人工智能启用了心电图分析来预测心力衰竭事件
Shaan Khurshid1,2,3, Samuel F Friedman4, Shinwan Kany1,5
1Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S. Khurshid, S. Kany, J.W.C., E.S.L., D.P., M.A.-A., J.T.R., S.A.L., P.T.E., J.E.H.).
Circulation. Heart failure
|February 23, 2026
概括
一个新的人工智能模型,ECG-to-HF (ECG2HF),准确地预测了未来的心力衰竭 (HF) 风险,使用12个心电图. 这种公开可用的工具有助于识别个人进行预防措施.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 预测分析是一种预测分析.
背景情况:
- 预测心力衰竭对于预防策略至关重要.
- 现有的专有模型显示的准确性有限或不一致.
- 需要用于高频风险评估的可通用,公开可用的AI工具.
研究的目的:
- 开发和验证一个公开可用的卷积神经网络 (CNN),用于预测事件高频风险.
- 为了利用12导心电图波形进行高频风险预测.
- 创建一个名为ECG-to-HF (ECG2HF) 的AI模型.
主要方法:
- 开发ECG2HF使用马萨诸塞州综合医院 (MGH) 94,636名患者的数据.
- 在三个独立的测试组 (MGH,BWH,BIDMC) 中验证了ECG2HF,涉及93,868名30-79岁的个人,没有先前的HF.
- 使用NLP识别了10年HF事件,并通过AUC和净重新分类量化了模型性能.
主要成果:
- 在所有测试组中,ECG2HF表现出强烈的10年HF预测,AUC从0.84到0.86.
- 该模型显示,与聚合队列方程相比,歧视和净重新分类得到了改善.
- 在美国东北部的大型医疗保健样本中,表现一致.
结论:
- ECG2HF是一个经过验证的,公开可用的AI工具,用于预测来自12ECG的未来HF风险.
- 该模型在不同患者群体中表现出有利和一致的性能.
- ECG2HF可以为有针对性的HF预防提供有效的风险分层.
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