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.

翻译：冠状动脉疾病（CAD）仍然是全球主要的公共卫生负担，然而可扩展的风险筛查工具十分有限。尽管冠状动脉计算机断层扫描血管造影（CCTA）是一线无创诊断手段，但其广泛应用受限于资源需求和辐射暴露。人工智能赋能的心电图（AI-ECG）可能为CAD风险分层提供一种补充性方法。我们以CCTA作为参考标准，开发并验证了一个AI-ECG模型，用于评估四条主要冠状动脉中严重（$\geq 70\%$）或完全（$\geq 99\%$）狭窄的情况。在内部验证中，该模型在不同血管上获得的受试者工作特征曲线下面积（AUC）值为0.706至0.744，并在外部验证中表现出了一致的性能（AUC：0.673至0.714）。在临床心电图正常的个体中以及在不同人口统计学和临床亚组中，模型的区分能力保持稳定。在一个具有纵向随访的专门临床队列中，基于模型预测概率进行的血管特异性风险分层，在使用Kaplan-Meier曲线的时间-事件分析中，高风险组与低风险组之间显示出明显的分离，而决策曲线分析则提示其作为辅助筛查工具具有潜在的临床效用。可解释性分析突出了与预测风险升高相关的波形模式。这些发现支持了AI-ECG用于辅助CAD风险筛查的可行性，并值得进行前瞻性评估。