CAD remains a major global public health burden, yet scalable screening tools are limited. Although CCTA is a first-line non-invasive diagnostic modality, its use is constrained by resource requirements and radiation exposure. AI-ECG may offer a complementary approach for CAD risk stratification. In this multicenter study, we developed and validated an AI-ECG model using CCTA as the anatomical reference standard to predict vessel-specific coronary stenosis. In internal validation, the model achieved AUC values of 0.683-0.744 across vessels and showed consistent external performance. Discrimination was maintained in clinically normal ECGs and remained broadly stable across subgroups. Model-predicted probabilities increased monotonically with CCTA-defined stenosis severity. Model probabilities were converted into vessel-specific low-, intermediate-, and high-risk strata using predefined sensitivity- and specificity-based thresholds. Calibration analysis showed agreement between predicted and observed risk, while DCA indicated net clinical benefit over treat-all and treat-none strategies. Integrating AI-derived risk strata with guideline-based PTP categories improved rule-out performance, reduced the gray-zone proportion, and achieved positive NRI compared with PTP alone. In a longitudinal follow-up cohort, Kaplan-Meier analysis showed clear separation of major adverse cardiovascular event risk across model-defined risk groups. Waveform- and attribution-based analyses further identified structured ECG morphology differences and physiologically meaningful signal regions associated with high-risk predictions. These findings support AI-ECG as a feasible tool for complementary CAD screening, anatomical risk estimation, and clinical triage, while prospective studies are needed to confirm its clinical impact.

翻译：冠心病仍然是全球主要公共卫生负担，但可扩展的筛查工具十分有限。尽管冠状动脉CT血管造影（CCTA）是一线无创诊断方式，但其应用受限于资源需求和辐射暴露。人工智能心电图（AI-ECG）可能为冠心病风险分层提供补充方法。在这项多中心研究中，我们以CCTA为解剖学参考标准，开发并验证了一种用于预测血管特异性冠状动脉狭窄的AI-ECG模型。在内部验证中，该模型在各血管上的AUC值达到0.683-0.744，并显示出一致的外部性能。该判别能力在临床上正常的心电图中得以保持，并在各亚组中保持总体稳定。模型预测概率随CCTA定义的狭窄严重程度呈单调递增。通过预定义的基于灵敏度和特异度的阈值，模型概率被转换为血管特异性的低、中、高风险分层。校准分析显示预测风险与观察风险之间具有一致性，而决策曲线分析（DCA）表明该模型相比“全治疗”和“不治疗”策略具有净临床获益。将AI衍生的风险分层与指南推荐的验前概率（PTP）类别相结合，可改善排除诊断性能，减少灰区比例，并且与单独使用PTP相比实现了正向净重分类改善（NRI）。在纵向随访队列中，Kaplan-Meier分析显示，不同模型定义的风险组之间主要不良心血管事件风险存在明显分离。基于波形和归因的分析进一步识别出与高风险预测相关的结构化心电图形态差异和具有生理意义的信号区域。这些发现支持AI-ECG作为补充性冠心病筛查、解剖学风险估计和临床分诊的可行工具，但仍需前瞻性研究来证实其临床影响。