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

Stephen W Smith¹, Jeremy Rapin², Jia Li², Yann Fleureau², William Fennell³, Brooks M Walsh⁴, Arnaud Rosier⁵, Laurent Fiorina⁶, Christophe Gardella²

¹Hennepin County Medical Center, Department of Emergency Medicine, University of Minnesota, United States of America.

²Cardiologs® Technologies, Paris, France.

³University College, Cork, Ireland.

⁴Dept of Emergency Medicine, Bridgeport Hospital, Bridgeport, CT, United States of America.

⁵Service de rythmologie, Hôpital privé Jacques Cartier, Groupe GDS, Massy, France.

⁶Hopital privé Jacques Cartier, Massy, France.

PubMed PMC DOI PDF

Abstract

BACKGROUND: Automated electrocardiogram (ECG) interpretations may be erroneous, and lead to erroneous overreads, including for atrial fibrillation (AF). We compared the accuracy of the first version of a new deep neural network 12-Lead ECG algorithm (Cardiologs®) to the conventional Veritas algorithm in interpretation of AF. METHODS: 24,123 consecutive 12-lead ECGs recorded over 6 months were int…

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