Automatic Triage of 12-Lead ECGs Using Deep Convolutional Neural Networks.

Content Provider	Europe PMC
Author	van de Leur, Rutger R. Blom, Lennart J. Gavves, Efstratios Hof, Irene E. van der Heijden, Jeroen F. Clappers, Nick C. Doevendans, Pieter A. Hassink, Rutger J. van Es, René
Abstract	BACKGROUNDThe correct interpretation of the ECG is pivotal for the accurate diagnosis of many cardiac abnormalities, and conventional computerized interpretation has not been able to reach physician‐level accuracy in detecting (acute) cardiac abnormalities. This study aims to develop and validate a deep neural network for comprehensive automated ECG triage in daily practice.METHODS AND RESULTSWe developed a 37‐layer convolutional residual deep neural network on a data set of free‐text physician‐annotated 12‐lead ECGs. The deep neural network was trained on a data set with 336.835 recordings from 142.040 patients and validated on an independent validation data set (n=984), annotated by a panel of 5 cardiologists electrophysiologists. The 12‐lead ECGs were acquired in all noncardiology departments of the University Medical Center Utrecht. The algorithm learned to classify these ECGs into the following 4 triage categories: normal, abnormal not acute, subacute, and acute. Discriminative performance is presented with overall and category‐specific concordance statistics, polytomous discrimination indexes, sensitivities, specificities, and positive and negative predictive values. The patients in the validation data set had a mean age of 60.4 years and 54.3% were men. The deep neural network showed excellent overall discrimination with an overall concordance statistic of 0.93 (95% CI, 0.92–0.95) and a polytomous discriminatory index of 0.83 (95% CI, 0.79–0.87).CONCLUSIONSThis study demonstrates that an end‐to‐end deep neural network can be accurately trained on unstructured free‐text physician annotations and used to consistently triage 12‐lead ECGs. When further fine‐tuned with other clinical outcomes and externally validated in clinical practice, the demonstrated deep learning–based ECG interpretation can potentially improve time to treatment and decrease healthcare burden.
Page Count	11
Journal	Journal of the American Heart Association
Volume Number	9
PubMed Central reference number	PMC7660886
Issue Number	10
PubMed reference number	32406296
e-ISSN	20479980
DOI	10.1161/jaha.119.015138
Language	English
Publisher	John Wiley and Sons Inc.
Publisher Date	2020-05-14
Publisher Place	Hoboken
Access Restriction	Open
Rights License	This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. © 2020 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.
Subject Keyword	deep learning deep neural networks electrocardiography triage Arrhythmias Electrophysiology Ventricular Fibrillation Epidemiology Electrocardiology (ECG)
Content Type	Text
Resource Type	Article
Subject	Cardiology and Cardiovascular Medicine