AUTHOR=Jiang Junrong , Deng Hai , Xue Yumei , Liao Hongtao , Wu Shulin 

TITLE=Detection of Left Atrial Enlargement Using a Convolutional Neural Network-Enabled Electrocardiogram

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 7 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2020.609976

DOI=10.3389/fcvm.2020.609976

ISSN=2297-055X

ABSTRACT=Background Left atrial enlargement (LAE) can independently predict the development of a variety of cardiovascular diseases.
Objectives This study sought to develop an artificial intelligence approach for the detection of LAE based on 12-lead electrocardiography (ECG).
Methods The study population came from an epidemiological survey of heart disease in Guangzhou. 3391 elderly people over 65 years old who had both 10 seconds 12 lead ECG and echocardiography were enrolled in this study. The left atrial (LA) anteroposterior diameter > 40mm on echocardiography was diagnosed as LAE. And the LA anteroposterior diameter was indexed by body surface area (BSA) to classify LAE into different degrees. A convolutional neural network (CNN) was trained and validated to detect LAE from normal ECGs. The performance of the model was evaluated by calculating the area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score.
Results In this study, gender, obesity, hypertension, and valvular heart disease seemed to be related to left atrial enlargement. The AI-enabled ECG identified LAE with an AUC of 0.949 (95% CI: 0.911-0.987). The sensitivity, specificity, accuracy, precision and F1 score were 84.0%, 92.0%, 88.0%, 91.3% and 0.875, respectively. Physicians identified LAE with sensitivity, specificity, accuracy, precision, and F1 scores of 38.0%, 84.0%, 61.0%, 70.4%, and 0.494, respectively. In classifying LAE in different degrees, the AUCs of identifying normal, mild LAE and moderate-severe LAE ECGs were 0.942 (95% CI: 0.903 to 0.981), 0.951 (95% CI: 0.917 to 0.987), 0.998 (95% CI: 0.996 to 1.00), respectively. The sensitivity, specificity, accuracy, positive predictive value and F1 scores of diagnosing mild LAE were 82.0%, 92.0%, 88.7%, 89.1% and 0.854, while the sensitivity, specificity, accuracy, positive predictive value and F1 scores of diagnosing moderate-severe LAE were 98.0%, 84.0%, 88.7%, 96.1% and 0.969, respectively.
Conclusions An AI-enabled ECG acquired during sinus rhythm permits identification of individuals with a high likelihood of LAE. This model requires further refinement and external validation, but it may hold promise for LAE screening.