人工智能驱动的心电图分析用于肺栓塞风险分层.
Tanmay A Gokhale1,2, Nathan T Riek2, Brent Medoff1
1Heart and Vascular Institute, University of Pittsburgh Medical Center, 3550 Terrace St, B571.3 Scaife Hall, Pittsburgh, PA 15261, USA.
European heart journal. Digital health
|September 23, 2025
概括
人工智能 (AI) 对心电图 (ECG) 的分析可以快速识别高风险的肺栓塞 (PE) 患者. 这种AI-ECG模型预测了临床风险,有助于及时治疗严重PE病例.
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:
- 急性肺栓塞 (PE) 需要快速的风险分层来进行有效的治疗.
- 目前的风险评估方法是多步骤的,包括体检,成像和实验室结果.
- 电心电图 (ECG) 单独被探索为一种快速风险分层工具.
研究的目的:
- 开发和验证使用心电图数据的AI模型,以预测急性PE患者的临床风险.
- 评估基于心电图的AI对快速识别高风险PE患者的有用性.
主要方法:
- 一个基于特征的随机森林模型被训练在1376名PE患者的ECG数据上.
- 该模型预测了由PE响应小组 (PERT) 确定的风险分层.
- 模型的性能在预测严重PE和死亡率的坚持测试组上进行了评估.
主要成果:
- 在预测临床风险 (低风险与严重PE) 中,AI-ECG模型实现了0.83的AUC和0.78的F1得分.
- 在AI模型确定的低风险和高风险组之间观察到30天和住院死亡率的显著差异.
- 55%的队列患有亚大质量或大质量PE,被定义为"严重PE".
结论:
- 对12个导线ECG的AI分析为快速PE风险分层提供了有价值的方法.
- 这种方法有助于迅速识别和治疗高风险出现不良后果的PE患者.
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