A multi-input deep learning model using 12-lead ECG data detected significant aortic regurgitation with an AUC of 0.802 (95% CI 0.762-0.837), significantly outperforming a 2D-CNN model (P<0.001).
Cross-Sectional (n=29,859)
Does a multi-input deep learning model using 12-lead ECG data improve the detection of significant aortic regurgitation compared to a 2D-CNN model alone?
A multi-input deep learning model using 12-lead ECG data can detect significant aortic regurgitation with modest predictive value, outperforming a standard 2D-CNN model.
Effect estimate: AUC 0.802 (95% CI 0.762-0.837)
Absolute Event Rate: 0.802% vs 0.734%
p-value: p=<0.001
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 multi-input 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.
Sawano et al. (Fri,) conducted a cross-sectional in Aortic regurgitation (n=29,859). Multi-input deep learning model (2D-CNN and FC-DNN) vs. 2D-CNN model alone and other machine learning models was evaluated on Detection of significant aortic regurgitation (AUC) (AUC 0.802, 95% CI 0.762-0.837, p=<0.001). A multi-input deep learning model using 12-lead ECG data detected significant aortic regurgitation with an AUC of 0.802 (95% CI 0.762-0.837), significantly outperforming a 2D-CNN model (P<0.001).
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