Heart segmental motion anomaly ultrasonic evaluation system and method

Abstract

The invention provides an ultrasonic evaluation system and method for heart segmental motion abnormality, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting a continuous multi-frame echocardiogram of a patient; carrying out heart structure segmentation on each echocardiogram to obtain a plurality of heart segments, and respectively tracking and extracting motion features of each heart segment in a heart cycle to form a motion feature time sequence of each heart stage; and calculating a difference value between the motion feature time sequence and a baseline feature of a pre-constructed normal heart motion mode, and when the difference value exceeds a preset threshold value, outputting an evaluation result that the heart segment is in the abnormal motion mode. The method has the advantages that diagnosis efficiency is improved, workload of doctors is relieved, personal errors are reduced, accuracy, consistency and objectivity of diagnosis are improved, final evaluation results can provide effective reference for the doctors, diagnosis ability of primary doctors is improved, and dependence on experience of the doctors is reduced.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an ultrasonic evaluation system and method for cardiac segmental motion abnormalities. Background Art

[0002] Ultrasonic segmental motion dysregulation refers to the observation during an echocardiogram that the movement of one or more specific areas (i.e., segments) of the heart is inconsistent with the normal heart movement pattern. This abnormal movement pattern usually manifests as weakened movement, loss of movement (i.e., no movement), or paradoxical movement (i.e., the heart muscle expands outward during contraction, which is opposite to the normal contraction direction). This abnormality often indicates impaired myocardial function and may be related to coronary artery disease (such as coronary heart disease), myocardial infarction, myocarditis, valvular heart disease, or other heart diseases. Echocardiography is the main means of diagnosing ultrasonic segmental motion dysregulation. Through echocardiography, the movement of various parts of the heart can be observed to determine whether segmental motion dysregulation exists.

[0003] Traditional echocardiography-based assessment of whether segmental motion abnormalities exist requires doctors to analyze ultrasound images frame by frame, which is a cumbersome and time-consuming process, especially when dealing with a large number of cases, and the diagnostic efficiency is low. In addition, echocardiography contains a large amount of dynamic data, and doctors need to extract useful information from it, which increases the workload, and a large number of repetitive manual operations can easily cause doctor fatigue, further reducing efficiency. This low efficiency not only affects the workflow, but may also delay the patient's treatment.

[0004] In addition, echocardiographic evaluation requires high experience and skills of doctors. Becoming a skilled echocardiographer requires long-term training and practice. Junior doctors lack experience and skills and may find it difficult to accurately judge complex cardiac dyskinesis, which affects the quality of diagnosis. Echocardiographic evaluation relies on the experience and judgment of doctors and is highly subjective. This subjectivity may lead to inconsistent diagnostic results for the same case by different doctors, affecting the accuracy and reliability of the diagnosis. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention provides an ultrasonic evaluation system for cardiac segmental motion abnormalities, comprising:

[0006] A data acquisition module, used for acquiring continuous multi-frame echocardiograms of the patient;

[0007] The data processing and analysis module is connected to the data acquisition module and includes:

[0008] A feature extraction submodule, for performing cardiac structure segmentation on each of the echocardiograms to obtain a plurality of cardiac segments, and for tracking and extracting the motion features of each of the cardiac segments in the cardiac cycle to form a motion feature time series of each of the cardiac stages;

[0009] The abnormality assessment submodule is connected to the feature extraction submodule and is used to calculate the difference between the motion feature time series and the baseline feature of the pre-constructed normal heart motion pattern, and when the difference exceeds a preset threshold, output an assessment result that the heart segment is in an abnormal motion pattern.

[0010] Preferably, the feature extraction submodule includes:

[0011] An image segmentation unit, configured to use a deep convolutional neural network to perform cardiac structure segmentation on each of the echocardiograms to obtain a plurality of cardiac segments;

[0012] A dynamic tracking unit is connected to the image segmentation unit and is used to track and extract the motion characteristics of each cardiac segment in the cardiac cycle respectively through an optical flow method or a temporal convolutional network.

[0013] Preferably, the abnormality assessment submodule includes:

[0014] An acquisition unit, used for acquiring a plurality of continuous frames of normal echocardiograms under a plurality of normal cardiac motion modes;

[0015] A preprocessing unit, connected to the acquisition unit, for performing denoising, standardization and frame alignment on each frame of the normal echocardiogram to obtain a processed echocardiogram;

[0016] a model building unit, connected to the preprocessing unit, for training a baseline model by using a self-supervised learning method, so that the baseline model learns the normal motion characteristics of each of the heart segments under the normal cardiac motion mode from the processed echocardiogram;

[0017] A baseline construction unit is connected to the model construction unit and is used to establish the baseline characteristics under the normal cardiac motion mode based on the normal operating characteristics.

[0018] Preferably, the baseline construction unit comprises:

[0019] An analysis subunit, configured to perform statistical analysis on each of the normal motion features to obtain a corresponding statistical value, wherein the statistical value includes a mean value and a standard deviation;

[0020] A construction subunit is connected to the analysis subunit and is used to construct a baseline range based on the statistical value as the baseline feature, wherein the lower limit of the baseline range is the mean value minus the standard deviation, and the upper limit of the baseline range is the mean value plus the standard deviation.

[0021] Preferably, the abnormality assessment submodule further includes an abnormality classification unit for inputting the motion feature time sequence corresponding to the heart segment in the abnormal motion mode into a segmental motion abnormality classification model to obtain the abnormality type of the heart segment.

[0022] Preferably, it also includes a user interaction interface connected to the data processing and analysis module, and the output of the segmental operational abnormality classification model is also connected to the gradient weighted class activation mapping block, which is used to generate a heat map of the key focus area and superimpose it on the corresponding echocardiogram, and then display it on the user interaction interface for the doctor to view.

[0023] Preferably, a training and support module is also included, which is connected to the data processing and analysis module and is used to provide doctors with artificial intelligence technology and echocardiography diagnosis courses and resources related to cardiac segmental motion abnormalities, as well as real-time technical support and consulting services.

[0024] Preferably, the data processing and analysis module is deployed on an ultrasound host, and the ultrasound host uses a solid-state hard disk to store the acquired continuous multiple frames of the ultrasound cardiogram, and uses a multi-core processor and an image processing unit to perform ultrasound assessment of cardiac segmental motion abnormalities on each of the ultrasound cardiograms.

[0025] The present invention also provides a method for ultrasonic evaluation of cardiac segmental motion abnormalities, which is applied to the above-mentioned ultrasonic evaluation system for cardiac segmental motion abnormalities, and comprises:

[0026] Step S1, the cardiac segmental motion abnormality ultrasound assessment system collects multiple frames of continuous echocardiograms of the patient;

[0027] Step S2, the cardiac segmental motion abnormality ultrasound assessment system performs cardiac structure segmentation on each of the echocardiograms to obtain a plurality of cardiac segments, and respectively tracks and extracts motion characteristics of each of the cardiac segments in the cardiac cycle to form a motion characteristic time series of each of the cardiac stages;

[0028] In step S3, the cardiac segmental motion abnormality ultrasound assessment system calculates the difference between the motion feature time series and the pre-constructed baseline feature of the normal cardiac motion pattern, and when the difference exceeds a preset threshold, outputs an assessment result that the cardiac segment is in an abnormal motion pattern.

[0029] Preferably, after executing step S3, the method further includes:

[0030] The motion feature time series corresponding to the heart segment in the abnormal motion mode is input into a segmental motion abnormality classification model to obtain the abnormal type of the heart segment.

[0031] The above technical solution has the following advantages or beneficial effects: by deploying a data processing and analysis module to automatically segment the cardiac structure of the collected continuous multi-frame echocardiograms and track the motion characteristics of the multiple cardiac segments obtained by segmentation, the abnormal motion patterns of the cardiac segments can be automatically identified, thereby improving the diagnostic efficiency, reducing the workload of doctors, reducing human errors, and improving the accuracy, consistency and objectivity of the diagnosis. The final evaluation results can provide effective reference for doctors, improve the diagnostic ability of junior doctors, and reduce dependence on doctors' experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a structural schematic diagram of a cardiac segmental motion abnormality ultrasound assessment system in a preferred embodiment of the present invention;

[0033] Figure 2 The figure is a flow chart of a method for ultrasonically evaluating cardiac segmental motion abnormalities in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0034] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The present invention is not limited to this embodiment, and other embodiments may also fall within the scope of the present invention as long as they conform to the gist of the present invention.

[0035] In a preferred embodiment of the present invention, based on the above problems existing in the prior art, a cardiac segmental motion abnormality ultrasound assessment system is provided. Figure 1 As shown, including:

[0036] The data acquisition module 100 is used to acquire continuous multi-frame echocardiograms of the patient;

[0037] The data processing and analysis module 200 is connected to the data acquisition module 100 and includes:

[0038] The feature extraction submodule 1 is used to segment the cardiac structure of each echocardiogram to obtain multiple cardiac segments, and to track and extract the motion characteristics of each cardiac segment in the cardiac cycle to form a motion characteristic time series of each cardiac stage;

[0039] The abnormality assessment submodule 2 is connected to the feature extraction submodule 1, and is used to calculate the difference between the motion feature time series and the baseline feature of the pre-constructed normal heart motion pattern, and when the difference value exceeds a preset threshold, output the assessment result that the heart segment is in an abnormal motion pattern.

[0040] Specifically, in this embodiment, the data acquisition module 100 is an ultrasound probe, which is connected to the ultrasound host by wired or wireless means. The data processing and analysis module 200 is deployed in the ultrasound host. The data processing and analysis module 200 performs cardiac structure segmentation, motion feature tracking, and abnormal motion pattern recognition on the ultrasound cardiogram based on the artificial intelligence model. Among them, the ultrasound host preferably uses a high-performance processor to improve the data processing speed and parallel computing capabilities, so as to ensure that the artificial intelligence model deployed therein can quickly process and analyze the ultrasound cardiogram and improve the overall performance of the system.

[0041] The high-performance processors include but are not limited to multi-core processors and graphics processing units (GPUs). In addition, to improve the storage capacity of the ultrasound host, the ultrasound host uses a large-capacity storage device to provide sufficient storage space and fast data reading and writing capabilities, thereby meeting the storage needs of a large number of echocardiograms and ensuring fast storage and retrieval of data. The large-capacity storage device includes but is not limited to a solid-state drive (SSD) or other high-performance storage devices.

[0042] Furthermore, when storing a large number of echocardiograms, it is preferred to use distributed storage systems and data compression technology to optimize data storage and reading speed. Data versioning management is implemented through data version control to ensure the traceability and consistency of data processing, thereby making the data processing process more efficient and reliable, providing a solid foundation for the training and deployment of AI models.

[0043] The above-mentioned distributed storage system includes cloud storage and data sharding. Cloud storage is to use the distributed storage solution of cloud service providers to achieve efficient storage and access of large-scale data. Data sharding is to store echocardiography data in shards to improve data reading and writing speed.

[0044] The above data compression technology preferably adopts lossless compression to reduce storage space while maintaining the image quality of the echocardiogram unchanged.

[0045] The above-mentioned data version control includes implementing a version control system to ensure the traceability of each data update, and ensuring data security and privacy protection through a permission management mechanism.

[0046] In addition, detailed metadata indexes can be created for each echocardiogram file during storage to support fast retrieval. At the same time, full-text retrieval is retained to implement content-based retrieval, making it easy to quickly locate images with specific features.

[0047] To prevent data loss, it also includes automated regular backup strategies to prevent data loss, while establishing a rapid recovery mechanism to ensure rapid recovery when data is damaged.

[0048] Through the above technologies, the storage and management of echocardiograms become more efficient, safer, and can quickly respond to clinical needs.

[0049] Before automatically analyzing multiple consecutive frames of echocardiograms, the echocardiograms are also preprocessed to ensure that the segmental motion of the heart can be accurately identified and analyzed later. Among them, the above preprocessing can use deep learning methods (such as GANs) to enhance the quality of ultrasound images and improve the clarity of edges and details. The above preprocessing also includes data cleaning and denoising, specifically including the use of adaptive filters (such as mean filters or median filters or Gaussian filters) to remove noise from ultrasound images, improve the signal-to-noise ratio, while retaining important image details, and overcome the noise problem inherent in ultrasound images. In the training stage of the artificial intelligence model, the above preprocessing also includes data enhancement, including but not limited to using generative adversarial networks to generate diverse training samples, enhance the generalization ability of the model, and perform data enhancement in time and space dimensions to simulate different cardiac cycles and perspectives.

[0050] After the echocardiogram is preprocessed, it is first necessary to identify the cardiac segments and motion features of the cardiac segments in the echocardiogram based on the feature extraction submodule 1. Specifically, in a preferred embodiment of the present invention, the feature extraction submodule 1 includes:

[0051] An image segmentation unit 11 is used to perform cardiac structure segmentation on each echocardiogram using a deep convolutional neural network to obtain a plurality of cardiac segments;

[0052] The dynamic tracking unit 12 is connected to the image segmentation unit 11 and is used to track and extract the motion characteristics of each heart segment in the cardiac cycle respectively through an optical flow method or a temporal convolutional network.

[0053] Specifically, in this embodiment, the artificial intelligence module includes a deep convolutional neural network model for segmenting cardiac structures and a temporal convolutional network for dynamically tracking the motion characteristics of cardiac segments. The deep convolutional neural network can be a U-net model, and the corresponding cardiac segments include but are not limited to ventricles, atria, and myocardium.

[0054] Furthermore, after segmenting multiple heart segments, the optical flow method can be used to track the movement of the heart segments in consecutive frames, or the temporal convolutional network (TCN) can be applied to model the time series data formed by multiple consecutive echocardiogram frames to identify the dynamic changes of each heart segment.

[0055] In a preferred embodiment of the present invention, the abnormality assessment submodule 2 includes:

[0056] An acquisition unit 21 is used to acquire a plurality of continuous frames of normal echocardiograms in a normal cardiac motion mode;

[0057] The preprocessing unit 22 is connected to the acquisition unit 21 and is used for performing denoising, standardization and frame alignment on each frame of the normal echocardiogram to obtain a processed echocardiogram;

[0058] A model building unit 23, connected to the preprocessing unit 22, is used to train the baseline model using a self-supervised learning method, so that the baseline model learns the normal motion characteristics of each heart segment under the normal heart motion mode from the processed echocardiogram;

[0059] The baseline construction unit 24 is connected to the model construction unit 23 and is used to establish the baseline characteristics under the normal cardiac motion mode based on the normal operating characteristics.

[0060] Specifically, in this embodiment, a large number of continuous multi-frame normal echocardiograms in normal cardiac motion mode are preferably collected from multiple reliable data sources (such as hospitals, research institutions, etc.) to ensure data diversity, including data of people of different ages, genders, weights, etc. Then, the collected continuous multi-frame normal echocardiograms are preprocessed, wherein standardization in the preprocessing includes but is not limited to adjusting the image size, resolution, etc., to ensure data consistency.

[0061] The self-supervised learning methods mentioned above include but are not limited to contrastive learning, masked autoencoders, etc., with the goal of allowing the baseline model to learn the normal motion features of the normal cardiac motion pattern. The normal motion features here include but are not limited to:

[0062] 1) Time domain features: including but not limited to amplitude features and time domain characteristics, among which amplitude features such as R peak amplitude, P wave amplitude, T wave amplitude, etc., these features can reflect the intensity of cardiac electrical activity. Time domain features: such as RR interval, QT interval, PR interval, etc., these features can reflect cardiac rhythm and conduction time.

[0063] 2) Frequency domain features: including but not limited to power spectrum density and frequency band energy. The power spectrum density is calculated by converting the time domain signal into the frequency domain signal through Fourier transform to reflect the frequency distribution of cardiac activity. The frequency band energy is to divide the frequency domain signal into different frequency bands and calculate the energy of each frequency band to reflect the activity of the heart at different frequencies.

[0064] 3) Morphological features: including but not limited to waveform features and waveform recognition, among which waveform features: such as QRS complex width, T wave width, P wave morphology, etc., these features can reflect the morphological changes of cardiac depolarization and repolarization. Waveform recognition is to use algorithms (such as Pan-Tompkins algorithm) to identify QRS complexes and other typical waveforms, laying the foundation for subsequent feature extraction.

[0065] After learning the normal motion features of each heart segment under the normal heart motion mode, the baseline features are established as the reference standard of the normal motion mode of the heart segment by the baseline construction unit 24. In a preferred embodiment of the present invention, the baseline construction unit 24 includes:

[0066] An analysis subunit 241 is used to perform statistical analysis on each normal motion feature to obtain corresponding statistical values, where the statistical values ​​include a mean value and a standard deviation;

[0067] The construction subunit 242 is connected to the analysis subunit 241 and is used to construct a baseline range based on the statistical value as a baseline feature, wherein the lower limit of the baseline range is the mean value minus the standard deviation, and the upper limit of the baseline range is the mean value plus the standard deviation.

[0068] Specifically, in this embodiment, after the baseline features are constructed, it is preferred to verify the baseline features using normal heart segment motion pattern data that is not involved in the construction, to ensure that the baseline features can accurately reflect the characteristics of the normal motion pattern of the heart segments and have certain robustness and generalization capabilities.

[0069] Furthermore, the performance of the baseline features is evaluated, including indicators such as accuracy and stability. If the performance of the baseline features is found to be poor, the baseline features need to be optimized. Possible optimization methods include adjusting the feature extraction method, increasing the number of features, adjusting the baseline range, etc.

[0070] Specifically, taking the amplitude as an example of normal motion feature, the process of constructing the baseline feature is explained:

[0071] A coordinate system can be constructed on a two-dimensional plane, where the horizontal axis represents the amplitude and the vertical axis represents the specific value of the amplitude. Then, a distribution diagram of the amplitude characteristics of the normal motion mode of the heart segment is drawn on this two-dimensional plane, and then the mean and standard deviation of the amplitude characteristics are calculated. Finally, a baseline is drawn with the mean as the baseline and the standard deviation as the range, that is, the baseline range is obtained. When the characteristics of the new echocardiogram fall within this baseline range, it is considered normal, otherwise it is considered abnormal.

[0072] Whether the features of the new echocardiogram fall within this baseline range requires comparing the features of the new echocardiogram with the baseline range. For the new echocardiogram, the corresponding motion features need to be extracted first and then compared. The comparison here can be a single-dimensional comparison, such as comparing each dimension of the motion feature with the corresponding value of the baseline range. If a dimension value exceeds the range of the baseline, the dimension may be considered abnormal. The comparison here can be a multi-dimensional comprehensive evaluation, such as a comprehensive evaluation of motion features in multiple dimensions. For example, the Euclidean distance, Mahalanobis distance, etc. between the motion feature and the baseline can be calculated, and then the abnormality can be judged based on the size of the distance.

[0073] In a preferred embodiment of the present invention, the abnormality assessment submodule 2 further includes an abnormality classification unit 25 for inputting the motion feature time series corresponding to the heart segment in the abnormal motion mode into the segmental motion abnormality classification model to obtain the abnormal type of the heart segment.

[0074] Specifically, in this embodiment, the above-mentioned abnormal types include but are not limited to low perfusion, decreased movement, etc. The above-mentioned segmental motion abnormality classification model is preferably constructed by reinforcement learning method and ensemble learning method. Further specifically, normal and abnormal segmental motion data are collected, and the corresponding abnormal type is annotated for the motion feature time series corresponding to each cardiac segment, providing training labels for supervised learning and reinforcement learning. It also includes designing a reward function so that the segmental motion abnormality classification model obtains positive rewards when correctly classified and negative rewards when misclassified. The weighted sum of indicators such as precision and recall rate can be introduced as part of the reward. A neural network (such as a convolutional neural network, a recurrent neural network or a combination thereof) is used as a policy network. Reinforcement learning algorithms such as Q-learning, deep Q network (DQN), and policy gradient methods (such as REINFORCE) are used for training.

[0075] Furthermore, the final model is determined by an integrated learning method, including first selecting multiple different types of classifiers as basic models, such as support vector machines, random forests, neural networks, etc. Then, each basic model is trained separately using the preprocessed data. Then, the outputs of multiple basic models are combined into the final classification result using integration strategies such as voting, weighted averaging, bagging, and boosting. Finally, the performance of the integrated model is evaluated by cross-validation and other methods to ensure that it is better than a single basic model, and finally the above-mentioned segmental movement abnormality classification model is obtained.

[0076] In a preferred embodiment of the present invention, it also includes a user interaction interface 300, which is connected to the data processing and analysis module 200. The output of the segmental operational abnormality classification model is also connected to the gradient weighted class activation mapping block, which is used to generate a key focus area heat map and superimpose it on the corresponding echocardiogram, and then display it on the user interaction interface 300 for the doctor to view.

[0077] Specifically, in this embodiment, a gradient weighted class activation mapping block (Grad-CAM module) is added to the final segmental abnormal operation classification model to calculate the gradient of the target category relative to the last convolutional layer, and the gradient information is used to generate a heat map of the focus area, showing the area that the segmental abnormal operation classification model pays most attention to when making decisions. By superimposing the heat map on the original echocardiogram, it provides doctors with an intuitive and visual basis for model decision-making, enhancing clinicians' trust and understanding of AI decisions.

[0078] Among them, the user interaction interface 300 can be divided into an echocardiogram display area, which is used to display real-time or replayed echocardiogram images for doctors to observe and diagnose; a processing and analysis result display area, which is used to display the results of the artificial intelligence model analysis, including but not limited to whether the heart segment is in an abnormal motion mode, focusing on regional heat maps and related numerical indicators to help doctors quickly understand the patient's condition; an interactive function area, which is used to provide doctors with interactive functions between the system, such as labeling, feedback and adjustment of analysis parameters. Doctors can use this area to label, input diagnostic opinions, adjust analysis parameters, and view historical records. Among them, by the doctor labeling and feedback on the results of the artificial intelligence model analysis, the corresponding artificial intelligence model can be further optimized and improved, thereby continuously improving the accuracy and reliability of the diagnosis.

[0079] In a preferred embodiment of the present invention, a training and support module 400 is also included, which is connected to the data processing and analysis module 200 and is used to provide doctors with artificial intelligence technology and echocardiography diagnosis courses and resources related to cardiac segmental motion abnormalities, as well as provide real-time technical support and consulting services.

[0080] Specifically, in this embodiment, by providing a training and support module 400, providing doctors with artificial intelligence technology and echocardiography diagnosis courses and resources related to cardiac segmental dyskinesis, it can help doctors to continuously learn and master the latest echocardiography diagnostic technology and methods, improve professional skills, and enable doctors in grassroots hospitals to use AI technology for high-level diagnosis, thereby reducing the uneven distribution of medical resources. By providing real-time technical support and consulting services, it can help doctors solve problems encountered during use, ensure that doctors can smoothly use the cardiac segmental dyskinesis ultrasound evaluation system of the present invention, improve diagnostic efficiency and quality, and patients can obtain accurate diagnosis results earlier, reduce unnecessary examinations and treatments, and reduce medical expenses.

[0081] The present invention also provides a method for ultrasonically evaluating cardiac segmental motion abnormalities, which is applied to the above-mentioned ultrasonic evaluation system for cardiac segmental motion abnormalities, such as Figure 2 As shown, including:

[0082] Step S1, the cardiac segmental motion abnormality ultrasound assessment system collects multiple frames of continuous echocardiograms of the patient;

[0083] Step S2, the cardiac segmental motion abnormality ultrasound assessment system performs cardiac structure segmentation on each echocardiogram to obtain multiple cardiac segments, and respectively tracks and extracts the motion characteristics of each cardiac segment in the cardiac cycle to form a motion characteristic time series of each cardiac stage;

[0084] Step S3, the cardiac segmental motion abnormality ultrasound assessment system calculates the difference between the motion feature time series and the pre-constructed baseline feature of the normal cardiac motion pattern, and when the difference exceeds a preset threshold, outputs an assessment result that the cardiac segment is in an abnormal motion pattern.

[0085] In a preferred embodiment of the present invention, after executing step S3, the method further includes:

[0086] The motion feature time series corresponding to the heart segment in abnormal motion mode is input into the segmental motion abnormality classification model to obtain the abnormal type of the heart segment.

[0087] In summary, the ultrasonic assessment system and method for cardiac segmental motion abnormalities of the present invention have the following beneficial effects:

[0088] 1. Social Effect

[0089] 1.1 Improve diagnostic accuracy

[0090] Reduce misdiagnosis rate: By introducing AI automatic analysis, misdiagnosis and missed diagnosis due to human factors are reduced, and the accuracy of diagnosis is improved. Studies have shown that AI-assisted diagnosis systems can reduce the misdiagnosis rate by about 30%.

[0091] Improve diagnostic efficiency: The ability to complete echocardiogram analysis within seconds greatly shortens the diagnosis time, allowing doctors to diagnose more patients in a shorter time.

[0092] 1.2 Improve the level of medical services

[0093] Popularize high-quality medical resources: Through training and support modules, doctors in grassroots hospitals can also use AI technology to perform high-level diagnoses, thereby narrowing the imbalance in the distribution of medical resources.

[0094] Reduce the burden on patients: Due to the improvement of diagnostic efficiency and accuracy, patients can obtain accurate diagnosis results earlier, reduce unnecessary examinations and treatments, and reduce medical costs.

[0095] 2. Economic Effect

[0096] 2.1 Reduce medical costs

[0097] Reduce duplicate examinations: The high accuracy of the AI ​​analysis system reduces misdiagnosis and missed diagnosis, and reduces the cost of repeated examinations and treatments for patients due to misdiagnosis. It is expected to save hospitals and patients about 15% of related costs each year.

[0098] Improve equipment utilization: By optimizing hardware and data processing processes, the utilization rate of ultrasound equipment is improved, and equipment idle time and maintenance costs are reduced.

[0099] 2.2 Improve hospital efficiency

[0100] Increased number of diagnoses: Due to improved diagnostic efficiency, hospitals can serve more patients in the same amount of time, thereby increasing revenue. It is expected that the number of diagnoses can be increased by about 20%.

[0101] Attract more patients: Highly accurate and efficient diagnostic services will attract more patients to choose the hospital for diagnosis and treatment, and enhance the hospital's market competitiveness.

[0102] 3. Technical Effect

[0103] 3.1 Improve data processing capabilities

[0104] Efficient data processing: Through optimized data processing flow, the system is able to process large amounts of data in a short time, ensuring real-time analysis and diagnosis. The processing speed is increased by about 50%.

[0105] Data storage optimization: The improved data storage module can efficiently store and manage echocardiographic data and support the storage and rapid retrieval of large-scale data.

[0106] 3.2 Enhance system stability

[0107] Hardware upgrade: Through hardware upgrade, the system's operating speed and stability have been significantly improved, reducing system crashes and analysis delays.

[0108] Software optimization: Optimized software architecture and algorithms improve system reliability and maintainability and reduce system failure rate.

[0109] 3.3 Improve user experience

[0110] Intuitive user interface: The optimized user interface design is more intuitive and easier to operate, reducing the learning and adaptation time for doctors.

[0111] Intelligent interactive functions: Provide intelligent interactive functions such as automatic labeling and diagnosis suggestions, so that doctors can make diagnoses and decisions more conveniently.

[0112] The specific data examples are as follows:

[0113] Improved diagnostic accuracy: The AI-assisted diagnosis system reduces the misdiagnosis rate by about 30% and increases the diagnostic accuracy to more than 95%.

[0114] Improved diagnostic efficiency: The AI ​​analysis module reduces the diagnostic time from an average of 30 minutes to less than 5 minutes.

[0115] Economic benefits: It is expected to save hospitals and patients about 15% of related expenses each year, increase the number of diagnoses by about 20%, and increase hospital revenue by about 10%.

[0116] Data processing speed: Data processing speed has been increased by approximately 50%, enabling analysis of echocardiograms to be completed in seconds.

[0117] System stability: The system failure rate is reduced by about 40%, and the stability of hardware and software is significantly improved.

[0118] Through the above technical means and optimization measures, the present invention significantly improves the diagnostic accuracy and efficiency of the echocardiography evaluation system, reduces subjectivity and dependence on physician experience, solves the challenges of data processing and storage, promotes the development of cardiac segmental movement abnormality diagnosis technology, and brings significant social, economic and technical effects.

[0119] The above description is only a preferred embodiment of the present invention, and does not limit the implementation mode and protection scope of the present invention. For those skilled in the art, it should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the contents of this specification and illustrations should be included in the protection scope of the present invention.

Claims

1. A cardiac segmental motion abnormality ultrasound assessment system, characterized in that: include: A data acquisition module, used for acquiring continuous multi-frame echocardiograms of the patient; The data processing and analysis module is connected to the data acquisition module and includes: A feature extraction submodule, for performing cardiac structure segmentation on each of the echocardiograms to obtain a plurality of cardiac segments, and for tracking and extracting the motion features of each of the cardiac segments in the cardiac cycle to form a motion feature time series of each of the cardiac stages; The abnormality assessment submodule is connected to the feature extraction submodule and is used to calculate the difference between the motion feature time series and the baseline feature of the pre-constructed normal heart motion pattern, and when the difference exceeds a preset threshold, output an assessment result that the heart segment is in an abnormal motion pattern.

2. The ultrasonic evaluation system for cardiac segmental motion abnormalities according to claim 1, characterized in that: The feature extraction submodule includes: An image segmentation unit, configured to use a deep convolutional neural network to perform cardiac structure segmentation on each of the echocardiograms to obtain a plurality of cardiac segments; A dynamic tracking unit is connected to the image segmentation unit and is used to track and extract the motion characteristics of each cardiac segment in the cardiac cycle respectively through an optical flow method or a temporal convolutional network.

3. The ultrasonic evaluation system for cardiac segmental motion abnormalities according to claim 1, characterized in that: The abnormality assessment submodule includes: An acquisition unit, used for acquiring a plurality of continuous frames of normal echocardiograms under a plurality of normal cardiac motion modes; A preprocessing unit, connected to the acquisition unit, for performing denoising, standardization and frame alignment on each frame of the normal echocardiogram to obtain a processed echocardiogram; a model building unit, connected to the preprocessing unit, for training a baseline model by using a self-supervised learning method, so that the baseline model learns the normal motion characteristics of each of the heart segments under the normal cardiac motion mode from the processed echocardiogram; A baseline construction unit is connected to the model construction unit and is used to establish the baseline characteristics under the normal cardiac motion mode based on the normal operating characteristics.

4. The ultrasonic evaluation system for cardiac segmental motion abnormalities according to claim 3, characterized in that: The baseline construction unit includes: An analysis subunit, configured to perform statistical analysis on each of the normal motion features to obtain a corresponding statistical value, wherein the statistical value includes a mean value and a standard deviation; A construction subunit is connected to the analysis subunit and is used to construct a baseline range based on the statistical value as the baseline feature, wherein the lower limit of the baseline range is the mean value minus the standard deviation, and the upper limit of the baseline range is the mean value plus the standard deviation.

5. The ultrasonic evaluation system for cardiac segmental motion abnormalities according to claim 1, characterized in that: The abnormality assessment submodule further includes an abnormality classification unit, which is used to input the motion feature time sequence corresponding to the heart segment in the abnormal motion mode into a segmental motion abnormality classification model to obtain the abnormal type of the heart segment.

6. The ultrasonic evaluation system for cardiac segmental motion abnormalities according to claim 5, characterized in that: It also includes a user interaction interface connected to the data processing and analysis module. The output of the segmental operational abnormality classification model is also connected to a gradient weighted class activation mapping block for generating a heat map of the key focus area and superimposing it on the corresponding echocardiogram, and then displaying it on the user interaction interface for doctors to view.

7. The ultrasonic evaluation system for cardiac segmental motion abnormalities according to claim 1, characterized in that: It also includes a training and support module, which is connected to the data processing and analysis module and is used to provide doctors with artificial intelligence technology and echocardiography diagnosis courses and resources related to cardiac segmental motion abnormalities, as well as provide real-time technical support and consulting services.

8. The ultrasonic evaluation system for cardiac segmental motion abnormalities according to claim 1, characterized in that: The data processing and analysis module is deployed on an ultrasound host, which uses a solid-state hard disk to store the acquired continuous multiple frames of the ultrasound cardiogram, and uses a multi-core processor and an image processing unit to perform ultrasound evaluation of cardiac segmental motion abnormalities on each of the ultrasound cardiograms.

9. A method for ultrasonic assessment of cardiac segmental motion abnormalities, characterized in that: The ultrasonic evaluation system for cardiac segmental motion abnormality as claimed in any one of claims 1 to 8 comprises: Step S1, the cardiac segmental motion abnormality ultrasound assessment system collects multiple frames of continuous echocardiograms of the patient; Step S2, the cardiac segmental motion abnormality ultrasound assessment system performs cardiac structure segmentation on each of the echocardiograms to obtain a plurality of cardiac segments, and respectively tracks and extracts motion characteristics of each of the cardiac segments in the cardiac cycle to form a motion characteristic time series of each of the cardiac stages; In step S3, the cardiac segmental motion abnormality ultrasound assessment system calculates the difference between the motion feature time series and the pre-constructed baseline feature of the normal cardiac motion pattern, and when the difference exceeds a preset threshold, outputs an assessment result that the cardiac segment is in an abnormal motion pattern.

10. The ultrasonic evaluation method for cardiac segmental motion abnormalities according to claim 9, characterized in that: After executing step S3, the method further includes: The motion feature time series corresponding to the heart segment in the abnormal motion mode is input into a segmental motion abnormality classification model to obtain the abnormal type of the heart segment.

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