基于人工智能的心电图分析改善了从智能手表心电图中检测心房律乱的功能.
Laurent Fiorina1, Pascale Chemaly1, Joffrey Cellier1
1Ramsay Santé, Institut Cardiovasculaire Paris Sud, Hôpital privé Jacques Cartier, 6 avenue du Noyer Lambert, 91 300 Massy, France.
European heart journal. Digital health
|September 25, 2024
概括
深度神经网络 (DNN) 显著提高了智能手表ECG准确度,用于检测心房律乱 (AAs),在临床心脏病学环境中优于标准智能手表软件.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
背景情况:
- 智能手表心电图 (SW ECGs) 提供了对心脏节律异常的非侵入性评估,例如与中风风险相关的心房律乱 (AAs).
- 目前的SW ECG性能有限,需要像深度神经网络 (DNN) 这样的先进算法来提高准确性,特别是在不同的临床群体中.
研究的目的:
- 评估DNN算法的诊断性能,应用于SW ECG,用于在临床心脏病患者队列中检测心房律乱.
- 为了比较DNN算法的准确性与标准的果手表心电图软件和专家的12导心电图解释.
主要方法:
- 进行了两项与400名患者的临床试验,记录了同时进行的SW ECGs和12-lead ECGs (12L ECGs).
- 使用DNN算法和果手表ECG软件处理SW ECG.
- 将SW ECG的解释与专家电生理学家对12L ECG的判断进行了比较,报告了灵敏度,特异性和不确定的速率.
主要成果:
- 该DNN算法实现了91%的灵敏度和95%的特异性,明显优于果应用程序 (61%的灵敏度,97%的特异性) 和专家解释.
- DNN为99%的ECG提供了诊断,而果应用程序有22%的不确定的结果.
- 与标准SW ECG软件相比,DNN显示出更高的准确性和诊断覆盖率.
结论:
- 应用于SW ECG的基于DNN的算法在临床心脏病学环境中提供高度准确的心房失常检测.
- 与现有的智能手表软件相比,DNN显著提高了SW ECG的诊断实用性,为识别心律失常提供了更可靠的工具.
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