Development and Validation of a Deep-Learning Network for Detecting Congenital Heart Disease from Multi-View Multi-Modal Transthoracic Echocardiograms.

Journal: Research (Washington, D.C.)

Volume: 7

Issue: 

Year of Publication: 

Affiliated Institutions:  Department of Intelligent Medical Engineering, School of Biomedical Engineering, Department of Psychology, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei , China. Heart Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing , China. Gordon Center for Medical Imaging, Harvard Medical School, and Massachusetts General Hospital, Boston, MA , USA. School of Life Course and Population Sciences, Faculty of Life Science and Medicine, King's College London, London, UK. Center for Genetics, National Research Institute for Family Planning, Beijing , China.

Abstract summary 

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.

Authors & Co-authors:  Cheng Wang Liu Wang Wu Wang Li Wang Zhang Xie

Study Outcome 

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Statistics
Citations :  Zhao L, Chen L, Yang T, Wang T, Zhang S, Chen L, Ye Z, Luo L, Qin J. Birth prevalence of congenital heart disease in China, 1980–2019: A systematic review and meta-analysis of 617 studies. Eur J Epidemiol. 2020;35(7):631–642.
Authors :  10
Identifiers
Doi : 0319
SSN : 2639-5274
Study Population
Male,Female
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Other Terms
Study Design
Study Approach
Country of Study
Publication Country
United States