Deep learning model to detect significant aortic regurgitation using electrocardiography

Abstract

Background: Aortic regurgitation (AR) is a common heart disease, with a relatively high prevalence of 4.9% in the Framingham Heart Study. Because the prevalence increases with advancing age, an upward shift in the age distribution may increase the burden of AR. To provide an effective screening method for AR, we developed a deep learning-based artificial intelligence algorithm for the diagnosis of significant AR using electrocardiography (ECG). Methods: Our dataset comprised 29,859 paired data of ECG and echocardiography, including 412 AR cases, from January 2015 to December 2019. This dataset was divided into training, validation, and test datasets. We developed a multi-input neural network model, which comprised a two-dimensional convolutional neural network (2D-CNN) using raw ECG data and a fully connected deep neural network (FC-DNN) using ECG features, and compared its performance with the performances of a 2D-CNN model and other machine learning models. In addition, we used gradient-weighted class activation mapping (Grad-CAM) to identify which parts of ECG waveforms had the most effect on algorithm decision making. Results: The area under the receiver operating characteristic curve of the multi-input model (0.802; 95% CI, 0.762-0.837) was significantly greater than that of the 2D-CNN model alone (0.734; 95% CI, 0.679-0.783; p < 0.001) and those of other machine learning models. Grad-CAM demonstrated that the multiinput model tended to focus on the QRS complex in leads I and aVL when detecting AR. Conclusions: The multi-input deep learning model using 12-lead ECG data could detect significant AR with modest predictive value.

Comprehensive clinical application analysis of artificial intelligence-enabled electrocardiograms for screening multiple valvular heart diseases

“…This study achieved state-of-the-art performance in detecting aortic stenosis,(8, 9) aortic regurgitation, (10) and mitral regurgitation(11) compared to recently published retrospective studies using the same conditions. Moreover, we also demonstrated the feasibility of using AI-ECG for detecting pulmonary regurgitation (AUC > 0.77) and tricuspid regurgitation (AUC > 0.83), and all DLMs were not worse than the screening tests already implemented on a large scale, such as breast cancer screening (AUC = 0.78)(16)…”

“…Previous studies have developed DLMs for detecting aortic stenosis with an AUC > 0.86 using 12-lead ECG and demography;(8, 9) aortic regurgitation with an AUC > 0.80 using 12-lead ECG and demography; (10) and mitral regurgitation with an AUC > 0.81 using 12-lead ECG. (11) However, the low positive predictive value, which may cause anxiety and inconvenience for patients, was the major concern for direct application of these DLMs in clinical practice.…”

Automatic Detection of Left Ventricular Dilatation and Hypertrophy from Electrocardiograms Using Deep Learning

“…We implemented a multi-input neural network in Python version 3.7.4 17) using the open-source PyTorch version 1.6.0 deep learning library and the NVIDIA Tesla V100 32 Gb graphics processing unit (NVIDIA Corporation, Santa Clara, CA, USA). For all 3 models, the following parameters were used: loss function, binary cross-entropy with logits loss (BCEwithLogitsLoss); optimizer, Adam; learning rate, 0.00005; and batch size, 128 (based on the grid search of previous studies 13,14) ). Machine learning models: Logistic regression and random forest methods, which have been used in previous studies, 18,19) were used as machine learning models.…”

“…Deep learning can detect significant aortic regurgitation and left ventricular systolic dysfunction from a 12-lead ECG. 13,14) However, there are no established LVD diagnostic criteria or screening methods based on ECG data, and the usefulness of deep learning for the detection of LVD has not been investigated. For the detection of LVH, a detailed comparison of deep learning with conventional criteria and other machine learning methods needs to be made.…”

Age prediction from coronary angiography using a deep neural network: Age as a potential label to extract prognosis-related imaging features

“…Although coronary artery calcium (CAC) scoring by computed tomography (CT) [ 6 ] and carotid intima-media thickness (CIMT) [ 7 ] assessment by B-mode ultrasonography can be used to define vascular age [ 8 ], it is not clearly known whether CAG contains useful age-related imaging features. Recently, deep neural networks (DNNs) have been utilized to analyse various types of images [ 1 , 9 ], including data interpretation that is difficult for humans, such as predicting age and gender from electrocardiograms [ 10 ]. The purpose of this study was to develop a deep learning neural network to estimate vascular age based on coronary angiographic imaging and to examine the clinical usefulness of this age prediction.…”