考虑不确定的结果和重复测试:评估可穿戴心电图诊断性能的框架,适用于Apple Watch和人工智能增强解释
Peter Doggart1, Caitlin Fisher2, Pardis Biglarbeigi3
1PulseAI Ltd, Belfast, United Kingdom; Ulster University, Belfast, United Kingdom.
Heart rhythm
|November 9, 2025
概括
穿戴式心电图 (ECG) 的新报告框架提高了心房 (AF) 检测的准确性. 用重复测试和人工智能进行意图诊断 (ITD) 方法可以提高现实世界的可靠性.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 传统的可穿戴式心电图 (ECG) 验证通过排除不确的结果并假设一次性试验来提高心房动 (AF) 检测性能.
- 目前的报告框架并不准确地反映可穿戴心电图设备的实际临床使用情况.
研究的目的:
- 引入和评估一个意图诊断 (ITD) 框架,以对穿戴式心电图诊断性能在AF检测中进行现实的评估.
- ITD框架包括不确定的输出和重复测试.
主要方法:
- 一项前性观察性研究比较了三个报告框架:天真 (排除不确定的),务实 (计为不正确) 和ITD (允许3次尝试).
- 使用原生算法和支持人工智能的神经网络分析了相同的Apple Watch ECG记录.
- 这项研究包括爱尔兰一家教学医院的296名参与者.
主要成果:
- 朴的果手表ECG灵敏度/特异性为96.1%/97.9%;务实的78.1%/81.0%;ITD为92.2%/91.0%.
- 人工智能算法实现了实用98.4%/96.1%和ITD98.4%/96.6%,与Apple Watch原生算法相比,不确定的输出减少了92%.
- 对于Apple Watch来说,可重复性很大 (kappa 0.77),对于AI来说,几乎是完美的 (0.96).
结论:
- 可穿戴式心电图报告应该采用ITD框架,明确处理不确定的输出和重复测试.
- 简单的报告会增加业绩,而务实的报告会降低业绩.
- 人工智能增强的解释显著减少了不确定的结果,并提高了ITD准确性,以可靠地检测真实世界AF.
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