Automated Coronary Artery Calcium Scoring Using Deep Learning: Validation Across Diverse Chest CT Protocols

Coronary artery calcium (CAC) scoring refines atherosclerotic cardiovascular disease (ASCVD) risk but is not frequently reported on routine non‑gated chest CT (NCCT), whose use expanded in the COVID‑19 era. We sought to develop and validate a workflow-ready deep learning model for fully automated, protocol-agnostic CAC quantification.

In this retrospective study, a deep learning (DL) model was trained and validated using 2132 chest CT scans (routine, CT-CAC, and CT-COVID) from patients without established atherosclerotic cardiovascular disease (ASCVD) collected (2013-2023) at a single university hospital. The index test was a DL-based CAC segmentation model; the reference standard was manual annotation by experienced observers. Agreement was evaluated using intra-class correlation coefficients (ICC) for Agatston scores and Cohen's kappa for CAC risk categories. Sensitivity, specificity, positive, and negative predictive values, and F1 scores were calculated to measure diagnostic performance.

The DL model demonstrated high reliability for Agatston scores (ICC=0.987) and strong agreement in CAC categories (Cohen's κ=0.86-0.95). Diagnostic performance for CAC >100 (F1=0.956) and CAC >300 (F1=0.967) was very high. External validation in the Mashhad COVID Study showed good agreement (κ=0.8). In the SBU COVID study, the F1 score for detecting moderate-to-severe CAC was 0.928.

The proposed DL model delivers accurate, workflow‑ready CAC quantification across routine, dedicated, and pandemic‑era chest CT scans, supporting opportunistic, cost‑effective cardiovascular risk stratification in contemporary clinical practice.