15 results about "Coronary artery calcification" patented technology

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.

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.

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.

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 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 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 provides a coronary heart disease examination path triage system based on AI technology, and relates to the technical field of medical treatment.The coronary heart disease examination path triage system based on AI technology comprises a data access module, an AI integral calculation module, a symptom judgment module, a rule engine module, a result display and interface module and a parameter management and audit module, collects non-ECG gated chest CT image data, automatically calculates coronary artery calcification integral (CACS) by using AI, combines chest pain type judgment results and clinical adaptation evidence, constructs a binary risk stratification system, and outputs low-risk or high-risk stratification results and corresponding examination path suggestions.The application solves the defects of the prior art, such as dependence on subjective experience, low efficiency of calcification integral calculation and inaccuracy of examination path matching, realizes standardized and accurate shunt decision, greatly improves diagnosis and treatment efficiency, reduces medical risks and resource waste, and is suitable for coronary heart disease diagnosis and shunt scenes of medical institutions at all levels.