用人工智能增强的六导线便携式心电图装置用于检测左心室缩功能障碍:一项前性单中心队列研究
Jaehyun Lim1, Hak Seung Lee2,3, Ga In Han2,3
1Division of Cardiology and Cardiovascular Centre, Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
European heart journal. Digital health
|May 21, 2025
概括
使用便携式设备的人工智能电心图 (AI-ECG) 模型在检测左心室缩功能障碍 (LVSD) 中表现出有效性. 这种AI-ECG方法的准确性与传统的12心电图可比,用于早期LVSD查.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗器械 医疗器械
背景情况:
- 左心室缩功能障碍 (LVSD) 是一种需要早期检测的危急状况.
- 与人工智能 (AI) 集成的便携式心电图 (ECG) 设备提供了远程和可访问的心脏评估的潜力.
- 来自用于LVSD检测的便携式设备的AI-ECG模型的实际有效性需要验证.
研究的目的:
- 为了评估AI-ECG模型的诊断性能,使用一个用于检测LVSD的六便携式设备.
- 为了比较便携式AI-ECG模型的准确性与传统的基于12导电图的AI-ECG模型.
- 评估便携式AI-ECG设备在早期LVSD查方面的潜力.
主要方法:
- 一项前性,单中心研究,涉及19岁及以上的患者,在胸前心电回声学期间接受六线心电图记录.
- 从手持便携式设备 (AliveCor KardiaMobile 6L) 来解释六导电图输入的先前验证的12导AI-ECG模型的重新训练.
- 主要结果是用于检测LVSD的接收器操作特征曲线 (AUROC) 下的面积 (射出分数<40%).
主要成果:
- 使用六便携设备的AI-ECG模型实现了AUROC为0.924 (95%CI为0.903-0.944),灵敏度为83.4%,特异性为88.7%.
- 基于传统的12导心电图的AI-ECG模型显示AUROC为0.962 (95%CI为0.947-0.977),灵敏度为90.1%,特异性为91.1%.
- 总共有1635名患者被纳入最终分析,其中163人被诊断为LVSD.
结论:
- AI-ECG模型使用一个六导线手持便携式心电图装置有效地识别LVSD.
- 便携式AI-ECG模型的诊断准确性与传统的12导电心电图相当.
- 便携式AI-ECG设备作为早期LVSD查的有效工具具有显著的潜力.
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