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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.<\/jats:p>","DOI":"10.34133\/research.0319","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T06:57:44Z","timestamp":1706684264000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":20,"title":["Development and Validation of a Deep-Learning Network for Detecting Congenital Heart Disease from Multi-View Multi-Modal Transthoracic Echocardiograms"],"prefix":"10.34133","volume":"7","author":[{"given":"Mingmei","family":"Cheng","sequence":"first","affiliation":[{"name":"Department of Intelligent Medical Engineering, School of Biomedical Engineering, Department of Psychology, School of Mental Health and Psychological Sciences, \rAnhui Medical University, Hefei 230011, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"Heart Center, Beijing Children\u2019s Hospital, Capital Medical University, National Center for Children\u2019s Health, Beijing 10045, China."},{"name":"School of Basic Medical Sciences, Capital Medical University, Beijing 10069, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Gordon Center for Medical Imaging, Harvard Medical School, and Massachusetts General Hospital, Boston, MA 02114, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanzhong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Life Course and Population Sciences, Faculty of Life Science and Medicine, King\u2019s College London, London, UK."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qun","family":"Wu","sequence":"additional","affiliation":[{"name":"Heart Center, Beijing Children\u2019s Hospital, Capital Medical University, National Center for Children\u2019s Health, Beijing 10045, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangyun","family":"Wang","sequence":"additional","affiliation":[{"name":"Heart Center, Beijing Children\u2019s Hospital, Capital Medical University, National Center for Children\u2019s Health, Beijing 10045, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pei","family":"Li","sequence":"additional","affiliation":[{"name":"Heart Center, Beijing Children\u2019s Hospital, Capital Medical University, National Center for Children\u2019s Health, Beijing 10045, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binbin","family":"Wang","sequence":"additional","affiliation":[{"name":"Center for Genetics, \rNational Research Institute for Family Planning, Beijing 100730, China."},{"name":"Graduated School, \rPeking Union Medical College, Beijing 100730, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Heart Center, Beijing Children\u2019s Hospital, Capital Medical University, National Center for Children\u2019s Health, Beijing 10045, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanqing","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Intelligent Medical Engineering, School of Biomedical Engineering, Department of Psychology, School of Mental Health and Psychological Sciences, \rAnhui Medical University, Hefei 230011, China."},{"name":"Beth Israel Deaconess Medical Center, Harvard Medical School, \rHarvard University, Boston, MA 02215, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2024,3,6]]},"reference":[{"issue":"7","key":"e_1_3_4_2_2","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1007\/s10654-020-00653-0","article-title":"Birth prevalence of congenital heart disease in China, 1980\u20132019: A systematic review and meta-analysis of 617 studies","volume":"35","author":"Zhao L","year":"2020","unstructured":"Zhao L, Chen L, Yang T, Wang T, Zhang S, Chen L, Ye Z, Luo L, Qin J. 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