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 tools for risk screening are limited. Although coronary computed tomography angiography (CCTA) is a first-line non-invasive diagnostic modality, its widespread use is constrained by resource requirements and radiation exposure. Artificial intelligence--enabled electrocardiography (AI-ECG) may provide a complementary approach for CAD risk stratification. We developed and validated an AI-ECG model using CCTA as the reference standard to estimate severe ($\geq 70\%$) or complete ($\geq 99\%$) stenosis in the four major coronary arteries. In internal validation, the model achieved area under the receiver operating characteristic curve (AUC) values of 0.706--0.744 across vessels and demonstrated consistent performance in external validation (AUCs: 0.673--0.714). Discrimination remained stable among individuals with clinically normal ECGs and across demographic and clinical subgroups. In a dedicated clinical cohort with longitudinal follow-up, vessel-specific risk stratification based on model-predicted probabilities yielded distinct separation between high-risk and low-risk groups in time-to-event analyses using Kaplan--Meier curves, while decision curve analysis suggested potential clinical utility as an adjunctive screening tool. Explainable analyses highlighted waveform patterns associated with elevated predicted risk. These findings support the feasibility of AI-ECG for complementary CAD risk screening and warrant prospective evaluation.