一个人工智能支持的心电图算法,用于在鼻节律期间识别心房动患者:对结果预测的回顾性分析
Zachi I Attia1, Peter A Noseworthy1, Francisco Lopez-Jimenez1
1Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Lancet (London, England)
|August 6, 2019
概括
一个人工智能 (AI) 支持的心电图 (ECG) 可以在正常的鼻节奏中检测心房动. 这种快速,即时的工具有助于识别心房动患者,改善早期诊断和治疗.
科学领域:
- 心脏病学
- 人工智能
- 医疗诊断
背景情况:
- 前庭动 (AF) 往往无症状,导致未被检测到以及相关风险,如中风和心力衰竭.
- 目前的查方法昂贵,耗时,检测率低.
- 需要快速,廉价的AF诊断.
研究的目的:
- 开发一种基于机器学习的工具,用于快速,廉价的心房动诊断.
- 使用人工智能 (AI) 来检测正常鼻腔节律中的心电图 (ECG) 信号.
主要方法:
- 开发了一个卷积神经网络 (CNN) AI模型,使用标准的10秒,12导电图,来自18岁以上的患者.
- 包括超过180,000名经过验证的心电图患者,分为培训,内部验证和测试数据集.
- 使用曲线下面面积 (AUC),精度,灵敏度,特异性和F1分数等指标评估模型性能.
主要成果:
- 一次人工智能支持的心电图检测出心房动,心房动值为0. 87,准确率为79. 4%.
- 从患者的第一个月开始加入心电图,提高了性能,AUC为0. 90和准确率为83. 3%.
- 人工智能模型在检测AF方面表现出显著的灵敏度和特异性.
结论:
- 在正常鼻节奏期间使用人工智能支持的心电图可以有效地识别患有心房动的人.
- 这项技术为早期AF检测和管理提供了有前途的方法.
- 这种人工智能工具提供了一种快速且易于使用的查方法来检测心房动.
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