Fine-tuning an ECG Foundation Model to Predict Coronary CT Angiography Outcomes

Abstract

Coronary artery disease (CAD) remains a major global public health burden, yet scalable pre-imaging risk stratification tools are limited. In this multicenter study, we developed and validated an artificial intelligence-enabled electrocardiography (AI-ECG) model using coronary computed tomographic angiography (CCTA) as the anatomical reference to predict vessel-specific hemodynamically significant stenosis ($\ge 70\%$ for RCA, LAD, LCX; $\ge 50\%$ for LM). The model was evaluated in internal and external cohorts, clinically normal ECGs, and prespecified demographic and clinical subgroups. It showed discrimination across vessels in internal validation and consistent external and normal ECG performance. Predicted probabilities increased with CCTA-defined stenosis severity and were converted into vessel-specific low-, intermediate-, and high-risk strata. Calibration and decision curve analyses supported its clinical utility. Integration with guideline-based pre-test probability improved risk reclassification, enhanced rule-out performance, and reduced the gray-zone proportion. In longitudinal follow-up, model-defined risk groups showed clear separation in major adverse cardiovascular events. Waveform- and attribution-based analyses identified structured ECG differences and physiologically meaningful signal regions linked to high-risk predictions. These results support AI-ECG as a feasible tool for pre-imaging risk stratification and clinical triage, warranting prospective validation in broader clinical settings.