Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram
Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram
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DOI：
10.1038/s41591-018-0240-2
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发表时间：
2019-01-01
期刊：
影响因子：
82.9
通讯作者：
Friedman, Paul A.
中科院分区：
文献类型：
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作者：
Attia, Zachi I.;Kapa, Suraj;Friedman, Paul A.
无症状性左心室功能障碍（ALVD）存在于3-6%的普通人群中，与生活质量和寿命降低相关，并且在发现时是可以治疗的（1-4）。在医生的办公室里没有一种便宜的、非侵入性的ALVD筛查工具。我们测试了将人工智能（AI）应用于心电图（ECG）（一种测量心脏电活动的常规方法）可以识别ALVD的假设。使用配对的12导联ECG和超声心动图数据，包括来自马约诊所44,959名患者的左心室射血分数（收缩功能的测量），我们训练了一个卷积神经网络来识别心室功能障碍患者，定义为射血分数
Asymptomatic left ventricular dysfunction (ALVD) is present in 3-6% of the general population, is associated with reduced quality of life and longevity, and is treatable when found(1-4). An inexpensive, noninvasive screening tool for ALVD in the doctor's office is not available. We tested the hypothesis that application of artificial intelligence (AI) to the electrocardiogram (ECG), a routine method of measuring the heart's electrical activity, could identify ALVD. Using paired 12-lead ECG and echocardiogram data, including the left ventricular ejection fraction (a measure of contractile function), from 44,959 patients at the Mayo Clinic, we trained a convolutional neural network to identify patients with ventricular dysfunction, defined as ejection fraction