32 results about "Coronary artery calcification" patented technology

The invention provides a clinical diagnosis and treatment path generation method and system for tracking a chest pain patient. Wherein a dynamic spatio-temporal data atlas is constructed by synchronously collecting respiratory rhythm phase difference, coronary artery calcification integral and hypersensitive troponin curve slope, and risk degree transition signals are generated by coupling analysis of ST segment elevation amplitude asymmetric growth, left ventricular ejection fraction attenuation gradient and B-type natriuretic peptide release rate. In the acute stage, the constraint relation between a vascular recanalization time window and thrombolysis taboo is dynamically bound, in the subacute stage, a phase synchronization rule for enhancing CT scanning and anticoagulant drug dosage is established through a multidisciplinary interface, intravascular ultrasonic plaque morphological mutation characteristics and extracorporeal circulation parameters are fused, the blood supply reconstruction time sequence is dynamically adjusted based on a myocardial oxygen consumption balance equation, and the thrombolysis time sequence is determined. And path self-optimization is realized through space-time superposition of an interventional therapy delay parameter and a drug metabolism curve. According to the technical scheme provided by the invention, chest pain diagnosis and treatment path dynamic optimization and multi-modal parameter collaborative decision-making are realized, and the risk layering precision and the treatment timeliness are improved.

The invention provides application of retinoic acid in preparation of a medicine for treating vascular calcification, and belongs to the technical field of medicines. The early warning and treatment effects of retinoic acid on vascular calcification are provided for the first time, a new source is provided for preparing the medicine for early warning and / or treating vascular calcification, and meanwhile the new medicinal value of retinoic acid is explored. Cardiovascular calcification and vascular smooth muscle cell calcification are relieved by supplementing retinoic acid in vivo and in vitro, and the feasibility that retinoic acid can be used as a potential medicine for treating vascular calcification is prompted. The retinoic acid content is in negative correlation with the coronary artery calcification integral of the human body and the calcification severity degree, and it is prompted that the retinoic acid content can serve as an early warning index of cardiovascular calcification and the severity degree.

The invention discloses a system for evaluating coronary artery calcification points through photon CT non-electrocardiogram gated lung CT plain scanning, and relates to the technical field of coronary artery calcification point evaluation. Through the CT data acquisition module, the coronary artery calcification recognition module, the integral calculation evaluation module and the risk evaluation generation module, X-ray photons are directly counted, electronic noise is eliminated, the signal-to-noise ratio and the contrast noise ratio are improved, in conventional chest low-dose CT, electrocardio gating is not needed, and the cost is reduced. Automatic processing from low-dose lung CT flat scanning data acquisition to structured report generation is achieved, manual data transmission and intervention among all links are not needed, coronary artery calcification evaluation is fused into a conventional lung CT examination process, the extra burden caused by the fact that a patient needs to independently conduct coronary artery CT scanning is avoided, and the examination efficiency is improved; errors possibly caused by manual operation are reduced, consistency and reliability of evaluation results are ensured, and clinical examination and diagnosis processes are optimized.

The invention relates to a cardiac coronary artery calcification score prediction method and system based on a multi-modal large model. The method comprises the following steps: acquiring coronary artery angiography videos of a heart and other target body positions, and extracting visual feature vectors; performing body position marking and word segmentation in a text prompt, inputting the body position marking and word segmentation into a large language model for coding, and replacing corresponding body position marks with visual feature vectors corresponding to different body positions to obtain visual text interleaving data; and fitting the preliminary calcification score by using each normalization function, and performing inverse transformation on the preliminary calcification score by using an inverse function of the target normalization function to obtain a predicted calcification score. Visual feature vectors of the heart and other body positions are fused, and information of different modes and complementary information among the multiple modes are utilized; the preliminary calcification score is subjected to normalization processing and inverse transformation, so that the stability and precision of regression prediction are improved, and the coronary artery calcification degree of the heart can be quantitatively predicted.

A method for predicting an anomaly in coronary artery calcification (CAC) using a mammography image and executed by at least one processor according to exemplary embodiments of the present invention may comprise the steps of: acquiring a mammography image and clinical data of a subject; and determining the presence of a CAC anomaly in the subject on the basis of the mammography image and the clinical data.

The embodiment of the invention provides an ablation impact assembly and an ablation impact device. An ablation piece of the ablation impact assembly is installed on a tube shell, and an impact wave excitation piece is installed on the tube shell and avoids the ablation piece. The adjusting assembly is movably installed in the tube shell and suitable for adjusting the angle of the laser emitted by the shock wave excitation piece so that the laser can be emitted towards the mixed liquid in the balloon. Thus, the laser can be emitted through the ablation part to break through the blocked coronary artery, the angle and energy parameters of the laser emitted by the shock wave excitation part can be adjusted through the adjusting assembly so that the laser can generate shock waves in the direction of the balloon, and the shock waves penetrate through the balloon to impact and damage a calcified structure. Therefore, the ablation impact assembly can integrate the functions of CTO ablation and coronary artery calcification destruction, the risk of an operation can be reduced, and the duration and cost of the operation can be reduced.

The present invention relates to the use of high-dose vitamin K2, particularly menaquinone-7 (MK-7), in the treatment of coronary heart disease in high-risk subjects with a coronary artery calcification (CAC) score of ≧400 (e.g., at the start of treatment), and the treatment slows down the progression of coronary artery calcification.

A system and method for modifying calcifications on a heart valve includes a dual-function heart valve catheter for performing a cutting operation and an intravascular lithotripsy (IVL) operation. The dual-function heart valve catheter includes an inflatable balloon having a cutting feature on an outer surface of the balloon such that, when the balloon is inflated, the cutting feature cuts or abrades calcification regions on the heart valve. The dual-function heart valve catheter includes first and second electrodes inside the balloon for generating a high voltage pulse from an electrical source, thereby creating a spark through a conductive medium inside the balloon to perform IVL on the calcification regions. The cutting and IVL operations can be repeated as appropriate to effectively treat the calcium legions, including moving and repositioning the catheter within the heart.

The invention discloses a CT-guided coronary artery calcified plaque and blood vessel deformation analysis method, and relates to the field of cardiovascular disease diagnosis and treatment. The method comprises the following steps: S1, measurement of aorta root reference parameters and coronary artery opening spatial position; S2, quantitative analysis of coronary artery length and deformation curvature; S3, coronary artery calcified plaque form and three-dimensional reconstruction; S4, calcified plaque lumen occupancy and occlusion section calcification evaluation; through detailed CT guidance analysis, the method comprises the steps of aorta root reference parameter measurement, coronary artery length and deformation curvature quantification and calcified plaque form and three-dimensional reconstruction, the anatomical structure and the lesion condition of the coronary artery can be accurately evaluated, the high-precision diagnosis method helps doctors to more accurately know the illness state before an operation, and the diagnosis accuracy is improved. And a more reasonable operation scheme is formulated, so that the success rate of an operation and the prognosis effect of a patient are improved.

The present invention discloses a coronary artery calcification prediction and analysis system based on the receiver operating characteristic curve, which relates to the field of coronary artery calcification detection technology, including: an integrated processing unit for collecting clinical data, laboratory data, and imaging data, and generating standardized data through data preprocessing; an evaluation and prediction unit for predicting the risk probability of coronary artery calcification under different physiological feature combinations and supporting risk stratification; an analysis and display unit for generating transparent explanations and assisting decision-making through interactive visualization tools; a decision optimization unit for generating intervention recommendations based on the prediction results and optimizing blood magnesium management strategies. The present invention significantly improves the prediction accuracy and clinical practicality through an improved random forest model that integrates dynamic integrated selection and weighted probability. Through algorithm innovation and visual closed-loop design, it achieves full-chain optimization from data to decision-making, providing a precise, transparent, and dynamic intelligent tool for the prevention and treatment of CAC in CKD patients.

The invention discloses a coronary artery calcification prediction analysis system based on a subject working characteristic curve, and relates to the technical field of coronary artery calcification detection, and the coronary artery calcification prediction analysis system comprises an integration processing unit which is used for collecting clinical data, laboratory data and image data, and generating standardized data after data preprocessing; the evaluation and prediction unit is used for predicting the risk probability of coronary artery calcification under different physiological feature combinations and supporting risk layering; the analysis and display unit is used for generating transparent explanation and assisting decision making through an interactive visualization tool; and the decision optimization unit is used for generating intervention suggestions according to the prediction result and optimizing a blood magnesium management strategy. According to the method, through the improved random forest model of dynamic integration selection and weighted probability fusion, the prediction precision and clinical practicability are remarkably improved, through algorithm innovation and visual closed-loop design, full-chain optimization from data to decision is achieved, and an accurate, transparent and dynamic intelligent tool is provided for CAC prevention and treatment of CKD patients.

This invention discloses a cardiovascular disease risk scoring system based on multidimensional coronary artery calcification indicators, comprising: a data acquisition module, a feature screening module, a model construction module, and a scoring conversion module. The cardiovascular disease risk scoring system and method based on multidimensional coronary artery calcification indicators can achieve early risk identification and accurate risk stratification in the primary prevention population of cardiovascular disease, assist clinicians in formulating individualized intervention strategies, and at the same time take into account clinical operability and convenience, better meeting the actual diagnosis and treatment needs of primary screening and rapid assessment.

Provided herein are methods, systems, and computer program products for the detection and evaluation of coronary artery calcium (CAC) (e.g., CAC scoring) comprising receiving voltage-time data of a plurality of leads of an electrocardiograph subject; generating a feature vector from the voltage-time data; providing the feature vector to a pretrained learning system; and receiving from the pretrained learning system an indication of the level of coronary artery calcium in the subject.

The invention relates to the technical field of medical monitoring, in particular to a coronary artery calcification stability prediction system fusing an IVUS image and hemodynamic data, which comprises a memory and a processor, and can realize a coronary artery calcification stability prediction method, the method comprises the following steps: obtaining an IVUS image and hemodynamic data of a to-be-detected person, the IVUS image and the to-be-tested person have a preset first three-dimensional relationship; processing the IVUS image based on a gray level co-occurrence matrix to obtain a corrected IVUS image, the corrected IVUS image and the IVUS image having a preset two-dimensional relationship; based on the first three-dimensional relationship and the two-dimensional relationship, determining a second three-dimensional relationship between the corrected IVUS image and the to-be-tested person; and establishing a coronary artery calcification stability prediction model based on the corrected IVUS image and the hemodynamic data, and obtaining a coronary artery calcification stability trend chart according to the coronary artery calcification stability prediction model so as to judge the coronary artery calcification stability of the to-be-tested person. Through the arrangement, the accuracy of coronary artery calcification judgment of the person to be detected can be improved.

A system for coronary artery calcium (CAC) estimation includes a processor and a memory, including instructions stored thereon, which when executed by the processor, cause the system to: extract a plurality of features from patient data using a first machine learning (ML) model, the patient data including a retinal image, sociodemographic data, and / or clinical data; refine the extracted plurality of features using an attention layer, the attention layer configured to highlight retinal structures with an indication of calcification risk; combine a subset of the plurality of features into a data vector using a second ML model; provide a CAC estimation based on the data vector; determine that the provided CAC estimation exceeds a predetermined threshold; and generate an output indicating a calcification risk level, based on the CAC estimation.

The application discloses a kind of coronary angiography calcification identification method, system and storage medium based on deep learning, it is related to medical cardiovascular image processing technical field.The present application comprises the following steps: the comprehensive evaluation of different blood vessel angiography projection position of the to-be-tested coronary artery;According to the CT angiography, mark the calcification lesion on the original digital subtraction angiography image independently;Using U-net neural network combined with focal loss function learning and identifying the marked calcification lesion;The U-net neural network output result is input into the LSTM network to calculate the calcification score.The present application improves the accuracy of coronary artery calcification detection and improves the prediction performance of machine learning algorithm.

The application discloses a coronary artery plaque and stenosis analysis system based on a photon CT pure calcification sequence and belongs to the technical field of coronary arteries. The system comprises a data acquisition sequence generation module, a calcification artifact precision correction module and a plaque and stenosis analysis and diagnosis module. The data acquisition sequence generation module is used for coronary artery scanning based on photon counting CT to obtain photon CT pure calcification sequence image data. The calcification artifact precision correction module is used for the precise correction of coronary artery calcification artifacts of CCTA images to determine a real lumen view without artifact interference. The plaque and stenosis analysis and diagnosis module is used for the analysis and diagnosis of coronary artery plaques and stenosis. The application solves the problem that the existing coronary artery plaques and stenosis cannot be accurately analyzed based on a photon CT pure calcification sequence, thereby leading to low analysis and diagnosis accuracy of coronary artery plaques and stenosis. The application can accurately analyze coronary artery plaques and stenosis based on a photon CT pure calcification sequence and can improve the analysis and diagnosis accuracy of coronary artery plaques and stenosis.

Receiving voltage-time data of a plurality of leads of an electrocardiograph of a subject, generating a feature vector from the voltage-time data, providing the feature vector to a pre-trained learning system, and receiving, from the pre-trained learning system, a display of the level of coronary artery calcium in the subject. A method, system, and computer program product for detecting and evaluating coronary artery calcium (CAC) (e.g., CAC scoring) are provided herein.

This application provides a method, apparatus, and storage medium for calculating coronary artery calcium scores, comprising: segmenting an initial three-dimensional medical image of the cardiac region from a plain coronary CT image using an image segmentation model; identifying calcified plaque regions in the initial three-dimensional medical image to determine at least one target region image and the location information of each image; identifying the coronary vessel category in each image by recognizing the input consisting of the target region image and the image location information; performing overall recognition of the initial three-dimensional medical image using a second coronary classification model to determine the calcified plaque regions included in the initial three-dimensional medical image, the location information of each region, and the category of coronary vessels; and calculating the calcium score for the calcified regions identified by both models to obtain the total coronary artery calcium score. Thus, the method provided in this application can quickly and accurately calculate the coronary artery calcium score based on plain CT scans.

Provided herein are methods, systems, and computer program products for the detection and evaluation of coronary artery calcium (CAC) (e.g, CAC scoring) comprising receiving voltage-time data of a plurality of leads of an electrocardiograph of a subject; generating a feature vector from the voltage-time data; providing the feature vector to a pretrained learning system; and receiving from the pretrained learning system an indication of the level of coronary artery calcium in the subject.

A system for coronary artery calcium (CAC) estimation includes a processor and a memory, including instructions stored thereon, which when executed by the processor, cause the system to: extract a plurality of features from patient data using a first machine learning (ML) model, the patient data including a retinal image, sociodemographic data, and / or clinical data; refine the extracted plurality of features using an attention layer, the attention layer configured to highlight retinal structures with an indication of calcification risk; combine a subset of the plurality of features into a data vector using a second ML model; provide a CAC estimation based on the data vector; determine that the provided CAC estimation exceeds a predetermined threshold; and generate an output indicating a calcification risk level, based on the CAC estimation.

The application discloses a photon CT non-electrocardiogram gating lung CT plain scanning coronary artery calcification score evaluation system and relates to the technical field of coronary artery calcification score evaluation.The CT data acquisition module, the coronary artery calcification identification module, the score calculation and evaluation module and the risk evaluation generation module directly count X-ray photons, eliminate electronic noise, improve the signal-to-noise ratio and the contrast-to-noise ratio, in the conventional chest low-dose CT, without electrocardiogram gating, realize the automatic processing from the low-dose lung CT plain scanning data acquisition to the structured report generation, without manual data transmission and intervention among the links, integrate the coronary artery calcification evaluation into the conventional lung CT examination process, avoid the additional burden of the patient needing to separately perform coronary artery CT scanning, improve the examination efficiency, reduce the error possibly introduced by manual operation, ensure the consistency and reliability of the evaluation result and optimize the clinical examination and diagnosis process.

The invention provides a mitral valve calcification evaluation method and device, electronic equipment and a readable storage medium. The method comprises the following steps: acquiring a coronary artery calcification integral sequence and a CCTA image of a target object; determining a first calcification index value of the mitral valve based on the coronary artery calcification integral sequence; determining a second calcification index value of the mitral valve based on the coronary artery calcification integral sequence and the CCTA image; according to the first calcification index value and the second calcification index value, an evaluation result of mitral valve calcification is determined, and the evaluation result comprises a calcification positioning map and multiple quantitative indexes. According to the method, CCTA image data and a calcification integral sequence are synchronously collected, mitral valve anatomical structure information and calcification quantification characteristics are integrated, the limitation of single data is made up, dependence on subjective experience is eliminated, errors caused by calcification boundary fuzziness are avoided, accurate positioning and accurate quantification of calcification are achieved, and the accuracy of calcification positioning is improved. The evaluation objectivity and accuracy are improved, and more reliable data support is provided for clinical diagnosis.

The present disclosure provides systems and methods to receiving OCT or IVUS image data frames to output one or more representations of a blood vessel segment. The image data frames may be stretched and / or aligned using various windows or bins or alignment features. Arterial features, such as the calcium burden, may be detected in each of the image data frames. The arterial features may be scored. The score may be a stent under-expansion risk. The representation may include an indication of the arterial features and their respective score. The indication may be a color coded indication.

The invention provides a coronary artery calcification score estimation method and device, equipment and a storage medium. The method comprises the steps that a to-be-evaluated target three-dimensional chest CT image is acquired; performing coronary artery calcification region segmentation on the target three-dimensional chest CT image to obtain a coronary artery calcification image corresponding to the target three-dimensional chest CT image, and performing coronary artery calcification classification on the target three-dimensional chest CT image to obtain a coronary artery calcification classification result corresponding to the target three-dimensional chest CT image, the coronary artery calcification classification result is used for indicating whether coronary artery calcification exists in the target three-dimensional chest CT image or not; and if the coronary artery calcification classification result is used for indicating that the target three-dimensional chest CT image has coronary artery calcification, performing coronary artery calcification score estimation based on the coronary artery calcification image to obtain a coronary artery calcification score of the target three-dimensional chest CT image. According to the technical scheme, the evaluation efficiency can be improved, and the accuracy of coronary artery calcification scoring is improved.

The invention discloses a multi-mode epicardial fat and coronary artery calcification SPECT-CT image classification method and system. The method comprises the following steps: acquiring non-enhanced heart CT three-dimensional voxel data of a one-stop SPECT-CT; dividing the preprocessed CT three-dimensional voxel data by adopting threshold segmentation and connected domain analysis to obtain a coronary artery calcification region and an epicardial fat region; according to the coronary artery calcification region and the epicardial fat region, respectively calculating an Agatston calcification integral CACS, an epicardial fat volume and an epicardial fat density MEAN; according to the Agatston calcification integral CACS, the epicardial fat volume and the epicardial fat density MEAN, algorithm construction is conducted on the non-enhanced same-machine heart CT image through a pre-trained disordered multi-classification logistic regression model, image classification is conducted on SPECT-CT MPI, and the classification probability of four coronary artery structure-myocardial function combination categories is obtained; the method is only based on the non-enhanced heart CT scanning image, and the efficiency of one-stop SPECT-CT MPI image classification is improved.

The invention belongs to the technical field of biological detection, and particularly relates to a kit for auxiliary detection of coronary artery calcification and a preparation method thereof.The kit comprises a paper packaging box, a pre-coated pore plate and a detection reagent, and the detection reagent comprises a detection probe A, a detection probe B, a standard substance A, a standard substance B, a diluent and a cleaning solution; fluorescent dyes with different excitation / emission wavelengths are connected to an anti-KIRREL1 antibody and an anti-p-YAP antibody, and the content and the proportion of KIRREL1 and p-YAP in a biological sample are synchronously detected by utilizing fluorescence intensity analysis, so that the coronary artery calcification risk is evaluated in an auxiliary manner. And an efficient quantification tool is provided for dynamic monitoring of disease early-stage auxiliary diagnosis early warning, risk assessment and drug treatment effect auxiliary evaluation.

The application belongs to an image processing method, aiming at the technical problems that the coronary artery calcification is difficult to accurately extract on the CT image, the calcification integral quantification is difficult and the operation is complicated, a heart CT image coronary artery calcification integral quantification method and related device are provided, the obtained heart CT image is input into a coronary artery calcification segmentation model to obtain a coronary artery calcification segmentation result. In the coronary artery calcification segmentation model, the encoder and the decoder are multiple groups of structures with symmetric skip connection, each group of encoder and decoder adopts a residual U cell, in the encoder, the residual U cell is connected in parallel with a calcification region attention mechanism structure for obtaining a calcification region attention map, a bottleneck layer is based on a multi-head attention dissection structure feature fusion mechanism, and finally the coronary artery calcification segmentation result is integrated and calculated. While ensuring the extraction accuracy, the processing difficulty is relatively low.

The invention discloses a system for analyzing coronary plaque and stenosis through a photon CT pure calcification sequence, and belongs to the technical field of coronary arteries, and the system comprises a data collection sequence generation module which is used for carrying out coronary artery scanning based on photon counting CT, and obtaining photon CT pure calcification sequence image data; the calcification artifact accurate correction module is used for carrying out accurate correction on coronary artery calcification artifacts on the CCTA image and determining a real lumen view without artifact interference; and the plaque and stenosis analysis and diagnosis module is used for analyzing and diagnosing coronary artery plaque and stenosis. According to the method, the problem that the accuracy of analysis and diagnosis of the coronary plaque and stenosis is low due to the fact that the coronary plaque and stenosis cannot be accurately analyzed according to the photon CT pure calcification sequence in the prior art is solved. According to the method, the coronary plaque and stenosis can be accurately analyzed according to the photon CT pure calcification sequence, and the analysis and diagnosis accuracy of the coronary plaque and stenosis can be improved.

The invention belongs to an image processing method, and provides a heart CT image coronary artery calcification integral quantification method and a related device in order to solve the technical problems that at present, accurate extraction difficulty of coronary artery calcification on a CT image is large, calcification integral quantification difficulty is high, and operation is tedious. And inputting the acquired heart CT image into the coronary artery calcification segmentation model to obtain a coronary artery calcification segmentation result. In the coronary artery calcification segmentation model, encoders and decoders are multiple groups of structures with symmetrical jump connection, each group of encoder and decoder adopts a residual U-shaped unit, and in the encoders, the residual U-shaped units are connected in parallel with a calcification region attention mechanism structure used for obtaining a calcification region attention map. And the bottleneck layer is based on a multi-head attention anatomical structure feature fusion mechanism, and finally integral calculation is performed on a coronary artery calcification segmentation result. The processing difficulty is low while the extraction precision is ensured.