A deep neural network for 12-lead electrocardiogram interpretation outperforms a conventional algorithm, and its physician overread, in the diagnosis of atrial fibrillation.

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作者： Stephen W,Smith ^[1] ; Jeremy,Rapin ^[2] ; Jia,Li ^[2] ; Yann,Fleureau ^[2] ; William,Fennell ^[3] ; Brooks M,Walsh ^[4] ; Arnaud,Rosier ^[5] ; Laurent,Fiorina ^[6] ; Christophe,Gardella ^[2]

作者单位： Hennepin County Medical Center, Department of Emergency Medicine, University of Minnesota, United States of America. ^[1] Cardiologs® Technologies, Paris, France. ^[2] University College, Cork, Ireland. ^[3] Dept of Emergency Medicine, Bridgeport Hospital, Bridgeport, CT, United States of America. ^[4] Service de rythmologie, Hôpital privé Jacques Cartier, Groupe GDS, Massy, France. ^[5] Hopital privé Jacques Cartier, Massy, France. ^[6]

关键词 AD, atrial dysrhythmia AF, atrial fibrillation AFL, atrial flutter AT, atrial tachycardia Artificial intelligence Atrial dysrhythmia Atrial fibrillation DNN, deep neural network Deep neural network ECG, electrocardiogram ED, emergency department Electrocardiogram HCP, health care provider

DOI 10.1016/j.ijcha.2019.100423

PMID 31517038

发布时间 2022-04-10

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International journal of cardiology. Heart & vasculature

2019年25卷

100423页

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A deep neural network for 12-lead electrocardiogram interpretation outperforms a conventional algorithm, and its physician overread, in the diagnosis of atrial fibrillation.

International journal of cardiology. Heart & vasculature

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International journal of cardiology. Heart & vasculature

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A deep neural network for 12-lead electrocardiogram interpretation outperforms a conventional algorithm, and its physician overread, in the diagnosis of atrial fibrillation.

International journal of cardiology. Heart & vasculature

相似文献

International journal of cardiology. Heart & vasculature

相关期刊

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