Intelligent diagnosis of atrial septal defect in children using echocardiography with deep learning

Background: Atrial septal defect (ASD) is one of the most common congenital heart diseases. The diagnosis of ASD via transthoracic echocardiography is subjective and time-consuming. Methods: The objective of this study was to evaluate the feasibility and accuracy of automatic detection of ASD in children based on color Doppler echocardiographic static images using end-to-end convolutional neural networks. The proposed depthwise separable convolution model identifies ASDs with static color Doppler images in a standard view. Among the standard views, we selected two echocardiographic views, i.e., the subcostal sagittal view of the atrium septum and the low parasternal four-chamber view. The developed ASD detection system was validated using a training set consisting of 396 echocardiographic images corresponding to 198 cases. Additionally, an independent test dataset of 112 images corresponding to 56 cases was used, including 101 cases with ASDs and 153 cases with normal hearts. Results: The average area under the receiver operating characteristic curve, recall, precision, specificity, F1-score, and accuracy of the proposed ASD detection model were 91.99, 80.00, 82.22, 87.50, 79.57, and 83.04, respectively. Conclusions: The proposed model can accurately and automatically identify ASD, providing a strong foundation for the intelligent diagnosis of congenital heart diseases.