用AI分析心电图预测冠脉狭窄，助力无创风险分层。
Fine-tuning an ECG Foundation Model to Predict Coronary CT Angiography Outcomes
- 基于CCTA标注的冠脉狭窄数据，训练AI模型识别心电图中高风险信号。
- 模型在内外部数据集表现稳定，对不同血管狭窄程度有显著区分能力。
- 可辅助临床决策，尤其适合用于低中危人群的精准筛查与分流。
冠状动脉疾病（CAD）仍是全球重大公共卫生负担，但缺乏可扩展的影像前风险分层工具。本多中心研究开发并验证了一种基于冠状动脉计算机断层扫描血管造影（CCTA）作为解剖参考的AI心电图（AI-ECG）模型，用于预测血管特异性血流动力学显著狭窄（右冠状动脉、前降支、回旋支≥70%；左主干≥50%）。模型在内部和外部队列、正常心电图及预设人口统计学与临床亚组中均表现出良好判别力，性能一致。预测概率随CCTA定义的狭窄程度升高，被划分为低、中、高风险三类。校准与决策曲线分析支持其临床实用性。结合指南推荐的先验概率可改善风险再分类，提升排除能力，减少灰区比例。纵向随访显示，模型定义的风险组在主要不良心血管事件上呈现清晰分离。波形与归因分析揭示了结构化的心电图差异及与高风险预测相关的生理意义信号区域。结果表明AI-ECG可作为影像前风险分层与临床分诊的可行工具，值得在更广泛临床场景中进行前瞻性验证。
原文摘要 · Abstract (English)
Coronary artery disease (CAD) remains a major global public health burden, yet scalable pre-imaging risk stratification tools are limited. In this multicenter study, we developed and validated an artificial intelligence-enabled electrocardiography (AI-ECG) model using coronary computed tomographic angiography (CCTA) as the anatomical reference to predict vessel-specific hemodynamically significant stenosis ($\geq 70\%$ for RCA, LAD, LCX; $\geq 50\%$ for LM). The model was evaluated in internal and external cohorts, clinically normal ECGs, and prespecified demographic and clinical subgroups. It showed discrimination across vessels in internal validation and consistent external and normal ECG performance. Predicted probabilities increased with CCTA-defined stenosis severity and were converted into vessel-specific low-, intermediate-, and high-risk strata. Calibration and decision curve analyses supported its clinical utility. Integration with guideline-based pre-test probability improved risk reclassification, enhanced rule-out performance, and reduced the gray-zone proportion. In longitudinal follow-up, model-defined risk groups showed clear separation in major adverse cardiovascular events. Waveform- and attribution-based analyses identified structured ECG differences and physiologically meaningful signal regions linked to high-risk predictions. These results support AI-ECG as a feasible tool for pre-imaging risk stratification and clinical triage, warranting prospective validation in broader clinical settings.
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