通过一种新型的人工智能算法,减少不确定的智能设备单导电图追踪负担
Simon Weidlich1,2, Diego Mannhart1,2, Alan Kennedy3
1University Hospital Basel, Basel, Switzerland.
Cardiovascular digital health journal
|February 23, 2024
概括
一个新的人工智能 (AI) 算法显著降低了对心电图 (ECG) 检测心房动 (AF) 的不确定的智能设备结果. 这种人工智能工具与制造商算法相比,提高了诊断准确度,提高了临床实用性.
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:
- 智能设备可以通过单导电心电图 (SL-ECG) 来自动检测心房动 (AF).
- 目前的制造商算法产生了很高的不确定的追踪率,限制了临床有用性.
研究的目的:
- 通过使用四种商业智能设备,评估基于人工智能 (AI) 的新型AF检测算法的临床价值.
- 为了比较人工智能算法的性能与原生制造商对AF检测的算法.
主要方法:
- 一项前性观察性研究,涉及206名第三级推中心的患者.
- 从AliveCor KardiaMobile,Apple Watch 6,Fitbit Sense和三星Galaxy Watch3.3上同时进行12导电心电图和SL-ECG记录,这些记录来自于三星.
- 对比一种新的AI算法 (PulseAI) 与每个设备专有的AF检测算法.
主要成果:
- 与制造商算法相比,新型AI算法在所有测试的设备中显示了保持或提高的灵敏度和显著更高的特异性.
- 对于AF检测,人工智能算法的特异性在94%-97%之间,而制造商算法的特异性为77%-83% (P < .001).
- 不确定的SL-ECG的比例从制造商算法的14%-17%大幅下降到AI算法的1.2% (P < .001).
结论:
- 一个新的AI算法大大减少了用于AF检测的不确的SL-ECG诊断.
- 与现有的制造商算法相比,人工智能算法保持了诊断灵敏度,同时显著提高了特异性.
- 这种人工智能驱动的方法有望改善基于智能设备的ECG监测对AF的临床实用性.
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