A method to screen left ventricular dysfunction through ECG based on convolutional neural network.

Objective: This study aims to develop an artificial intelligence‐based method to screen patients with left ventricular ejection fraction (LVEF) of 50% or lesser using electrocardiogram (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 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 data set 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 data set (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 noninvasive method to identify patients with left ventricular dysfunction. [ABSTRACT FROM AUTHOR]