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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ID: 44843
2019
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Abstract
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.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.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) ( < 0.0001), and equal to Veritas® + physician (90.0%, CI:87.1-92.3) ( = 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;  < 0.0001). The second database had 39 AF cases; sensitivity was 92% vs. 87% ( = 0.46) and specificity was 99.5% vs. 98.7% ( = 0.03) for Cardiologs® and Veritas® respectively.Cardiologs® 12-lead ECG algorithm improves the interpretation of atrial fibrillation.
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smith2019ainternational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Smith, Stephen W;Rapin, Jeremy;Li, Jia;Fleureau, Yann;Fennell, William;Walsh, Brooks M;Rosier, Arnaud;Fiorina, Laurent;Gardella, Christophe;
Journal international journal of cardiology heart & vasculature
Year 2019
DOI
10.1016/j.ijcha.2019.100423
URL
Keywords
artificial intelligence Atrial fibrillation electrocardiogram deep neural network ad, atrial dysrhythmia af, atrial fibrillation afl, atrial flutter at, atrial tachycardia atrial dysrhythmia dnn, deep neural network ecg, electrocardiogram ed, emergency department hcp, health care provider

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