This article discusses the potential for end-to-end video-based deep learning models to detect and classify cardiac anomalies and cardiovascular diseases (CVDs) from cardiac magnetic resonance (CMR) images with high accuracy. The study has the potential to improve the efficiency and scalability of CMR interpretation, making it more widely used in CVD screening and diagnosis. The study was conducted using anonymized and deidentified CMR data from eight participating institutions.
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