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

doi:10.1016/j.ijcha.2019.100423

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

Int J Cardiol Heart Vasc. 2019 Sep 8:25:100423. doi: 10.1016/j.ijcha.2019.100423. eCollection 2019 Dec.

Authors

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

Affiliations

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

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 interpreted by 1) the Veritas® algorithm, 2) physicians who overread Veritas® (Veritas® + physician), and 3) Cardiologs® algorithm. We randomly selected 500 out of 858 ECGs with a diagnosis of AF according to either algorithm, then compared the algorithms' interpretations, and Veritas® + physician, with expert interpretation. To assess sensitivity for AF, we analyzed a separate database of 1473 randomly selected ECGs interpreted by both algorithms and by blinded experts.

Results: Among the 500 ECGs selected, 399 had a final classification of AF; 101 (20.2%) had ≥1 false positive automated interpretation. Accuracy of Cardiologs® (91.2%; CI: 82.4-94.4) was higher than Veritas® (80.2%; CI: 76.5-83.5) (p < 0.0001), and equal to Veritas® + physician (90.0%, CI:87.1-92.3) (p = 0.12). When Veritas® was incorrect, accuracy of Veritas® + physician was only 62% (CI 52-71); among those ECGs, Cardiologs® accuracy was 90% (CI: 82-94; p < 0.0001). The second database had 39 AF cases; sensitivity was 92% vs. 87% (p = 0.46) and specificity was 99.5% vs. 98.7% (p = 0.03) for Cardiologs® and Veritas® respectively.

Conclusion: Cardiologs® 12-lead ECG algorithm improves the interpretation of atrial fibrillation.

Keywords: 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.