An Explainable Artificial Intelligence-enabled ECG Framework for the Prediction of Subclinical Coronary
1Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin-si, Gyeonggi-do, Republic of Korea.
Abstract:
Coronary artery calcium (CAC) as assessed by computed tomography (CT) is a marker of subclinical coronary atherosclerosis. However, routine application of CAC scoring via CT is limited by high costs and accessibility. An electrocardiogram (ECG) is a widely-used, sensitive, cost-effective, non-invasive, and radiation-free diagnostic tool. Considering this, if artificial intelligence (AI)-enabled electrocardiograms (ECGs) could opportunistically detect CAC, it would be particularly beneficial for the asymptomatic or subclinical populations, acting as an initial screening measure, paving the way for further confirmatory tests and preventive strategies, a step ahead of conventional practices. With this aim, we developed an AI-enabled ECG framework that not only predicts a CAC score ≥400 but also offers a visual explanation of the associated potential morphological ECG changes, and tested its efficacy on individuals undergoing health checkups, a group primarily comprising healthy or subclinical individuals. To ensure broader applicability, we performed external validation at a separate institution.
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