Key result
Interpretable AI-ECG accurately predicts severe coronary stenosis on CCTA across validation cohorts.
Why the study?
Coronary CT angiography is the first-line non-invasive test for CAD diagnosis, but radiation exposure, need for high-end equipment, and strict patient cooperation limit its large-scale use.
Does an AI-ECG model accurately predict hemodynamically significant coronary artery stenosis on CCTA in patients with suspected CAD?
Observational (n=2,458)
Yes
Does an AI-ECG model accurately predict hemodynamically significant coronary artery stenosis on CCTA in patients with suspected CAD?
An AI-ECG model can accurately predict vessel-specific hemodynamically significant coronary stenosis and stratify future MACE risk, offering a scalable pre-imaging triage tool.
AI-ECG may aid CAD detection where CCTA is limited; extends prior models yet remains hypothesis-generating without prospective validation.
Abstract Coronary artery disease (CAD) remains a major global health burden. Accurate identification of the culprit vessel and assessment of stenosis severity are essential for guiding individualized therapy. Although coronary CT angiography (CCTA) is the first-line non-invasive modality for CAD diagnosis, its dependence on high-end equipment, radiation exposure, and strict patient cooperation limits large-scale use. With advances in artificial intelligence (AI) and the widespread availability of electrocardiography (ECG), AI-ECG offers a promising alternative for CAD screening. In this study, we developed an interpretable AI-ECG model to predict severe or complete stenosis of the four major coronary arteries on CCTA. On the internal validation set, the model's AUCs for the right coronary artery (RCA), left main coronary artery (LM), left anterior descending artery (LAD), and left circumflex artery (LCX) were 0.794, 0.818, 0.744, and 0.755, respectively; on the external validation set, the AUCs reached 0.749, 0.971, 0.667, and 0.727, respectively. Performance remained stable in a clinically normal-ECG subset, indicating robustness beyond overt ECG abnormalities. Subgroup analyses across demographic and acquisition-time strata further confirmed model stability. Risk stratification based on vessel-specific incidence thresholds showed consistent separation on calibration and cumulative event curves. Interpretability analyses revealed distinct waveform differences between high- and low-risk groups, highlighting key electrophysiological regions contributing to model decisions and offering new insights into the ECG correlates of coronary stenosis.
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Xiao et al. (2026) conducted an observational in Coronary Artery Disease (n=2,458). AI-ECG model vs. Coronary CT Angiography (CCTA) was evaluated on Prediction of severe or complete stenosis of the four major coronary arteries (AUC). An interpretable AI-ECG model predicted severe or complete coronary artery stenosis on CCTA with AUCs ranging from 0.744 to 0.818 in internal validation and 0.667 to 0.971 in external validation.
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