人工智能用于检测心律失常的诊断准确性使用12导电心电图:系统性审查和元分析
medRxiv : the preprint server for health sciences
|February 23, 2026
概括
人工智能 (AI) 在使用12导电心电图 (ECG) 检测心律失常方面表现出高准确度. 这一元分析证实了AI的存在.
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:
- 人工智能 (AI) 是解释12导电心电图 (ECG) 的有希望的工具.
- 人工智能有可能提高心律失常检测的诊断准确性.
- 现有研究表明方法和验证的变化,需要进行定量合成.
研究的目的:
- 用12导电心电图量化合成AI算法用于检测心律失常的诊断性能.
- 评估基于AI的心律失常检测的灵敏度,特异性和曲线下的面积 (AUC).
主要方法:
- 根据PRISMA-DTA 2018指南进行的系统审查和元分析.
- 在MEDLINE,Embase和Cochrane图书馆进行的搜索,截至2025年9月.
- 包括20项研究,涵盖超过550万个ECG;使用双变随机效应模型生成的聚合估计.
主要成果:
- 聚合灵敏度:94.0%,特异性:98.7%,AUC:0.982用于基于AI的心律失常检测.
- 对心房的检测:灵敏度为92.6%,特异性为99.1%,AUC为0.988.
- 外部验证 (n=6) 显示出高性能:灵敏度为96.9%,特异性为95.6%,AUC为0.983.
结论:
- 人工智能模型在12导电心电图上检测心律失常的诊断性能非常出色.
- 研究结果支持将人工智能整合到临床工作流程中,特别是在心脏病专业知识有限的地方.
- 标准化数据集和前性验证对于有效和公平的AI实施至关重要.
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