Abstract
PURPOSE: Coronary artery disease is the leading global cause of mortality. Automated detection and scoring of calcified plaques can help cardiovascular risk assessment. We propose a deep learning method for automatic detection and scoring of coronary artery calcified plaques on noncontrast CT scans. APPROACH: We utilized five datasets from one internal and four external tertiary care institutions, three of them with manually annotated plaques. A coronary artery calcified plaque detection model was developed using the state-of-the-art nnU-Net deep learning framework, incorporating simultaneous segmentation of the aorta, heart, and lungs to reduce false positives. The training data consisted of 641 noncontrast CT scans from three labeled datasets, representing diverse vascular disease etiologies. Agatston scores were automatically computed to quantify plaque burden. The model was tested on 160 labeled CT scans and compared with a previous detection method. In addition, Agatston scores were correlated with patient demographics and clinical outcomes using two unlabeled datasets. RESULTS: The predicted and reference Agatston scores demonstrated a strong correlation ( r2 = 0.973 ), with a precision of 89.3%, recall of 89.1%, and an average Dice score of 75.0 ± 16.0% on the labeled testing datasets. The stratified four Agatston groups achieved 92.0% accuracy and a Cohen's Kappa of 0.913. In the unlabeled datasets, Agatston groups showed significant correlations with the Framingham risk score, cardiovascular disease, heart failure, cancer status, fragility fracture risk, smoking, and age, whereas remaining consistent across race and scanner types. CONCLUSIONS: Coronary artery plaques were accurately detected and segmented using the proposed nnU-Net-based method on noncontrast CT scans. The Agatston-score-based plaque burden assessment facilitates cardiovascular risk stratification, enabling opportunistic screening and population-based studies.