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

Smith, Stephen W; Rapin, Jeremy; Li, Jia; Fleureau, Yann; Fennell, William; Walsh, Brooks M; Rosier, Arnaud; Fiorina, Laurent; Gardella, Christophe

Smith, Stephen W; Rapin, Jeremy; Li, Jia; Fleureau, Yann; Fennell, William; Walsh, Brooks M; Rosier, Arnaud; Fiorina, Laurent; Gardella, Christophe.

Afiliação

Smith SW; Hennepin County Medical Center, Department of Emergency Medicine, University of Minnesota, United States of America.
Rapin J; Cardiologs® Technologies, Paris, France.
Li J; Cardiologs® Technologies, Paris, France.
Fleureau Y; Cardiologs® Technologies, Paris, France.
Fennell W; University College, Cork, Ireland.
Walsh BM; Dept of Emergency Medicine, Bridgeport Hospital, Bridgeport, CT, United States of America.
Rosier A; Service de rythmologie, Hôpital privé Jacques Cartier, Groupe GDS, Massy, France.
Fiorina L; Hopital privé Jacques Cartier, Massy, France.
Gardella C; Cardiologs® Technologies, Paris, France.

Int J Cardiol Heart Vasc ; 25: 100423, 2019 Dec.

Article em En | MEDLINE | ID: mdl-31517038

Palavras-chave

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

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article

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