98 results about "Heart structure" patented technology

The invention discloses a hypertensive heart disease early screening system fusing ultrasonic multiple parameters, which comprises the following steps: acquiring ultrasonic image data and physiological index monitoring parameters of a patient at different time points, performing difference comparison on heart structure parameters to generate time sequence change data, and establishing a multi-dimensional data fusion scheme based on the time sequence change data and physiological indexes; according to the fusion scheme, acute risk trigger factors are identified, blood flow abnormal parameters are generated, and emergency threshold comparison is carried out to generate acute risk early warning signals; extracting a myocardial strain degradation rate to determine a chronic degradation acceleration time point, carrying out disease progress stage association to generate a chronic risk layering curve, and establishing a clinical index comparison table; carrying out double-layer correlation on the acute risk early warning signal and the chronic risk layering curve, and carrying out calibration judgment to generate a comprehensive risk judgment sequence; the sequence is decomposed into an acute judgment layer and a chronic judgment layer, a risk threshold value is configured to generate an instant early warning signal, a layered screening report is formed, and technical support is provided for early discovery of the hypertensive heart disease.

The invention provides a left heart data analysis method and system based on image segmentation, and relates to the technical field of medical image processing. Comprising the following steps: acquiring cardiac ultrasound image data and preprocessing; performing image segmentation on the preprocessed image data to obtain a segmentation result including a left ventricle, a left ventricular wall and a left atrium; searching a boundary line of the left ventricle and the left atrium, selecting two boundary points corresponding to the minimum and maximum abscissas in the boundary line as left and right mitral valve ring points respectively, and establishing a long axis based on the mitral valve ring midpoint and the cardiac apex point; modeling the ventricle into a structure formed by superposing a plurality of discs along the long axis direction, and obtaining the diameter of the corresponding disc; based on the obtained diameters of the corresponding discs, the left ventricular volumes in the diastolic period and the systolic period are calculated respectively, and the left ventricular ejection fraction is calculated. According to the method, image segmentation and geometric modeling are fused to achieve accurate calculation of the cardiac ejection fraction, meanwhile, the accuracy of cardiac structure image segmentation is improved, and an interpretable ejection fraction estimation process is achieved.

The invention discloses an echocardiogram myocardial motion estimation method and system based on a structure perception diffusion model, and relates to the technical field of ultrasonic video data processing. The invention aims to solve the problem of myocardial motion estimation in an ultrasonic video. According to the technical key points, the method comprises the following steps: (1) preprocessing collected echocardiogram video data; (2) establishing an echocardiogram myocardial motion estimation model; (3) constructing a diffusion model motion estimation module of structure perception; (4) constructing a spatial adaptive up-sampling module; (5) training the network by using the existing data to obtain a network model; and (6) estimating the myocardial movement of the echocardiogram video by using the trained model. According to the method, the cardiac structure information and the multi-scale correlation contained in the echocardiogram can be fully utilized to automatically estimate the cardiac muscle movement of the echocardiogram, and the accuracy and the robustness of the algorithm are effectively improved.

The invention relates to a myocardial resection area determination method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a heart scanning image, and determining a heart three-dimensional model according to the heart scanning image; determining a heart short-axis section and a heart long-axis section according to the heart three-dimensional model; the clock direction angle of the scalpel is determined on the short axis section of the heart; based on the clock direction angle, the excision depth of the scalpel is determined on the long-axis section of the heart; and performing myocardial resection on the heart three-dimensional model according to the clock direction angle and the resection depth through a scalpel model, determining an updated three-dimensional model after myocardial resection, and marking a resection area on the updated three-dimensional model. According to the scheme, accurate preoperative planning guidance and comprehensive heart structure information presentation are provided for the myocardial rotary cutting operation, and therefore the accuracy and safety of the myocardial rotary cutting operation are improved.

The embodiment of the invention relates to a processing method and device for predicting ejection fraction retention type heart failure. The method comprises the steps that a deep learning model used for conducting dichotomy prediction on the ejection fraction retention type heart failure is constructed and recorded as a first prediction model; constructing a model data set and recording the model data set as a first data set; training the first prediction model based on the first data set; after model training is finished, heart structure feature analysis, heart hemodynamic feature analysis and left ventricle vortex time sequence feature analysis are conducted on four-dimensional blood flow heart magnetic resonance imaging data input by a user to obtain a corresponding structure feature set, and the hemodynamic feature set and the vortex time sequence feature form a corresponding first feature data set; and inputting the first feature data set into a first prediction model for prediction to obtain a corresponding first classification vector which is fed back to the current user. According to the invention, the prediction accuracy and the prediction efficiency can be improved.

Planning cardiac surgery is a complex task. Current heart models are mostly static and provide no functional information or personalization apart from structural information from imaging modalities like MRI, CT etc. The embodiments of present disclosure addresses unresolved problem of generating a personalized four dimensional cardiac model that integrates blood flow velocity, pressure profile and volume on three-dimensional cardiac structure, providing a comprehensive tool for pre or post-surgical planning and analysis. Unlike existing techniques, which often rely on separate assessments of cardiac anatomy and hemodynamics, using invasive methods, the present disclosure provides a 4D cardiac model that captures the person specific dynamic interaction between cardiac structures and blood flow throughout the cardiac cycle. Further, the 4D cardiac model enables clinicians to visualize and quantify complex flow patterns and pressure distributions within the region of interest in the heart, facilitating precise surgical planning and outcome prediction especially in pediatric heart surgery.

The invention provides a method for three-dimensional visualization of heart scars, a control device and a storage medium, and belongs to the technical field of three-dimensional heart modeling and visual display. The method comprises the following steps: matching a heart structure obtained by segmenting a pre-trained nnU-Net and a binary tag graph corresponding to a scar tissue with a myocardial shape shown by the binary tag graph of the heart structure by using a standard myocardial grid model as a template; and fitting deformation parameters of a standard myocardial grid model by using a deep learning regression model, and applying the deformation parameters to a standard grid to complete grid deformation so as to fit individual myocardial morphology. The heart shape prior is effectively utilized based on the template deformation method, so that the reconstructed three-dimensional model has higher anatomical consistency and fineness. And superposing the fitted three-dimensional myocardial grid model and the reconstructed scar tissue grid under the same coordinate for registration and superposition to realize image three-dimensional transparent myocardial scar visualization so as to accurately restore a continuous three-dimensional scar surface.

The invention relates to the technical field of medical image processing, in particular to a fetal echocardiogram heart structure segmentation model training and segmentation method, and the method comprises the steps: obtaining four-cavity view images of a plurality of fetal echocardiograms, and marking the four-cavity view images; generating a training data set according to the labeled four-cavity view image and the correspondingly segmented mask image; the training data set is utilized to train a heart structure segmentation model, the heart structure segmentation model outputs a heart structure segmentation result, the heart structure segmentation model comprises an image encoder, a prompt encoder and a mask decoder, a global attention module of the image encoder comprises an adapter, and a global attention module of the image encoder comprises a mask decoder; the image encoder and the mask decoder are connected through a cross attention module. Therefore, the problems of poor heart structure segmentation accuracy and the like in related technologies are solved.

The embodiment of the invention relates to the field of multivariable data processing, in particular to a processing method and a processing system for multi-dimensional cardiac structure function data. According to the method, the data of the dynamic electrocardiogram, the photoplethysmography and the seismocardiogram are subjected to noise reduction processing through the feedforward neural network, the data of the dynamic electrocardiogram, the photoplethysmography and the seismocardiogram after noise reduction are obtained, and the credibility of the data is higher; fusing the dynamic electrocardiogram, the photoplethysmography and the seismocardiogram of different time sequence segments, and determining characteristic parameters; combining and splicing the respectively determined characteristic parameters according to a time sequence to obtain a multi-parameter matrix; and processing the multi-parameter matrix by using a multi-modal fusion network to obtain a processed multi-dimensional vector, mutually correcting a plurality of parameters through processing, and splicing the plurality of parameters to obtain a multi-dimensional vector to obtain a high-credibility parameter which better conforms to a real physiological law.

The invention discloses an ultrasonic image automatic quantitative evaluation method and system based on artificial intelligence, and relates to the technical field of medical image processing. And respectively acquiring a modal category of the ultrasonic image, a detection frame of each target structure, a structure type and / or a sampling line position and a detection frame of each cardiac cycle through the depth image classification model and the target detection model. And the section type of the ultrasonic image is accurately judged according to the data. According to the method, in combination with the modal category and section type of the ultrasonic image and the image type contained in the ultrasonic image, an appropriate quantitative analysis mode can be accurately provided for different ultrasonic sections, and a quantitative analysis result is visualized, so that a doctor can observe the result conveniently. According to the invention, comprehensive analysis of the multi-modal section can be realized, and more comprehensive and deep analysis and evaluation of the heart structure or function can be realized.

The invention relates to the technical field of image processing, in particular to a cardiac ultrasound examination auxiliary system based on artificial intelligence, comprising: an image processing module comprising an acquisition unit for acquiring cardiac ultrasound dynamic images by using an ultrasound probe; the model training module is connected with the image processing module and comprises a model generation unit which is used for training an initial model according to the optimized image so as to output a convolutional neural network model; the feature extraction module is connected with the model training module and comprises a feature extraction unit which is used for extracting key features of the heart structure through a convolutional neural network model so as to output heart ultrasonic dynamic image features; and the control module is respectively connected with the image processing module, the model training module and the feature extraction module and is used for determining the pulse repetition frequency of the ultrasonic probe according to the artifact occurrence rate of the cardiac ultrasonic dynamic image. According to the invention, the processing stability of the cardiac ultrasonic dynamic image is improved.

The invention relates to the technical field of medical image processing and deep learning crossing, and discloses a congenital heart disease whole heart segmentation method based on text guidance. According to the method, based on a U-Net architecture, a text-image alignment module is added between different levels of jump connections, and semantic information in report texts and CT image features are subjected to feature semantic alignment and fusion layer by layer; a multi-scale image feature fusion module is added between adjacent levels of the encoder, and the feature extraction capability of the encoder on each substructure is improved through mask adaptive fusion weight; a text-driven multi-level segmentation supervision module is added to a decoder part, and a segmentation result is guided and optimized by using semantic features, so that a constructed deep learning network effectively understands heterogeneity features of a congenital heart disease structure, and the structure distinguishing capability of a fuzzy boundary is enhanced; automatic segmentation of the congenital heart disease whole heart structure can be achieved, and effectiveness and accuracy of congenital heart disease whole heart segmentation are improved.

The invention discloses a heart CT image segmentation method and system based on ultrasonic guidance, and relates to the field of image processing, and the method comprises an uploading module which is used for uploading a heart CT original image and an ultrasonic image of a corresponding part, and synchronously building the spatial position correlation mapping of the heart CT original image and the ultrasonic image; the preparation module is used for performing denoising and gray normalization processing on the heart CT original image and the ultrasonic image respectively, and completing spatial alignment of the two types of images based on spatial position correlation mapping; according to the method, spatial position correlation mapping of the heart CT and the ultrasonic image is established, denoising and gray level normalization processing of the two types of images are combined, the image quality and the spatial alignment precision are effectively improved, CT segmentation interested areas are positioned by means of heart structure features extracted by the ultrasonic image, interference of irrelevant areas is avoided, and the accuracy of CT segmentation is improved. And during segmentation, image texture, gray gradient and structure edge features are fused, and accurate pixel-level segmentation of the heart target anatomical structure is realized.

The embodiment of the application discloses an echocardiogram auxiliary report generation method, system, device and storage medium, and relates to the technical field of medical treatment. The echocardiogram auxiliary report generation method first aligns the input echocardiogram video through automatic registration, then identifies the starting time of each cardiac cycle in the echocardiogram video, generates the echocardiogram video frame of each cardiac cycle based on a preset frame number, divides the heart structure in the echocardiogram video frame into N target images of target regions by using a medical image segmentation model, obtains a structure parameter, inputs the structure parameter into an echocardiogram knowledge graph to automatically generate an auxiliary report, including an auxiliary diagnosis report and / or an auxiliary interpretation report. Therefore, doctors do not need to manually measure and fill in related clinical parameters or diagnosis opinions, and the health status of patients can be understood, and the working pressure of doctors is effectively reduced.

Devices, systems, and methods for releasing a catheter from an implant may include a catheter comprising first and second elongate elements, first and second elongate element lumens, a tissue anchor lumen, apertures corresponding to each of the lumens, and retaining portions between adjacent apertures. The first elongate element may extend out of its lumen through the first elongate element aperture, across the retaining portion transversely with respect to a longitudinal axis, and towards the second elongate element aperture. The first elongate element may then extend into the second elongate element aperture, loop over the second elongate element, and extend back across the retaining portion and into the first elongate element lumen. In some instances, the second elongate element is retracted to uncouple the first and second elongate elements from each other and to open a channel for the release of tethered tissue anchors.

A stabilizer sleeve is disclosed for use with cardiac implantable electronic device leads. The sleeve comprises a tubular body sized to receive the lead and shaped to bias the lead toward a desired anatomical location, thereby reducing migration, dislodgement, and interference with cardiac structures. The sleeve may be introduced with the lead as a unitary assembly or added after lead placement. Various embodiments include a slitted body for expansion, radiopaque markers for visualization, detachable nose-cone tips for guidewire tracking, and antimicrobial coatings. The sleeve may be formed of polymers, alloys, or composite materials, and may include features such as texturing or anchoring projections for tissue engagement. Applications include positioning within a tricuspid valve commissure to minimize regurgitation and stabilizing left ventricular leads within branches of the coronary sinus. The device and methods described provide improved long-term stability and performance of implanted cardiac leads.

The application provides a method and device for comprehensively evaluating coronary plaque risk based on medical images. The method for comprehensively evaluating coronary plaque risk based on medical images comprises the following steps: acquiring a coronary strain parameter according to a coronary tree segmented image; acquiring plaque morphology parameter information according to a plaque segmented image; acquiring hemodynamic parameter information according to a resting state time sequence image; and acquiring a plaque risk index according to the coronary strain parameter, the plaque morphology parameter information and the hemodynamic parameter information. The application firstly segments a heart structure and calculates a coronary strain parameter according to the morphology of the coronary artery and the strain difference between different time phases. The plaque morphology parameter is calculated according to the position, morphology and texture structure of the plaque. Then, the computational fluid dynamics method is used to calculate the hemodynamic parameter of the coronary artery. Finally, a comprehensive index is designed to jointly determine the coronary plaque risk.

The application provides a congenital heart disease prediction method and device, including: obtaining a target three-dimensional heart point cloud of a target patient; inputting the target three-dimensional heart point cloud into a pre-constructed segmentation and classification model, determining a normal vector of each point in the target three-dimensional heart point cloud, and clustering points on the same surface based on the normal vector of each point to determine a three-dimensional structure point cloud of each heart structure; for each heart structure, performing weighted processing on the three-dimensional structure point cloud of the heart structure according to a preset structure weight of the heart structure to determine a three-dimensional weighted structure point cloud of each heart structure; inputting the three-dimensional weighted structure point cloud of each heart structure into the segmentation and classification model for congenital heart disease prediction to determine at least one prediction result. In this way, through the scheme of the application, accurate heart structure segmentation and simultaneous prediction of multiple congenital heart diseases can be performed, thereby effectively improving the accuracy of congenital heart disease recognition.

The invention discloses a heart multi-modal image feature extraction method and device, equipment, a medium and a product, and relates to the technical field of medical image processing, and the method comprises the steps: obtaining multi-modal heart image data, patient information and case information in a multi-center imaging department and an ultrasound department; labeling the multi-modal heart image data, and constructing a multi-modal iconography feature database; establishing a database platform comprising a server storage control terminal and a data screening and uploading terminal; opening a communication link after verifying the identity of the requester; training is carried out based on the marked multi-mode heart data and a deep learning algorithm so as to obtain heart structure features of heart ultrasound and nuclear magnetic images through coronary angiography (CCTA) image data; finally, the system is deployed and applied clinically. According to the method, the heart structure features of the other two modal images are predicted based on the single-modal CCTA image data, the diagnosis range based on the CCTA image data is expanded, the medical overhead is saved, and the diagnosis and treatment efficiency and accuracy are improved.

The invention discloses a traditional Chinese medicine composition for treating acute myocardial infarction, a pharmaceutical preparation and application thereof and belongs to the technical field of traditional Chinese medicine. The traditional Chinese medicine composition is prepared from, by weight, 9-18 parts of ginseng, 3-6 parts of pseudo-ginseng, 9-18 parts of ligusticum wallichii, 12-24 parts of radix achyranthis bidentatae, 6-12 parts of fructus forsythiae and 9-18 parts of radix curcumae. Compared with the prior art, the traditional Chinese medicine composition disclosed by the invention has the beneficial effects that (1) the traditional Chinese medicine composition provided by the invention can reduce the myocardial infarction area after ischemia, inhibit myocardial fibrosis and improve heart structure and function changes caused by acute ischemic injury; and (2) the traditional Chinese medicine composition provided by the invention can effectively inhibit oxidative stress reaction and relieve mitochondrial injury, thereby playing a role in protecting the heart. And (3) the traditional Chinese medicine composition has the advantage of good safety and can be taken for a long time.

The invention is applicable to the technical field of biological medicine, and provides application of short-chain fatty acid in preparation of medicine for treating diabetic cardiomyopathy, the short-chain fatty acid is butyric acid, and the short-chain fatty acid maintains the iron homeostasis of cardiac muscle cells and reverses ferroptosis of diabetic cardiac muscle cells by activating HIF-1alpha and transcriptionally up-regulating FTH and FTMT. According to the application disclosed by the invention, the effectiveness of the HIF-1alpha in preventing myocardial cell ferroptosis and cardiomyopathy caused by diabetes mellitus is researched in vivo and in vitro, and the expression of the HIF-1alpha can be promoted by improving the SCFAs level in a mouse, so that the change of a heart structure and functions caused by diabetes mellitus is improved, and myocardial cell ferroptosis is inhibited; a new molecular target can be provided for prevention and treatment of ferroptosis-related myocardial injury and cardiomyopathy, and theoretical and experimental foundations are laid for clinical prevention and treatment of cardiomyopathy.

For shape determination of cardiac anatomy with a medical imager, irregularities in motion, poor image quality, and misalignment of imaging planes are counteracted by a process relying on alignment of contours in combination with selection and fitting of a motion model. Contours are extracted from 2D images and aligned for each frame, which is extracted from the sequence of 2D images. The alignment may use a translation for each frame and rotation across frames for improved performance. A motion model is fit to the aligned contours and tested. If insufficient (greater than threshold difference), other motion models are aligned and tested. Motion models may be created on demand for improved performance. If sufficient, the shape of the heart structure is determined from the fit model.

The present invention belongs to the technical field of pulsed electric field ablation for hypertrophic cardiomyopathy, and specifically relates to a pulsed electric field ablation device for hypertrophic cardiomyopathy. The device comprises a pulse generator (1). A flexible catheter (2) is fixedly mounted on one side of the pulse generator (1), and a slidably connected sliding member (3) is arranged on the outer surface of the flexible catheter (2). By means of the arrangement of an auxiliary ablation assembly (6), a real-time three-dimensional echocardiography can be generated during the ablation process, and a relatively clear cardiac structure image can be provided for a doctor by means of the three-dimensional echocardiography, such that the doctor can better determine the region of hypertrophied myocardium. Moreover, data analysis is performed on the hearts of a large number of patients by means of artificial intelligence and a machine learning algorithm inside a high-speed analog-to-digital converter to generate a personalized cardiac model, so as to assist the doctor in accurate positioning, thereby reducing the number of times of adjusting the position of a PFAD electrode catheter, shortening the time required by the doctor during surgery, and reducing the difficulty of surgery treatment.

This invention relates to the field of congenital heart disease treatment technology, and discloses the application of sapropterin in the prevention and treatment of bisphenol fluorene-induced congenital heart disease in fetuses. The congenital heart disease includes cardiac structural defects, heart failure, or cardiomyocyte lesions, specifically selected from ventricular septal defects, atrial septal defects, tetralogy of Fallot, myocardial hypertrophy, decreased cardiac ejection fraction, decreased short-axis contraction rate, cardiomyocyte ferroptosis, myocardial tissue inflammatory infiltration, or myocardial collagen fibrosis. Sapropterin is used as a direct supplement to biological tetrahydropterin (BH4). Through exogenous intervention, it precisely reverses the endogenous BH4 deficiency caused by bisphenol fluorene exposure, restores the expression of GTP cyclase 1 (GCH1) and the dynamic balance of RNA m6A, and upregulates the level of YTH domain family protein 2 (YTHDF2), thereby blocking ferroptosis signal transduction, reducing oxidative stress and inflammatory response, and inhibiting the process of myocardial tissue fibrosis at the molecular level.

The invention discloses a coronary artery CTA epicardial fat automatic extraction and quantification method based on artificial intelligence, and aims to improve the accuracy and efficiency of fat extraction. The method comprises the following steps: preprocessing a coronary artery CTA image to enhance data quality; a segmentation model based on U-Net is constructed, and accurate segmentation of the heart structure is achieved; the epicardial adipose tissues in the segmented regions are automatically extracted in combination with a set CT value threshold value; quantifying the fat volume by adopting a voxel counting mode, and analyzing the density of the fat volume; a three-dimensional visual model is further generated, and the spatial relation between fat distribution and the heart structure is displayed. According to the method, the effect is verified on multiple data sets, it is shown that the method is superior to an existing method in the aspects of fat extraction precision, volume and density evaluation and model robustness, and the method has good application prospects.

A myocardial injury condition detection system based on image characteristics. The system comprises an image acquisition module, a feature extraction module, a data processing module and a report generation module, wherein the image acquisition module is used for acquiring high-resolution cardiac image data information; the feature extraction module is used for extracting, from the acquired image data information, feature information related to the cardiac structure and function; the data processing module is used for analyzing the extracted feature information, so as to identify myocardial-injury-related conditions; and the report generation module is used for generating a medical report of the myocardial-injury-related conditions for a doctor. By means of comprehensively analyzing cardiac image data information of a patient, a more comprehensive and accurate assessment of myocardial injury conditions is provided, thereby improving the accuracy and effectiveness of medical diagnosis.

The utility model relates to the technical field of three-sequence mixed wire clamps, in particular to a pre-centering structure and a three-sequence mixed wire clamp, which comprises a base plate, a fan-shaped clamping plate is slidably connected onto the surface of the base plate, a first clamping position and a second clamping position are arranged on the fan-shaped clamping plate, a push plate is movably inserted into the base plate, a sliding block is fixed on the fan-shaped clamping plate, and a first clamping groove and a second clamping groove are formed in the sliding block. An inclined sliding block is fixed to the sliding block, an inclined sliding groove is formed in the push plate, and the inclined sliding block is movably inserted into the inclined sliding groove. The hub pre-centering structure has the beneficial effects that each group of sliding blocks drive each group of fan-shaped clamping plates to move in the direction close to the center line of the base plate, so that a hub is clamped in a first clamping position or a second clamping position and pre-centering of the hub is completed, and the pre-centering structure and the three-sequence mixed line clamp can adapt to pre-centering clamping of hubs of various specifications; and in the process of mixed line machining of hubs of various specifications, the machining efficiency is greatly improved.

This invention relates to the field of medical image reconstruction technology, specifically to a method for reconstructing low-dose CT images of patients with coronary artery disease based on image enhancement. The method includes: acquiring multi-angle raw projection data of low-dose CT images from patients with coronary artery disease; performing statistical noise modeling and signal recovery on the projection data to obtain denoised projection data; generating an initial coronary CT image sequence through an iterative reconstruction algorithm; extracting cardiac structures and completing region segmentation to obtain labeled images; performing region-specific differential image enhancement based on the labeled images; enhancing vessel wall details and extracting calcified plaque features; combining edge-preserving noise suppression with post-optimization; and outputting high-quality low-dose CT reconstructed images. This method effectively improves the noise artifact problem caused by low-dose scanning, preserves key anatomical details and lesion features of the coronary arteries and myocardium, and enhances the clinical diagnostic practicality of low-dose CT images for coronary artery disease.

The embodiment of the invention discloses a three-dimensional myocardial scar visualization system. According to one specific embodiment, the system comprises a data processing end configured to perform heart structure and scar segmentation on target heart magnetic resonance data to obtain a binary tag graph for the heart and the myocardium; constructing an individualized ventricular network and a scar three-dimensional surface model based on the binary label graph; generating an initial myocardial scar three-dimensional model; generating lead implantation track information; generating a myocardial scar three-dimensional model according to the lead implantation track information; and the model application end is configured to perform model rendering on a target application page according to the model parameters corresponding to the myocardial scar three-dimensional model sent by the data processing end so as to provide an interactive operation function and generate a clinical report. According to the embodiment, accurate and visual three-dimensional visual decision support can be provided for preoperative planning and intraoperative navigation of heart diseases, so that the accuracy of surgical planning and the clinical working efficiency are remarkably improved.

The present invention provides a system for detecting myocardial damage based on imaging characteristics, which comprises an image acquisition module, a feature extraction module, a data processing module, and a report generation module. The image acquisition module is configured to acquire high-resolution cardiac imaging data; the feature extraction module is configured to extract cardiac structural and functional feature information from the acquired imaging data; the data processing module is configured to analyze and identify myocardial damage based on the extracted feature information; the report generation module is configured to generate medical reports for physicians regarding myocardial damage. The present invention provides a more comprehensive and accurate assessment of myocardial damage by performing a comprehensive analysis of the patient's cardiac imaging data, thereby enhancing the accuracy and effectiveness of medical diagnosis.