Advancements in the application of deep learning for coronary artery calcification

Coronary Artery Calcification (CAC) is a characteristic pathological alteration in the progression of coronary atherosclerosis and is considered an independent predictor of Major Adverse Cardiovascular Events (MACE). The distribution, pathological classification, and quantitative evaluation of CAC are pivotal factors influencing the incidence of MACE and guiding intracoronary interventions. Deep learning methods, a widely explored domain in artificial intelligence, achieve learning and understanding of big data by constructing multi-layer neural network models. This robust approach offers significant support for intelligent medical image diagnosis within clinical settings. Currently, deep learning methods have been applied to the identification and quantification of coronary artery calcification plaques, which not only improve diagnostic efficiency but also contribute to the early prevention and treatment of patients at moderate to low risk. This article reviews the progress of deep learning applications in coronary artery calcification to gain a comprehensive understanding of this field. • Coronary calcification predicts cardiovascular risk, requiring precise imaging for diagnosis and treatment guidance. • Deep learning enhances medical imaging analysis, boosting diagnostic accuracy and efficiency. • Deep learning assesses coronary calcification via segmentation, plaque detection, and score calculation.