AIMEDIC Publications

Deep Learning-Based Automated Quantification of Coronary Artery Calcification for Contrast-Enhanced Coronary Computed Tomographic Angiography

Category

Pub

Date

2023-03-28

Source

Journal of Cardiovascular Development and Disease

This study evaluates the accuracy of a deep learning-based automated algorithm for coronary artery calcium (CAC) quantification based on enhanced ECG-gated coronary CT angiography (CCTA) with dedicated coronary calcium scoring CT (CSCT) as the reference. The algorithm showed high agreement with CSCT results, demonstrating excellent performance in volume and Agatston scores with a failure rate of 1.3%. The results support the use of this automated system for efficient CAC quantification and cardiovascular risk classification without additional radiation exposure.

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