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Updated: Jul 1, 2025
Murine Fetal Echocardiography
Published on: February 15, 2013
Development and Validation of a Deep-Learning Network for Detecting Congenital Heart Disease from Multi-View
Mingmei Cheng1, Jing Wang2,3, Xiaofeng Liu4
1Department of Intelligent Medical Engineering, School of Biomedical Engineering, Department of Psychology, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230011, China.
Insights
A deep learning model accurately detects congenital heart disease (CHD) in children using transthoracic echocardiograms (TTEs). This AI approach integrates multiple views and data types, improving diagnostic accuracy and simplifying screening for atrial septal defects (ASDs) and ventricular septal defects (VSDs).
Area of Science:
- Medical Imaging Analysis
- Artificial Intelligence in Healthcare
- Pediatric Cardiology
Background:
- Early detection of congenital heart disease (CHD) is crucial for improving pediatric outcomes.
- Inexperienced sonographers often struggle with accurate CHD identification via transthoracic echocardiogram (TTE).
- Current diagnostic methods can be challenging, necessitating advanced tools for improved accuracy.
Purpose of the Study:
- To develop and evaluate a deep learning (DL) framework for automated CHD detection in children.
- To integrate multi-view and multi-modal TTE data for enhanced diagnostic performance.
- To predict the probability of normal cardiac function, atrial septal defect (ASD), or ventricular septal defect (VSD).
Main Methods:
- Analysis of 2D and Doppler TTEs from 1,932 children (2018-2022).
- Development of a DL framework to identify cardiac views, integrate information, and predict CHD presence.
- Utilized five standard echocardiographic views: apical 4 chamber, subxiphoid, parasternal long-axis, parasternal short-axis, and suprasternal long-axis.
Main Results:
- The DL model achieved high accuracy in cardiac view classification (0.989).
- For CHD screening, the model demonstrated excellent performance with AUCs of 0.996 (within-center) and 0.990 (cross-center).
- Classification accuracy for healthy, ASD, and VSD was high, reaching 0.991 (within-center) and 0.986 (cross-center).
Conclusions:
- Deep learning models integrating multiple TTE views and modalities significantly enhance CHD detection accuracy in children.
- The developed DL framework shows potential to approximate the performance of experienced sonographers.
- This noninvasive approach can improve CHD screening efficiency and accuracy, aiding early diagnosis and treatment.
Abstract:
Early detection and treatment of congenital heart disease (CHD) can significantly improve the prognosis of children. However, inexperienced sonographers often face difficulties in recognizing CHD through transthoracic echocardiogram (TTE) images. In this study, 2-dimensional (2D) and Doppler TTEs of children collected from 2 clinical groups from Beijing Children's Hospital between 2018 and 2022 were analyzed, including views of apical 4 chamber, subxiphoid long-axis view of 2 atria, parasternal long-axis view of the left ventricle, parasternal short-axis view of aorta, and suprasternal long-axis view. A deep learning (DL) framework was developed to identify cardiac views, integrate information from various views and modalities, visualize the high-risk region, and predict the probability of the subject being normal or having an atrial septal defect (ASD) or a ventricular septaldefect (VSD). A total of 1,932 children (1,255 healthy controls, 292 ASDs, and 385 VSDs) were collected from 2 clinical groups. For view classification, the DL model reached a mean [SD] accuracy of 0.989 [0.001]. For CHD screening, the model using both 2D and Doppler TTEs with 5 views achieved a mean [SD] area under the receiver operating characteristic curve (AUC) of 0.996 [0.000] and an accuracy of 0.994 [0.002] for within-center evaluation while reaching a mean [SD] AUC of 0.990 [0.003] and an accuracy of 0.993 [0.001] for cross-center test set. For the classification of healthy, ASD, and VSD, the model reached the mean [SD] accuracy of 0.991 [0.002] and 0.986 [0.001] for within- and cross-center evaluation, respectively. The DL models aggregating TTEs with more modalities and scanning views attained superior performance to approximate that of experienced sonographers. The incorporation of multiple views and modalities of TTEs in the model enables accurate identification of children with CHD in a noninvasive manner, suggesting the potential to enhance CHD detection performance and simplify the screening process.
