人工智能在通过心电图检测左心室缩的诊断准确性:一个系统的审查和元分析
Noppachai Siranart1,2,3, Natee Deepan4, Witina Techasatian5
1Division of Cardiology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, 1873 Rama 4 Road, Pathumwan, Bangkok, 10330, Thailand.
Scientific reports
|July 10, 2024
概括
与传统方法相比,人工智能 (AI) 在检测左心室缩 (LVH) 方面表现出卓越的准确性. 这种基于人工智能的查为LVH诊断提供了更好的灵敏度和特异性.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- 左心室缩 (LVH) 是心血管疾病的重要危险因素.
- 目前用于LVH的诊断标准,如Sokololow-Lyon和Cornell,在准确性上有局限性.
- 人工智能 (AI) 已经成为改善诊断能力的潜在工具.
研究的目的:
- 系统地审查和比较用于LVH检测的AI算法的诊断准确性与已建立的标准.
- 为开发用于诊断LVH的AI工具提供全面的概述.
- 通过聚合的灵敏度和特异性来评估AI在识别LVH方面的表现.
主要方法:
- 对观察性研究的系统审查和元分析,评估在LVH检测中的AI.
- 在MEDLINE,EMBASE和Cochrane数据库中搜索到2023年5月.
- 与Sokolov-Lyon和Cornell标准对比AI性能 (总结ROC,灵敏度,特异性),使用来自66,479名参与者的数据.
主要成果:
- 人工智能证明了LVH的卓越诊断准确性,总结ROC (SROC) 为0.87.
- 索科洛-里昂和康奈尔标准的准确性较低 (SROC分别为0.68和0.60).
- 人工智能实现了69%的灵敏度和87%的特异性,超过了灵敏度明显较低的传统方法.
结论:
- 与传统标准相比,基于AI的方法显示出更高的诊断准确度和更好的LVH检测灵敏度.
- 人工智能代表了一种有前途且经过验证的工具,用于查和诊断左心室缩.
- 这些发现支持将AI纳入临床实践,以更有效地管理LVH.
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An ECG utilizes electrodes on the skin...
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