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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Published version
Date
2019-09-08
Authors
Smith, Stephen W.
Rapin, Jeremy
Li, Jia
Fleureau, Yann
Fennell, William
Walsh, Brooks M.
Rosier, Arnaud
Fiorina, Laurent
Gardella, Christophe
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ireland Ltd
Published Version
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.
Description
Keywords
Deep neural network , Artificial intelligence , Artificial fibrillation , Atrial dysrhythmia , Electrocardiogram
Citation
Smith, S. W., Rapin, J., Li, J., Fleureau, Y., Fennell, W., Walsh, B. M., Rosier, A., Fiorina, L. and Gardella, C. (2019) 'A deep neural network for 12-lead electrocardiogram interpretation outperforms a conventional algorithm, and its physician overread, in the diagnosis of atrial fibrillation', IJC Heart & Vasculature, 25,100423 (6pp.). DOI: 10.1016/j.ijcha.2019.100423