智能手表心电图和人工智能在检测急性冠状动脉综合征方面与传统的12导心电图相比
Jina Choi1, Joonghee Kim2,3, Carmen Spaccarotella4
1Cardiovascular Center, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
International journal of cardiology. Heart & vasculature
|December 17, 2024
概括
一个分析智能手表心电图 (ECG) 的人工智能 (AI) 系统显示出对诊断急性冠状动脉综合征 (ACS) 的前景. 这种AI-ECG工具实现了与传统方法相比较的高精度,即使没有标准的ECG机器,也可以帮助ACS诊断.
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:
- 急性冠状动脉综合征 (ACS) 诊断依赖于心电图 (ECG),但使用心电图机的机会有限.
- 具有心电图功能的智能手表越来越普遍,为远程监控提供了潜在的替代方案.
研究的目的:
- 评估使用基于智能手表的多通道异步心电图数据来诊断ACS的AI驱动的心电图分析系统的可行性.
- 将智能手表数据AI-ECG分析的诊断性能与标准的12导电心电图进行比较.
主要方法:
- 56名ACS患者和15名健康对照人接受了标准的12心电图和基于智能手表的9心电图.
- 一个深度学习AI-ECG工具解释了2D心电图像,以生成ST升高心肌梗塞 (qSTEMI),ACS (qACS) 和心肌损伤 (qMI) 的概率得分.
- 根据冠状动脉血管学,患者被分为急性全闭性ACS (ACS-O(+)) 和无闭性ACS (ACS-O(-)) 的患者.
主要成果:
- 智能手表和标准心电图的AI-ECG得分显示出高相关性 (0.872-0.882,P < 0.001).
- 人工智能系统在区分ACS与控制器 (AUROC 0.987-0.991) 和识别ACS-O(+) (AUROC 0.982-0.989) 中表现出色.
- 智能手表和标准心电图之间的诊断性能相当,在区分闭塞严重程度方面存在轻微挑战 (AUROC 0.855-0.880).
结论:
- 智能手表数据的AI-ECG分析显示了高一致性和可比的诊断性能与ACS的标准心电图.
- 该系统有效地将ACS-O(+) 和ACS-O(-) 与控制区分开来.
- 基于智能手表的AI-ECG具有显著的前景,因为智能手表的可访问性越来越高,因此在各种环境中促进了ACS诊断.
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