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In Journal of cardiovascular electrophysiology

OBJECTIVE : This study aims to develop an artificial intelligence-based method to screen patients with left ventricular ejection fraction (LVEF) of ≤ 50% using ECG data alone.

METHODS : Convolutional neural network (CNN) is a class of deep neural networks, which has been widely used in medical image recognition. We collected standard 12-lead electrocardiogram (ECG) and transthoracic echocardiogram (TTE) data including the LVEF value. Then, we paired the ECG and TTE data from the same individual. For multiple ECG-TTE pairs from a single individual, only the earliest data pair was included. All the ECG-TTE pairs were randomly divided into the training, validation, or testing dataset in a ratio of 9:1:1 to create or evaluate the CNN model. Finally, we assessed the screening performance by overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.

RESULTS : We retrospectively enrolled a total of 26,786 ECG-TTE pairs and randomly divided them into training (n=21,732), validation (n=2,530), and testing dataset (n=2,530). In the testing set, the CNN algorithm showed an overall accuracy of 73.9%, sensitivity of 69.2%, specificity of 70.5%, positive predictive value of 70.1%, and negative predictive value of 69.9%.

CONCLUSION : Our results demonstrate that a well-trained CNN algorithm may be used as a low-cost and non-invasive method to identify patients with left ventricular dysfunction. This article is protected by copyright. All rights reserved.

Sun Jin-Yu, Qiu Yue, Guo Hong-Cheng, Hua Yang, Shao Bo, Qiao Yu-Cong, Guo Jin, Ding Han-Lin, Zhang Zhen-Ye, Miao Ling-Feng, Wang Ning, Zhang Yu-Min, Chen Yan, Lu Juan, Dai Min, Zhang Chang-Ying, Wang Ru-Xing

2021-Feb-09

Artificial intelligence, Convolutional neural network, Deep learning, Electrocardiogram, Heart failure, Left ventricular ejection fraction