Automatic Detection of Secundum Atrial Septal Defect in Children Based on Color Doppler Echocardiographic Images Using Convolutional Neural Networks

Abstract

Secundum atrial septal defect (ASD) is one of the most common congenital heart diseases (CHDs). This study aims to evaluate the feasibility and accuracy of automatic detection of ASD in children based on color Doppler echocardiographic images using convolutional neural networks. In this study, we propose a fully automatic detection system for ASD, which includes three stages. The first stage is used to identify four target echocardiographic views (that is, the subcostal view focusing on the atrium septum, the apical four-chamber view, the low parasternal four-chamber view, and the parasternal short-axis view). These four echocardiographic views are most useful for the diagnosis of ASD clinically. The second stage aims to segment the target cardiac structure and detect candidates for ASD. The third stage is to infer the final detection by utilizing the segmentation and detection results of the second stage. The proposed ASD detection system was developed and validated using a training set of 4,031 cases containing 370,057 echocardiographic images and an independent test set of 229 cases containing 203,619 images, of which 105 cases with ASD and 124 cases with intact atrial septum. Experimental results showed that the proposed ASD detection system achieved accuracy, recall, precision, specificity, and F1 score of 0.8833, 0.8545, 0.8577, 0.9136, and 0.8546, respectively on the image-level averages of the four most clinically useful echocardiographic views. The proposed system can automatically and accurately identify ASD, laying a good foundation for the subsequent artificial intelligence diagnosis of CHDs.

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Artificial Intelligence in Congenital Heart Disease

“…Lacks external validation and calibration from a different center. Hong et al 34 2022 Color Doppler echocardiogram images CNN for classification and segmentation 4,031 cases with 370,057 images 229 cases with 203,619 images of which 105 cases with ASD and 124 with intact atrial septum Accuracy, recall, precision, specificity, and F1 score of 0.8833, 0.8545, 0.8577, 0.9136, and 0.8546, respectively Not generalizable to spectrum of CHD; single center. Cardiac imaging Pereira et al 35 2017 90 patients; 26 coarctation and 64 healthy 2D echocardiograms of the parasternal long axis, apical 4-chamber, and suprasternal notch views SVM (support vector machine classifiers) Trained on 80% Tested on 20% Total error rate of 12.9% (11.5% false negative error and 13.6% false positive) Single-center study.…”