can be quickly shared within the surgical team and with other medical staff, enhancing the efficiency of team communication and facilitating multidisciplinary collaboration. The generated report will form a complete patient monitoring record, providing valuable data support for subsequent clinical analysis, research, and continuous improvement, and contributing to the improvement of medical technology levels.

[0090] Step Five: Post-operative data analysis and feedback, specifically including:

[0091] After the surgery, the system will analyze the data collected during the operation and generate a detailed post-operative report. The report content includes the trend of patient physiological parameters, records of warning events, and the doctor's treatment measures. Through subsequent data analysis, the experience during the operation will be refined to provide reference for future surgeries.

[0092] After the surgery, the system will analyze the data collected during the operation and generate a detailed post-operative report. The specific steps are as follows:

[0093] Data collection and collation: After the surgery, the system automatically collects all physiological parameter data, warning information, and the doctor's real-time treatment records during the operation, and conducts sorting and archiving.

[0094] Data analysis: Using statistical analysis methods, trend analysis is carried out on the collected physiological parameters to identify the changes in various physiological indicators of the patient during the operation. For warning events, analyze their occurrence frequency, type, and impact, and evaluate their impact on the operation process.

[0095] Report generation: Generate a detailed post-operative report, covering the trend of patient physiological parameters, records of warning events, and the doctor's treatment measures.

[0096] The report content includes the trend of patient physiological parameters, records of warning events, and the doctor's treatment measures. The specific steps are as follows:

[0097] Trend of physiological parameters: In the report, use charts to show the trend of various physiological indicators (such as heart rate, respiratory rate, blood oxygen saturation, etc.), including comparative analysis before, during, and after the surgery.

[0098] Record of warning events: List all warning events triggered during the operation, including their occurrence time, event type, severity, and the doctor's response measures for review and learning.

[0099] Doctor's treatment measures: For each warning event, record the specific treatment measures taken by the doctor at the time of the incident, and conduct an evaluation to analyze its effectiveness and room for improvement.

[0100] Through subsequent data analysis, extract the key experiences during the operation to provide reference for future operations. The specific steps are as follows:

[0101] Key experience extraction: Based on the postoperative report, identify the successful treatment measures and areas for improvement during the operation, and summarize and organize these experiences.

[0102] Establish a knowledge base: Compare the extracted key experiences with similar operation cases to form a reference knowledge base to provide guidance for future similar operations.

[0103] Feedback mechanism: Establish a feedback mechanism to feedback the analysis results to the operation team for discussion and summary, and continuously optimize the operation process.

[0104] Through postoperative data analysis and feedback, it is possible to improve the medical team's understanding of the operation process, enhance the medical quality, reduce the occurrence of complications. Report generation and experience sharing promote communication and collaboration within the operation team, help establish a more effective teamwork mechanism, and provide a scientific basis for the hospital's medical decision-making, training plans, and policy formulation through the analysis of historical data, thus promoting the optimization of the overall medical system.

[0105] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood 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 adaptive cardiology clinical surgery auxiliary system, characterized in that: The steps include: Step 1: Select the sensor combination; Step 2: Sensor data collection and synchronization; Step 3: Data fusion and analysis; Step 4: Generate decision support report; Step 5: Postoperative data analysis and feedback.

2. The adaptive cardiology clinical surgery assisting system according to claim 1, characterized in that: The step 1: selecting a sensor combination specifically includes: Select sensor: Photoplethysmography sensor: used to monitor heart rate and pulse waveform in real time; Respiratory monitoring sensor: used to record respiratory rate and oxygen saturation; Electrocardiogram monitor: records ECG signals in real time and evaluates heart function.

3. The adaptive cardiology clinical surgery auxiliary system according to claim 2, characterized in that: The step 2: sensor data collection and synchronization, specifically includes: Before the operation, all sensors are non-invasively installed on the patient, the data acquisition system is started, and the sensors begin to monitor physiological parameters in real time. The data synchronization module is used to ensure that the data of all sensors are recorded with the same timestamp to avoid data errors caused by time differences.

4. The adaptive cardiology clinical surgery auxiliary system according to claim 3, characterized in that: The step three: data fusion and analysis, specifically includes: Using a dynamic data fusion algorithm to integrate data from different sensors, the system uses a machine learning model to analyze real-time acquired data, identify the patient's physiological change patterns, set thresholds, establish an early warning mechanism, monitor abnormal data in real time, and generate alarms.

5. The adaptive cardiology clinical surgery auxiliary system according to claim 1, characterized in that: The step 4: generating a decision support report, specifically including: During the operation, the system will generate a decision support report in real time, summarizing the current physiological parameters, changing trends and early warning information. Through a visual interface, the report will be presented to the surgical team in the form of charts and key indicators to ensure the intuitiveness and ease of use of the information. Based on the evaluation report, the doctor will adjust the surgical plan in a timely manner, such as changing the anesthesia dose or adjusting the surgical steps if necessary.

6. The adaptive cardiology clinical surgery auxiliary system according to claim 1, characterized in that: The step 5: postoperative data analysis and feedback, specifically includes: After the operation, the system will analyze the data collected during the operation and generate a detailed postoperative report. The report content includes the changing trends of the patient's physiological parameters, warning event records and the doctor's treatment measures. Through subsequent data analysis, the experience of the operation can be refined to provide a reference for future operations.

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