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

• URL: http://arxiv.org/abs/2512.05136v1
• Date: Sat, 29 Nov 2025 05:21:24 GMT
• Title: Fine-tuning an ECG Foundation Model to Predict Coronary CT Angiography Outcomes
• Authors: Yujie Xiao, Gongzhen Tang, Deyun Zhang, Jun Li, Guangkun Nie, Haoyu Wang, Shun Huang, Tong Liu, Qinghao Zhao, Kangyin Chen, Shenda Hong,
• Abstract summary: We developed an interpretable AI-ECG model to predict severe or complete stenosis of the four major coronary arteries.<n>Performance remained stable in a clinically normal-ECG subset, indicating beyond overt ECG abnormalities.
• Score: 18.45168797720226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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