用心电图来识别冠状动脉疾病:人工智能模型的交叉验证
Michael Leasure1, Indu Poornima2, Adam Butchy1
1HEARTio, Pittsburgh, Pennsylvania, USA.
JACC. Advances
|January 30, 2026
概括
一个人工智能模型使用心电图 (ECG) 预测冠状动脉疾病 (CAD),提供一种非侵入性诊断替代方案. 这种人工智能工具在识别显著的CAD方面表现出高准确性,可能改善患者护理途径.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 侵入性血管造影是目前诊断冠状动脉疾病 (CAD) 的标准,但它对患者构成风险.
- 开发了一种人工智能 (AI) 模型,使用休息式12导数位心电图 (ECG) 来预测显著的CAD.
研究的目的:
- 为了评估AI模型对临床显著的冠状动脉血管学患者的CAD的预测性能.
- 评估模型作为CAD诊断的非侵入性工具的实用性.
主要方法:
- 一项对16476名患者的回顾性分析,在冠状动脉血管学90天内记录了休息的12数字心电图.
- 使用10倍交叉验证的人工智能模型开发,具有根据特定血管学标准定义的显著CAD.
- 在单独的患者队列上对AI模型的外部验证.
主要成果:
- 在交叉验证 (AUC 91.4%) 和外部验证 (AUC 92.4%) 期间,AI模型在预测显著的CAD方面取得了很高的表现.
- 积极的预测值为91.7% (交叉验证) 和82.5% (外部验证).
- 负预测值为72.8% (交叉验证) 和88.1% (外部验证).
结论:
- 一个深度学习的人工智能算法有效地分析数字心电图,以预测临床上显著的CAD.
- 人工智能模型显示,作为CAD检测的非侵入性方法,AI模型具有临床实用性,可以补充传统的诊断方法.
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