基于心电图的心力衰竭检测中机器学习算法的诊断准确性:系统性审查和元分析
Mustafa Eray Kilic1, Mehmet Emin Arayici2, Oguz Akbilgic3
1Department of Cardiology, Faculty of Medicine, Dokuz Eylül University, İzmir, Türkiye.
The Canadian journal of cardiology
|December 19, 2025
概括
应用于心电图 (ECG) 的人工智能 (AI) 显示出用于检测心力衰竭 (HF) 的前景. 性能因心力衰竭的定义和喷射率值而异,需要标准化的终点来实现可靠的AI-ECG.
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:
- 应用于心电图 (ECG) 的人工智能 (AI) 显示了心力衰竭 (HF) 检测的潜力.
- 现有研究报告异质性表现,经常将左心室缩功能障碍 (LVSD) 与HF的临床综合征混为一谈.
研究的目的:
- 系统地审查和元分析AI-ECG用于检测HF和LVSD的诊断准确性.
- 调查影响AI-ECG性能的因素,包括目标定义和射出分数 (EF) 值.
主要方法:
- 按照PRISMA-DTA指导方针对40个独特患者队列进行系统审查和元分析.
- 层次的双变量建模来合成诊断准确性,与分层和多值分析EF异质性.
- 进行元回归分析,检查外部验证,领先配置和AI模型架构等共变量.
主要成果:
- 主要分析显示,聚合灵敏度为85.9%,特异性为80.9% (HSROC AUC = 0.902).
- 性能根据目标状况 (LVSD与临床HF) 和EF值的定义而有显著差异.
- 12导电心电图和卷积神经网络架构与更高的特异性相关 (分别为p=0.003和p=0.024).
- 对高频分类模型的二次分析得出了96.2%的综合灵敏度和92.1%的特异性.
结论:
- AI-ECG显示了对HF和LVSD的显著但可变的诊断性能.
- 准确的实施需要仔细考虑目标条件定义,EF值和方法因素.
- 标准化的终点对于临床实践中可靠和可重复的AI-ECG应用至关重要.
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