5 results about "Coronary artery calcification" patented technology

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.

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.

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 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 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.