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

Abstract

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.

Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects

“…18 The positive and negative predictive values of the model were 70.1% and 69.9%, respectively. 18 AI algorithms have also been developed and validated for risk stratification and predicting mortality, medication adherence and recurrent hospitalizations in patients with HF. An ANN studied by Ortiz et al in 95 patients found the AI tool was able to predict 1-year all-cause mortality in patients with HFrEF with an accuracy of 90%, specificity of 93% and a sensitivity of 71.4%.…”

“…18 The authors found the CNN-based model detected LV dysfunction with an accuracy of 73.9%, sensitivity of 69.2%, specificity of 70.5% and AUC of 0.713. 18 The positive and negative predictive values of the model were 70.1% and 69.9%, respectively. 18 AI algorithms have also been developed and validated for risk stratification and predicting mortality, medication adherence and recurrent hospitalizations in patients with HF.…”

“…Sun et al developed a CNN to identify LV dysfunction and used 12,732 ECG-TTE pairs for training, 2530 pairs for validation and 2530 pairs for testing. 18 The authors found the CNN-based model detected LV dysfunction with an accuracy of 73.9%, sensitivity of 69.2%, specificity of 70.5% and AUC of 0.713. 18 The positive and negative predictive values of the model were 70.1% and 69.9%, respectively.…”

“… 18 The authors found the CNN-based model detected LV dysfunction with an accuracy of 73.9%, sensitivity of 69.2%, specificity of 70.5% and AUC of 0.713. 18 The positive and negative predictive values of the model were 70.1% and 69.9%, respectively. 18 …”

Deep-Learning-Based Detection of Paroxysmal Supraventricular Tachycardia Using Sinus-Rhythm Electrocardiograms

“…The application of a CNN to an ECG can predict cardiovascular diseases and even non-cardiovascular diseases such as serum potassium aberrations, anemia and sleep apnea [ 13 , 14 , 15 , 16 , 17 ]. CNN-enabled ECGs could identify patients with left ventricle dysfunction using a threshold of LVEF ≤ 35% or 50% according to transthoracic echocardiogram [ 18 , 19 ]. A previous study also reported that a well-trained CNN could detect patients with the electrocardiographic signature of atrial fibrillation present during normal sinus rhythm, which has practical implications for atrial fibrillation screening [ 20 ].…”