A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation.

Stephen W Smith¹, Brooks Walsh², Ken Grauer³, Kyuhyun Wang⁴, Jeremy Rapin⁵, Jia Li⁵, William Fennell⁶, Pierre Taboulet⁷

¹Department of Emergency Medicine, Hennepin County Medical Center, Minneapolis, MN, USA; University of Minnesota, Department of Emergency Medicine, USA. Electronic address: smith253@umn.edu.

²Bridgeport Hospital, Bridgeport, CT, USA.

³College of Medicine, University of Florida, USA.

⁴University of Minnesota, Department of Medicine, Division of Cardiology, USA.

⁵Cardiologs® Technologies, Paris, France.

⁶Department of Cardiology, University College, Cork, Ireland.

⁷Cardiologs® Technologies, Paris, France; Department of Emergency Medicine, Hôpital Saint Louis, Assistance Publique-Hôpitaux de Paris, Paris, France.

PubMed DOI

Abstract

BACKGROUND: Cardiologs® has developed the first electrocardiogram (ECG) algorithm that uses a deep neural network (DNN) for full 12‑lead ECG analysis, including rhythm, QRS and ST-T-U waves. We compared the accuracy of the first version of Cardiologs® DNN algorithm to the Mortara/Veritas® conventional algorithm in emergency department (ED) ECGs. METHODS: Individual ECG diagnoses were prospectivel…

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