使用可穿戴心电图 (ID-SHD) 进行结构性心脏病的人工智能驱动评估的研究协议
Arya Aminorroaya1, Lovedeep Singh Dhingra1, Aline Pedroso Camargos1
1Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT.
medRxiv : the preprint server for health sciences
|April 2, 2024
概括
这项研究验证了一种可穿戴的人工智能-ECG算法,用于使用便携式设备检测结构性心脏病 (SHD). 这就是AI-ECG算法.
科学领域:
- 心脏病学 心脏病学
- 医疗器械 医疗器械
- 人工智能的人工智能
背景情况:
- 便携式心电图 (ECG) 设备通过AI-ECG算法为早期结构性心脏病 (SHD) 检测提供了潜力.
- 目前尚不清楚AI-ECG用于SHD查的实际性能.
- 这项研究评估了一种适合穿戴的AI算法,用于使用单便携式ECG检测SHD.
研究的目的:
- 评估适应可穿戴设备的AI-ECG算法的有效性,以识别SHD.
- 在现实世界的临床查环境中评估AI-ECG性能.
- 为了比较AI-ECG结果与胸前回声心电图 (TTE) 发现.
主要方法:
- 耶鲁纽黑文医院 (YNHH) 的截面研究协议.
- 招募了585名接受常规跨胸腔心声图 (TTE) 的患者.
- 使用Apple Watch和另一个便携式设备,与EHR数据相关联的1-lead ECG采集.
- 评估AI-ECG算法性能与TTE作为SHD的黄金标准,包括LVSD,膜疾病和LVH.
主要成果:
- 该研究协议旨在评估AI-ECG算法在识别SHD方面的性能.
- 将AI-ECG结果与TTE数据进行比较将确定算法有效性.
- 建立了与电子健康记录和安全数据维护的数据链接.
结论:
- 该研究旨在验证可穿戴AI-ECG算法用于现实世界SHD查.
- 这些发现将为便携式心电图设备和人工智能在心血管疾病管理中的实用性提供信息.
- 这项研究解决了在临床实践中理解AI-ECG表现的关键差距.
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