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.
Validation cohort: patients with a paired non-gated chest CT and an ECG-gated cardiac CT acquired within ≤ 12 months.
Dialysis cohort: hemodialysis patients with serial non-gated CTs, plus matched kidney-healthy controls.
Emergency department cohort: patients > 50 years with non-gated chest CTs from the Emergency Department without a primary cardiac focus.
Documented objection to the scientific use of the data pursuant to Art. 21 GDPR.
Cases lacking the minimum data required for analysis (insufficient image quality or missing reference/outcome data).
Retrospective patients at University Hospital Cologne with routine clinical CT imaging (01 January 2015 - 31 December 2025), across three analytic cohorts (validation, dialysis, emergency department); approximately 1,950 cases.
Cohort
Retrospective
Not listed
Diagnostic agreement of the AI-CAC score with the reference Agatston score
Agreement between the automatically extracted AI-CAC score from the non-gated chest CT and the reference Agatston score from a paired ECG-gated cardiac CT, reported as Spearman correlation coefficient, Cohen's kappa across established risk classes (0, 1-100, 101-400, \> 400), and Bland-Altman limits of agreement; supplemented by sensitivity, specificity, positive predictive value(PPV)/negative predictive value(NPV) and F1 score
Annualized calcification progression rate and all-cause mortality
Difference in the mean annual increase in AI-CAC between hemodialysis patients (dialysis cohort) and matched kidney-healthy controls measured on serial non-gated CTs (t-test / Mann-Whitney-U), and all-cause mortality analyzed by Kaplan-Meier (log-rank) and multivariable Cox proportional-hazards models (hazard ratios adjusted for confounders
In-hospital Major Adverse Cardiac Event or cardiovascular readmission within 12 months (Emergency Department cohort).
Occurrence of in-hospital Major Adverse Cardiovascular Events (myocardial infarction, stroke, resuscitation) or cardiovascular readmission within 12 months of the index Emergency Department visit, in relation to an unrecognized high AI calcium score (\> 400); reported as adjusted odds ratios and hazard ratios from logistic regression and Cox models
Technical feasibility and inference time of the open-source AI model on local GPU clusters
Inference time per case and technical feasibility of running the open-weight deep-learning model (Swin-UNETR) as an isolated container on the institution's local GPU infrastructure
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University of Cologne
Lead sponsor
Institute for Diagnostic and Interventional Radiology, University Hospital Cologne
Collaborator
Medical Data Integration Center (MeDIC), University Hospital Cologne
Collaborator
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