人工智能增强心电图的可靠性
Lovedeep S Dhingra1,2, Philip M Croon1,2,3, Bruno Batinica1,2
1Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT, USA.
medRxiv : the preprint server for health sciences
|November 24, 2025
概括
与单个模型相比,人工智能启用整体心电图 (AI-ECG) 模型在检测结构性心脏疾病 (SHD) 中的可靠性更高. 住院患者的心电图显示出更多的预测不稳定性,突出了在临床环境中进一步验证的需要.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 人工智能启用心电图 (AI-ECG) 在检测和预测结构性心脏疾病 (SHD) 中表现有前途.
- 在现实世界中,AI-ECG的实用性需要在重复测试中得到一致和可靠的结果.
研究的目的:
- 评估AI-ECG模型对使用多个ECG的个体的可靠性.
- 评估不同诊断标签和建模方法的可靠性.
主要方法:
- 利用了来自5家医院和门诊网络 (2000-2024) 的心电图数据.
- 评估了7个模型,包括6个单个SHD的CNN和一个整体XGBoost模型.
- 使用一致性相关系数 (CCC),斯皮尔曼相关性,科恩的卡帕和百分比一致性评估可靠性.
- 分析了影响不同心电图布局的预测稳定性和稳定性的因素.
主要成果:
- 与单个CNN模型相比,整体XGBoost模型表现出更高的测试-重新测试相关性 (CCC:0.89-0.92) 和一致性 (kappa:0.75-0.82).
- 包括住院病人的病历在内的心电图对更有可能产生不稳定的预测 (ORs: 1.60-1.91).
- 整体模型预测在数字,印刷和照片心电图布局中保持稳定.
结论:
- 集成多个CNN预测的整体AI-ECG模型为SHD查提供了卓越的可靠性.
- 预测不一致性在住院患者中更为频繁,这表明高急症护理中的潜在不稳定性.
- 可靠的整体AI-ECG输出表明在SHD查计划中为临床实施做好准备.
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