使用移动心电图装置对心率纠正的QT间隔进行人工智能评估
John R Giudicessi1, Matthew Schram2, J Martijn Bos3
1Clinician-Investigator Training Program (J.R.G.), Mayo Clinic, Rochester, MN.
Circulation
|February 1, 2021
概括
一个人工智能 (AI) 深度神经网络 (DNN) 准确地预测心率校正的QT间隔 (QTc) 从移动心电图设备. 这种支持人工智能的移动心电图查为检测长QT综合征提供了一种具有成本效益的方法.
科学领域:
- 心脏病学
- 人工智能
- 医疗器械
背景情况:
- 延长心率校正的QT间隔 (QTc) 会增加心室节律失常和心脏突然死亡的风险.
- 原因包括药物,遗传 (先天性长QT综合征) 和COVID-19等疾病.
- 目前的QTc监测依赖于12导电心电图.
研究的目的:
- 训练和验证使用12导心电图的AI算法来确定QTc.
- 在移动心电图 (mECG) 上前性测试人工智能算法.
- 评估人工智能检测临床相关的QTc延长的能力.
主要方法:
- 一个深度神经网络 (DNN) 在超过160万个12导心电图上进行了训练和验证.
- DNN预测了QTc值,使用心脏病学家的重读值作为黄金标准.
- 预期测试涉及686名患有遗传性心脏病的患者,使用了12导电图和原型mECG装置.
主要成果:
- 在验证组中,AI DNN与人类读取的QTc值表现出强烈一致.
- 与12导心电图相比,mECG的DNN预测的QTc显示了名义上的差异.
- 在mECG数据上,AI在检测QTc延长方面表现出很高的准确性 (AUC为0. 97,敏感度为80. 0%,特异性为94. 4%).
结论:
- 人工智能DNN可以使用智能手机支持的电极从标准的12导电图中准确预测QTc.
- 支持人工智能的mECG设备提供了具有成本效益的获得和先天长QT综合征查工具.
- 在缺乏可访问或成本效益的12导电图的环境中,这种技术可能是有价值的.
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