The Effectiveness of a Deep Learning Model to Detect Left Ventricular Systolic Dysfunction from Electrocardiograms

Deep learning models can be applied to electrocardiograms (ECGs) to detect left ventricular (LV) dysfunc-tion. We hypothesized that applying a deep learning model may improve the diagnostic accuracy of cardiolo-gists in predicting LV dysfunction from ECGs. We acquired 37,103 paired ECG and echocardiography data re-cords of patients who underwent echocardiography between January 2015 and December 2019. We trained a convolutional neural network to identify the data records of patients with LV dysfunction (ejection fraction < 40%) using a dataset of 23,801 ECGs. When tested on an independent set of 7,196 ECGs, we found the area under the receiver operating characteristic curve was 0.945 (95% confidence interval: 0.936-0.954). When 7 car-diologists interpreted 50 randomly selected ECGs from the test dataset of 7,196 ECGs, their accuracy for pre-dicting LV dysfunction was 78.0% +/- 6.0%. By referring to the model's output, the cardiologist accuracy im -proved to 88.0% +/- 3.7%, which indicates that model support significantly improved the cardiologist diagnostic accuracy (P = 0.02). A sensitivity map demonstrated that the model focused on the QRS complex when detect-ing LV dysfunction on ECGs. We developed a deep learning model that can detect LV dysfunction on ECGs with high accuracy. Furthermore, we demonstrated that support from a deep learning model can help cardiolo-gists to identify LV dysfunction on ECGs.