Heart valve postoperative heart parameter management method based on three-dimensional ultrasonic model

Abstract

The invention discloses a cardiac valve postoperative cardiac parameter management method based on a three-dimensional ultrasonic model. Postoperative cardiac functions are accurately monitored through a three-dimensional ultrasonic technology. The method comprises the following steps: firstly, acquiring heart three-dimensional data by utilizing three-dimensional ultrasonic equipment, and generating a high-precision model to position a valve position; secondly, extracting valve opening degree, blood flow velocity, ventricular volume and left ventricular ejection fraction (LVEF) parameters, and comparing preoperative and postoperative data to evaluate an operation effect; then, regularly and dynamically monitoring parameter changes through three-dimensional ultrasound, analyzing a trend, identifying abnormal risks (parameters deviate from a normal value by more than 20%), and providing an early intervention basis; finally, in combination with individual differences of patients, a personalized scheme is formulated, medicine adjustment, rehabilitation training (aerobic exercise intensity is 60%-80% of the maximum heart rate) and lifestyle guidance are covered, data are shared through a cloud platform, and an intelligent early warning mechanism (abnormal parameter real-time alarm) is set. The postoperative management precision and curative effect are obviously improved, and the complication risk is reduced.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and particularly relates to a method for managing cardiac parameters after cardiac valve surgery based on a three-dimensional ultrasound model, which is used for precise monitoring, data analysis, and personalized management of postoperative cardiac function. Background Art

[0002] Cardiac valve surgery is an important means for treating cardiac valve diseases and is widely used in the treatment of mitral valve, aortic valve, and tricuspid valve lesions. The success of the surgery depends not only on the delicate operation during the surgery but also on the monitoring and management of postoperative cardiac function. The recovery of postoperative cardiac function is directly related to the long-term survival rate and quality of life of patients. Therefore, postoperative management is one of the key links in the success of cardiac valve surgery.

[0003] Traditional postoperative cardiac function monitoring mainly relies on two-dimensional ultrasound imaging technology, which evaluates cardiac function by measuring the size of cardiac chambers, the opening and closing of valves, and hemodynamic parameters. However, two-dimensional ultrasound imaging has certain limitations and cannot comprehensively display the three-dimensional structure of the heart and has insufficient accuracy in evaluating complex lesions. In addition, traditional monitoring methods mostly rely on static data, lack real-time monitoring of the dynamic changes in postoperative cardiac function, and are difficult to detect abnormal conditions early and provide targeted intervention measures.

[0004] In recent years, three-dimensional ultrasound imaging technology has gradually been applied to the diagnosis and treatment of cardiac diseases, which can provide high-resolution three-dimensional images and comprehensively display the three-dimensional structure and functional state of the heart. However, the application of three-dimensional ultrasound technology in the postoperative management of cardiac valves is still in its infancy, mainly focusing on preoperative diagnosis and intraoperative guidance, and the application in postoperative management is mostly limited to static data analysis, lacking systematic and dynamic management methods. In addition, the existing technology fails to fully utilize the potential of three-dimensional ultrasound data and lacks the combination with artificial intelligence technology, making it impossible to achieve automated and intelligent parameter extraction and analysis.

[0005] Therefore, there is an urgent need for a method for managing cardiac parameters after cardiac valve surgery based on a three-dimensional ultrasound model. Through high-precision three-dimensional imaging, dynamic data analysis, and personalized management plans, it can achieve comprehensive monitoring and optimized management of postoperative cardiac function, thereby improving the scientific nature and effectiveness of postoperative management and improving the prognosis and quality of life of patients. Summary of the Invention

[0006] The present invention relates to a method for managing cardiac parameters after cardiac valve surgery based on a three-dimensional ultrasound model, aiming to achieve comprehensive monitoring and optimized management of postoperative cardiac function through high-precision three-dimensional imaging technology, dynamic data analysis, and personalized management plans. The following are the specific technical solutions and implementation details of the present invention.

[0007] Technical Solution

[0008] Preferably, three-dimensional ultrasound data acquisition and modeling: Use a high-resolution three-dimensional ultrasound device to acquire stereoscopic image data of the patient's heart, including cardiac chambers, valve structures, and hemodynamic information. The resolution of the three-dimensional ultrasound device is not less than 0.5 mm, and it can clearly display the microscopic structure of the heart valves. Generate a three-dimensional model of the heart through a three-dimensional reconstruction algorithm, and accurately locate the valve position and functional areas. The three-dimensional reconstruction algorithm adopts a voxel-based surface reconstruction method, and the reconstruction accuracy is ±0.1 mm, ensuring the high precision and reliability of the model.

[0009] Preferably, cardiac parameter extraction and analysis: Based on the three-dimensional model, extract cardiac function parameters, including valve opening and closing degree, blood flow velocity, ventricular volume, left ventricular ejection fraction (LVEF), and stroke volume (SV). The measurement accuracy of the valve opening and closing degree is ±0.1 mm, and the normal value range is 15 - 25 mm; the measurement accuracy of the blood flow velocity is ±0.01 m / s, and the normal value range is 0.5 - 1.5 m / s; the measurement accuracy of the ventricular volume is ±1 ml, and the normal value range is 50 - 120 ml; the measurement accuracy of the left ventricular ejection fraction (LVEF) is ±1%, and the normal value range is 55% - 70%. Compare the parameter data before and after the operation to evaluate the surgical effect and the recovery of cardiac function. Through parameter comparison, quantify the surgical effect and provide a basis for subsequent management.

[0010] Preferably, dynamic monitoring and trend analysis: Obtain dynamic change data of postoperative cardiac parameters through regular three-dimensional ultrasound examinations. The time intervals for regular examinations are 1 month, 3 months, and 6 months, and the specific frequency is adjusted according to the patient's postoperative recovery. Analyze the parameter change trend and identify abnormal risks. The trend analysis adopts a linear regression model to predict the change trend of postoperative cardiac function, and the abnormal risk is defined as the parameter value deviating from the normal value range by more than 20%.

[0011] Optionally, formulate a personalized management plan: According to the parameter analysis results and combined with the patient's clinical situation, formulate a personalized postoperative management plan, including drug treatment, rehabilitation training, and lifestyle adjustment. The drug treatment plan includes dose adjustments of anticoagulants, diuretics, and beta blockers. The dose of anticoagulants is adjusted according to the international normalized ratio (INR), and the target range is 2.0 - 3.0; the dose of diuretics is adjusted according to the patient's weight and edema condition, and the range is 20 - 80 mg / day; the dose of beta blockers is adjusted according to the patient's heart rate, and the range is 25 - 100 mg / day. The rehabilitation training plan includes specific guidance on aerobic exercise, strength training, and breathing training. The intensity of aerobic exercise is 60% - 80% of the maximum heart rate, the frequency of strength training is 2 - 3 times per week, and the breathing training time is 10 - 15 minutes per day.

[0012] Preferably, data sharing and remote collaboration: Upload the three-dimensional ultrasound data and analysis results to the cloud platform to achieve data sharing and remote collaboration among doctors. The cloud platform supports multi-terminal access, including computers, tablets, and mobile phones, ensuring that doctors and patients can view the data and analysis results in real time. The data upload uses an encrypted transmission protocol to ensure data security.

[0013] Preferably, intelligent warning mechanism: When abnormal parameters are detected, automatically send alarms to doctors and patients and provide emergency treatment suggestions. Abnormal parameters include valve opening degree less than 12 mm, greater than 28 mm, blood flow velocity less than 0.4 m / s, greater than 1.6 m / s, left ventricular ejection fraction (LVEF) below 45%, and above 75%.

[0014] Preferably, patient education module: Through forms of pictures, texts, videos, and interactive Q&A, help patients understand the importance and specific measures of postoperative management. The education content includes postoperative diet suggestions, exercise guidance, and precautions for taking medications. The diet suggestion is that the daily sodium intake is less than 2 grams, and the exercise guidance is at least 150 minutes of moderate-intensity aerobic exercise per week.

[0015] Preferably, data standardization processing: Ensure that data between different medical institutions can be shared and compared. The standardization processing includes unified data format, unit conversion, and timestamp alignment.

[0016] Technical key points

[0017] High-precision three-dimensional ultrasound imaging: Use a three-dimensional ultrasound device with a resolution of not less than 0.5 mm, combined with a voxel-based surface reconstruction algorithm, to generate a high-precision three-dimensional model of the heart.

[0018] Automated parameter extraction: Based on the three-dimensional model, automatically extract cardiac function parameters, with measurement accuracies reaching ±0.1 mm (valve opening degree), ±0.01 m / s (blood flow velocity), ±1 ml (ventricular volume), and ±1% (LVEF).

[0019] Dynamic monitoring and trend analysis: Through regular three-dimensional ultrasound examinations, obtain dynamic change data of postoperative cardiac parameters, and use a linear regression model to predict trends and identify abnormal risks.

[0020] Personalized management plan: According to the parameter analysis results, formulate personalized drug treatment, rehabilitation training, and lifestyle adjustment plans to ensure the pertinence and effectiveness of postoperative management.

[0021] Intelligent warning and remote collaboration: Through the cloud platform, achieve data sharing and remote collaboration, combined with an intelligent warning mechanism, to timely detect abnormalities and provide treatment suggestions.

[0022] Adopting the technical solution of the present invention can bring the following remarkable technical effects:

[0023] 1. High-precision cardiac function assessment: The present invention uses a high-resolution three-dimensional ultrasound device (resolution not less than 0.5 mm) and a voxel-based surface reconstruction algorithm (reconstruction accuracy of ±0.1 mm) to generate a high-precision three-dimensional cardiac model, which can accurately locate the valve position and functional areas. The cardiac function parameters (valve opening and closing degree, blood flow velocity, ventricular volume, and left ventricular ejection fraction) extracted based on the three-dimensional model have extremely high measurement accuracy. The accuracy of the valve opening and closing degree is ±0.1 mm, and the accuracy of the blood flow velocity is ±0.01 m / s. This high-precision assessment can comprehensively and accurately reflect the postoperative cardiac function status, providing a scientific and reliable decision-making basis for doctors.

[0024] 2. Dynamic monitoring and early risk identification: The present invention obtains dynamic change data of postoperative cardiac parameters through regular three-dimensional ultrasound examinations (time intervals of 1 month, 3 months, and 6 months), and uses a linear regression model to analyze the parameter change trends. When the parameter value deviates from the normal value range by more than 20%, the system can automatically identify abnormal risks, such as the valve opening and closing degree being less than 12 mm, greater than 28 mm, the blood flow velocity being less than 0.4 m / s, greater than 1.6 m / s, the left ventricular ejection fraction (LVEF) being lower than 45% or higher than 75%. This dynamic monitoring and early risk identification mechanism can timely detect abnormal changes in postoperative cardiac function, providing doctors with the opportunity for early intervention and significantly reducing the incidence of postoperative complications.

[0025] 3. Personalized management and optimized rehabilitation effect: The present invention formulates a personalized postoperative management plan based on the parameter analysis results and combines with the patient's clinical situation. The drug treatment plan includes precise adjustment of anticoagulants (INR target range of 2.0 - 3.0), diuretics (dose range of 20 - 80 mg / day), and beta blockers (dose range of 25 - 100 mg / day); the rehabilitation training plan includes specific guidance on aerobic exercise (intensity of 60% - 80% of the maximum heart rate), strength training (frequency of 2 - 3 times per week), and breathing training (time of 10 - 15 minutes per day). This personalized management plan can meet the specific needs of patients, optimize the postoperative rehabilitation effect, improve the patient's quality of life, and reduce the waste of medical resources at the same time. Description of the Drawings

[0026] Figure 1 It is a flowchart of the method for managing cardiac parameters after cardiac valve surgery of the present invention Detailed Embodiments

[0027] The following will be combined with the attached drawings in the embodiments of the present invention Figure 1, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all 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.

[0028] Taking a patient who has undergone aortic valve replacement surgery as an example, the implementation steps and technical key points of the present invention are described in detail.

[0029] 1. Three-dimensional ultrasound data acquisition and modeling: Use a high-resolution three-dimensional ultrasound device (resolution not less than 0.5 mm) to acquire the stereoscopic image data of the patient's heart, including the cardiac chambers, aortic valve structure, and hemodynamic information. Generate a three-dimensional model of the heart through a voxel-based surface reconstruction algorithm, with a reconstruction accuracy of ±0.1 mm, and accurately locate the position and functional area of the aortic valve.

[0030] 2. Cardiac parameter extraction and analysis: Based on the three-dimensional model, extract cardiac function parameters, including aortic valve opening and closing degree, blood flow velocity, left ventricular volume, and left ventricular ejection fraction (LVEF). The measurement results show that the aortic valve opening and closing degree is 18 mm (the normal value range is 15 - 25 mm), the blood flow velocity is 1.2 m / s (the normal value range is 0.5 - 1.5 m / s), the left ventricular volume is 80 ml (the normal value range is 50 - 120 ml), and the left ventricular ejection fraction (LVEF) is 60% (the normal value range is 55% - 70%). Comparing with the preoperative data (aortic valve opening and closing degree is 10 mm, blood flow velocity is 0.8 m / s, left ventricular volume is 120 ml, LVEF is 45%), evaluate the surgical effect and the recovery of cardiac function. The results show that the surgery significantly improves the aortic valve function and left ventricular function.

[0031] 3. Dynamic monitoring and trend analysis: Perform three-dimensional ultrasound examinations at 1 month, 3 months, and 6 months after surgery to obtain the dynamic change data of the postoperative cardiac parameters. Use a linear regression model to analyze the parameter change trend and predict the change trend of the postoperative cardiac function. The results show that the aortic valve opening and closing degree, blood flow velocity, and left ventricular volume all tend to be stable, the left ventricular ejection fraction (LVEF) gradually increases, and no abnormal risks are found (the parameter values deviate from the normal value range by no more than 20%).

[0032] 4. Personalized Management Plan Formulation: Based on the parameter analysis results and combined with the patient's clinical situation, a personalized postoperative management plan is formulated. The drug treatment plan includes: the anticoagulant warfarin, with the dose adjusted according to the international normalized ratio (INR), and the target range is 2.0 - 3.0; the diuretic furosemide, with the dose of 40 mg / day; the beta-blocker metoprolol, with the dose of 50 mg / day. The rehabilitation training plan includes: aerobic exercises (fast walking, swimming), with the intensity being 60% - 80% of the maximum heart rate, 5 times a week, 30 minutes each time; strength training (dumbbell training), 2 times a week, 20 minutes each time; breathing training (deep breathing exercises), 10 minutes a day.

[0033] 5. Data Sharing and Remote Collaboration: The three-dimensional ultrasound data and analysis results are uploaded to the cloud platform to achieve data sharing and remote collaboration among doctors. Patients can view the data and analysis results in real time through the mobile terminal, and doctors can remotely adjust the treatment plan through the cloud platform.

[0034] 6. Intelligent Early Warning Mechanism: When abnormal parameters are detected, alarms are automatically sent to doctors and patients, and emergency treatment suggestions are provided. If the aortic valve opening and closing degree is less than 12 mm or greater than 28 mm, the system will immediately send an alarm and recommend that the patient undergo further examinations.

[0035] 7. Patient Education Module: Through pictures, texts, videos and interactive Q&A forms, it helps patients understand the importance and specific measures of postoperative management. The education content includes postoperative diet suggestions (sodium intake less than 2 grams per day), exercise guidance (at least 150 minutes of moderate-intensity aerobic exercise per week) and precautions for taking medications (taking time and precautions of warfarin).

[0036] The technical key points are as follows:

[0037] High-precision Three-dimensional Ultrasound Imaging: A three-dimensional ultrasound device with a resolution of not less than 0.5 mm is used, combined with a voxel-based surface reconstruction algorithm to generate a high-precision three-dimensional model of the heart.

[0038] Automated Parameter Extraction: Based on the three-dimensional model, cardiac function parameters are automatically extracted, and the measurement accuracy reaches ±0.1 mm (valve opening and closing degree), ±0.01 m / s (blood flow velocity), ±1 ml (ventricular volume) and ±1% (LVEF).

[0039] Dynamic Monitoring and Trend Analysis: Through regular three-dimensional ultrasound examinations, dynamic change data of postoperative cardiac parameters are obtained, and a linear regression model is used to predict trends and identify abnormal risks.

[0040] Personalized Management Plan: According to the parameter analysis results, personalized drug treatment, rehabilitation training and lifestyle adjustment plans are formulated to ensure the pertinence and effectiveness of postoperative management.

[0041] Intelligent early warning and remote collaboration: Through the cloud platform, data sharing and remote collaboration are realized. Combined with the intelligent early warning mechanism, anomalies can be detected in a timely manner and handling suggestions are provided.

[0042] Through three-dimensional ultrasound imaging technology, high-precision and comprehensive cardiac function data are provided to make up for the deficiencies of traditional two-dimensional ultrasound. Through regular three-dimensional ultrasound examinations, dynamic monitoring of postoperative cardiac function is achieved, and abnormal changes can be detected in a timely manner. According to the specific conditions of the patient, a personalized postoperative management plan is formulated to improve the management effect and the quality of life of the patient. Through the cloud platform and the intelligent early warning mechanism, data sharing and remote collaboration are realized, and the utilization efficiency of medical resources is improved.

[0043] The above has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for managing cardiac parameters after cardiac valve surgery based on a three-dimensional ultrasound model, characterized in that It includes the following steps: S1 Use a three-dimensional ultrasound device to collect three-dimensional image data of the patient's heart, including heart chambers, valve structures, and hemodynamic information; S2 Generate a three-dimensional model of the heart through a three-dimensional reconstruction algorithm to accurately locate the valve positions and functional regions; S3 Extract cardiac function parameters based on the three-dimensional model, including valve opening and closing degrees, blood flow velocities, ventricular volumes, left ventricular ejection fraction (LVEF), and stroke volume (SV); S4 Compare the parameter data before and after surgery to evaluate the surgical effect and the recovery of cardiac function; S5 Obtain dynamic change data of postoperative cardiac parameters through regular three-dimensional ultrasound examinations, analyze the parameter change trends, and identify abnormal risks; S6 According to the parameter analysis results and combined with the patient's clinical conditions, formulate a personalized postoperative management plan, including drug treatment, rehabilitation training, and lifestyle adjustments; S7 Upload the three-dimensional ultrasound data and analysis results to the cloud platform to achieve data sharing and remote collaboration among doctors.

2. The method according to claim 1, wherein In the three-dimensional ultrasound data acquisition and modeling steps (S1 and S2), the resolution of the three-dimensional ultrasound device is not less than 0.5 mm, and it can clearly display the microscopic structure of the heart valves. The three-dimensional reconstruction algorithm uses a voxel-based surface reconstruction method, and the reconstruction accuracy is ±0.1 mm.

3. The method according to claim 1, wherein In the cardiac parameter extraction and analysis step (S3), the measurement accuracy of the valve opening and closing degree is ±0.1 mm, and the normal value range is 15 - 25 mm; the measurement accuracy of the blood flow velocity is ±0.01 m / s, and the normal value range is 0.5 - 1.5 m / s; the measurement accuracy of the ventricular volume is ±1 ml, and the normal value range is 50 - 120 ml; the measurement accuracy of the left ventricular ejection fraction (LVEF) is ±1%, and the normal value range is 55% - 70%.

4. The method according to claim 1, wherein In the dynamic monitoring and trend analysis step (S5), the time intervals for regular three-dimensional ultrasound examinations are 1 month, 3 months, and 6 months. The specific frequency is adjusted according to the patient's postoperative recovery. Trend analysis uses a linear regression model to predict the change trend of postoperative cardiac function. The abnormal risk is defined as the parameter value deviating from the normal value range by more than 20%.

5. The method according to claim 1, characterized in that, In the personalized management plan formulation step (S6), the drug treatment plan includes dose adjustments of anticoagulants, diuretics, and β-blockers. The dose of anticoagulants is adjusted according to the international normalized ratio (INR), and the target range is 2.0 - 3.0; the dose of diuretics is adjusted according to the patient's weight and edema conditions, and the range is 20 - 80 mg / day; the dose of β-blockers is adjusted according to the patient's heart rate, and the range is 25 - 100 mg / day; the rehabilitation training plan includes specific guidance on aerobic exercise, strength training, and breathing training. The intensity of aerobic exercise is 60% - 80% of the maximum heart rate, the frequency of strength training is 2 - 3 times per week, and the breathing training time is 10 - 15 minutes per day.

6. The method according to claim 1, wherein In the data sharing and remote collaboration step (S7), the cloud platform supports multi-terminal access, including computers, tablets, and mobile phones, to ensure that doctors and patients can view the data and analysis results in real time. Data upload uses an encrypted transmission protocol to ensure data security.

7. The method according to claim 1, characterized in that The method further includes an intelligent early warning mechanism. When abnormal parameters are detected, it automatically sends alerts to doctors and patients and provides emergency treatment suggestions. The abnormal parameters include the valve opening degree being less than 12 mm, greater than 28 mm, the blood flow velocity being less than 0.4 m / s, greater than 1.6 m / s, the left ventricular ejection fraction (LVEF) being lower than 45%, and higher than 75%.

8. The method according to claim 1, wherein The method further includes a patient education module. Through pictures, texts, videos and interactive Q&A forms, it helps patients understand the importance and specific measures of postoperative management. The education content includes postoperative diet suggestions, exercise guidance and precautions for taking medications. The diet suggestion is that the daily sodium intake is less than 2 grams, and the exercise guidance is that there is at least 150 minutes of moderate-intensity aerobic exercise per week.

9. The method according to claim 1, wherein The method further includes data standardization processing to ensure that data between different medical institutions can be shared and compared. The standardization processing includes unifying data formats, unit conversion and timestamp alignment.

10. The method according to claim 1, characterized in that The method is applicable to all types of cardiac valve surgeries, including mitral valve repair, aortic valve replacement and tricuspid valve plasty. The specific parameter extraction and management plan are adjusted according to the type of surgery.

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