开发和多国验证一个集体深度学习算法,用于检测和预测结构性心脏病,使用杂的单导电心电图
Arya Aminorroaya1,2, Lovedeep S Dhingra1,2, Aline F Pedroso1,2
1Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06510, USA.
European heart journal. Digital health
|July 24, 2025
概括
一个新的AI驱动的心电图算法,ADAPT-HEART,可以使用便携式设备检测结构性心脏病. 该工具还可以预测未来的心脏病风险,为社区查提供可扩展的解决方案.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗器械 医疗器械
背景情况:
- 通过人工智能增强的12导电图可以识别结构性心脏病 (SHD),但它们在社区查中的使用是有限的.
- 开发了一种耐噪声单导AI-ECG算法,用于检测SHD并使用可穿戴设备预测其发展风险.
研究的目的:
- 开发和验证一种新的AI-ECG算法,用于使用便携式设备检测和预测结构性心脏病 (SHD).
- 评估算法在不同人群中的表现及其作为未来SHD风险预测生物标志物的实用性.
主要方法:
- 开发了ADAPT-HEART,一种深度学习算法,使用来自99,205名患者的266,740个心电图,并配对了心声图数据.
- 在四家美国社区医院,ELSA-巴西队列和英国生物银行验证了ADAPT-HEART.
- 定义SHD为LVEF<40%,中度/严重的膜疾病,或严重的LVH.
主要成果:
- ADAPT-HEART在检测SHD方面表现强,测试组的AUROC为0.879,在外部验证站点中一致 (AUROCs为0.852-0.891).
- 在没有基线SHD的个体中,ADAPT-HEART概率升高显著增加了未来SHD的风险 (增加2.8至5.7倍).
结论:
- 一个新的AI模型有效地检测和预测便携式设备上单导电心电图的SHD.
- 这项技术为基于社区的查和SHD风险分层提供了一个可扩展的战略.
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