Artificial Intelligence - Based Opportunistic Coronary Artery Calcium Scoring on Routine Chest-CT Scan

This retrospective, non-interventional study externally validates a pre-trained open-weight deep-learning algorithm (Swin-UNETR) for the opportunistic quantification of coronary artery calcium (CAC) on non-gated routine chest CT scans acquired at a German academic center, and evaluates the prognostic value of this automated imaging biomarker for cardiovascular risk stratification. Coronary calcium is an established predictor of cardiovascular risk, but is not routinely quantified on the tens of thousands of non-cardiac chest CTs performed each year. Because existing high-performing AI models were trained almost exclusively on U.S. cohorts, external validation on a European scanner fleet is required to exclude scanner bias (domain shift). The study comprises three linked analytic cohorts: (1) a validation cohort comparing the AI-CAC score against the ECG-gated cardiac CT Agatston reference; (2) a dialysis cohort assessing calcification progression and mortality; and (3) an emergency department cohort assessing short-term cardiovascular events. This is an investigator-initiated trial with no intervention on patients.