Assessing and Mitigating Bias in Medical Artificial Intelligence The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis
Assessing and Mitigating Bias in Medical Artificial Intelligence The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis
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DOI：
10.1161/circep.119.007988
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发表时间：
2020-03-01
影响因子：
8.4
通讯作者：
Lopez-Jimenez, Francisco
中科院分区：
文献类型：
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作者：
Noseworthy, Peter A.;Attia, Zachi, I;Lopez-Jimenez, Francisco
背景：在同质人群中衍生的深度学习算法可能难以推广，并且有可能反映、延续甚至加剧健康和医疗保健方面的种族/民族差异。在这项研究中，我们的目的是（1）评估设计用于使用12导联ECG检测低左心室射血分数的深度学习算法的性能是否因种族/种族而异，以及（2）确定其性能是否由推导人群或种族差异决定。方法：我们进行了一项回顾性队列分析，其中包括97829例配对ECG和超声心动图的患者。我们通过种族/民族测试了卷积神经网络的模型性能，该网络旨在识别具有左心室射血分数的患者
Background:Deep learning algorithms derived in homogeneous populations may be poorly generalizable and have the potential to reflect, perpetuate, and even exacerbate racial/ethnic disparities in health and health care. In this study, we aimed to (1) assess whether the performance of a deep learning algorithm designed to detect low left ventricular ejection fraction using the 12-lead ECG varies by race/ethnicity and to (2) determine whether its performance is determined by the derivation population or by racial variation in the ECG.Methods:We performed a retrospective cohort analysis that included 97 829 patients with paired ECGs and echocardiograms. We tested the model performance by race/ethnicity for convolutional neural network designed to identify patients with a left ventricular ejection fraction