{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T05:09:15Z","timestamp":1790744955334,"version":"4.1.0"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T00:00:00Z","timestamp":1678406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006579","name":"Ministry of Industry and Information Technology of China","doi-asserted-by":"publisher","award":["2020-0103-3-1"],"award-info":[{"award-number":["2020-0103-3-1"]}],"id":[{"id":"10.13039\/501100006579","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82202265"],"award-info":[{"award-number":["82202265"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec><jats:title>Objectives<\/jats:title><jats:p>We developed and tested a deep learning (DL) framework applicable to color Doppler echocardiography for automatic detection and quantification of atrial septal defects (ASDs).<\/jats:p><\/jats:sec><jats:sec><jats:title>Background<\/jats:title><jats:p>Color Doppler echocardiography is the most commonly used non-invasive imaging tool for detection of ASDs. While prior studies have used DL to detect the presence of ASDs from standard 2D echocardiographic views, no study has yet reported automatic interpretation of color Doppler videos for detection and quantification of ASD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A total of 821 examinations from two tertiary care hospitals were collected as the training and external testing dataset. We developed DL models to automatically process color Doppler echocardiograms, including view selection, ASD detection and identification of the endpoints of the atrial septum and of the defect to quantify the size of defect and the residual rim.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The view selection model achieved an average accuracy of 99% in identifying four standard views required for evaluating ASD. In the external testing dataset, the ASD detection model achieved an area under the curve (AUC) of 0.92 with 88% sensitivity and 89% specificity. The final model automatically measured the size of defect and residual rim, with the mean biases of 1.9\u2005mm and 2.2\u2005mm, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We demonstrated the feasibility of using a deep learning model for automated detection and quantification of ASD from color Doppler echocardiography. This model has the potential to improve the accuracy and efficiency of using color Doppler in clinical practice for screening and quantification of ASDs, that are required for clinical decision making.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcvm.2023.985657","type":"journal-article","created":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T05:29:10Z","timestamp":1678426150000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Echocardiography-based AI for detection and quantification of atrial septal defect"],"prefix":"10.3389","volume":"10","author":[{"given":"Xixiang","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feifei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxiu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiushuang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liwei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujiao","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haitao","family":"Pu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaotian","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peifang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Burkhoff","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunlun","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/s2352-4642(19)30402-x","article-title":"Global, regional, and national burden of congenital heart disease, 1990\u20132017: a systematic analysis for the global burden of disease study 2017","volume":"4","author":"Zimmerman","year":"2020","journal-title":"Lancet Child Adolesc Health"},{"key":"B2","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1016\/j.echo.2011.02.008","article-title":"Three-Dimensional transesophageal echocardiography of atrial septal defect: a qualitative and quantitative anatomic study","volume":"24","author":"Roberson","year":"2011","journal-title":"J Am Soc Echocardiogr"},{"key":"B3","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1093\/eurheartj\/ehaa554","article-title":"2020 Esc guidelines for the management of adult congenital heart disease","volume":"42","author":"Baumgartner","year":"2021","journal-title":"Eur Heart J"},{"key":"B4","doi-asserted-by":"publisher","first-page":"2183","DOI":"10.1161\/01.cir.0000437597.44550.5d","article-title":"Diagnosis and treatment of fetal cardiac disease","volume":"129","author":"Donofrio","year":"2014","journal-title":"Circulation"},{"key":"B5","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1016\/j.jcin.2013.05.007","article-title":"Prospective randomized trial of transthoracic echocardiography versus transesophageal echocardiography for assessment and guidance of transcatheter closure of atrial septal defects in children using the amplatzer septal occluder","volume":"6","author":"Bartakian","year":"2013","journal-title":"JACC Cardiovasc Interv"},{"key":"B6","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1016\/j.echo.2015.05.015","article-title":"Guidelines for the echocardiographic assessment of atrial septal defect and patent foramen Ovale: from the American society of echocardiography and society for cardiac angiography and interventions","volume":"28","author":"Silvestry","year":"2015","journal-title":"J Am Soc Echocardiogr"},{"key":"B7","doi-asserted-by":"publisher","first-page":"860","DOI":"10.1111\/1471-0528.14599","article-title":"Who recommendations on antenatal care for a positive pregnancy experience-going beyond survival","volume":"124","author":"un\u00e7alp","year":"2017","journal-title":"BJOG"},{"key":"B8","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1161\/CIRCULATIONAHA.118.034338","article-title":"Fully automated echocardiogram interpretation in clinical practice","volume":"138","author":"Zhang","year":"2018","journal-title":"Circulation"},{"key":"B9","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.jcmg.2019.02.024","article-title":"A deep learning approach for assessment of regional wall motion abnormality from echocardiographic images","volume":"13","author":"Kusunose","year":"2020","journal-title":"JACC Cardiovasc Imaging"},{"key":"B10","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.1161\/CIRCULATIONAHA.120.047530","article-title":"Automated recognition of regional wall motion abnormalities through deep neural network interpretation of transthoracic echocardiography","volume":"142","author":"Huang","year":"2020","journal-title":"Circulation"},{"key":"B11","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1038\/s41586-020-2145-8","article-title":"Video-Based ai for beat-to-beat assessment of cardiac function","volume":"580","author":"Ouyang","year":"2020","journal-title":"Nature"},{"key":"B12","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1016\/j.jcmg.2021.08.015","article-title":"Automated analysis of Doppler echocardiographic videos as a screening tool for valvular heart diseases","volume":"15","author":"Yang","year":"2022","journal-title":"JACC Cardiovasc Imaging"},{"key":"B13","author":"Chollet","year":"2017"},{"key":"B14","author":"Li","year":"2017"},{"key":"B15","year":""},{"key":"B16","author":"Iek","year":""},{"key":"B17","author":"Zhe","year":"2017"},{"key":"B18","author":"Dosovitskiy","year":"2021"},{"key":"B19","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal Fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"Jama"},{"key":"B20","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-Level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"B21","doi-asserted-by":"publisher","first-page":"2388","DOI":"10.1016\/s0140-6736(18)31645-3","article-title":"Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study","volume":"392","author":"Chilamkurthy","year":"2018","journal-title":"Lancet"},{"key":"B22","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.ultrasmedbio.2018.07.024","article-title":"Real-Time standard view classification in transthoracic echocardiography using convolutional neural networks","volume":"45","author":"\u00d8stvik","year":"2019","journal-title":"Ultrasound Med Biol"},{"key":"B23","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1038\/s41746-017-0013-1","article-title":"Fast and accurate view classification of echocardiograms using deep learning","volume":"1","author":"Madani","year":"2018","journal-title":"NPJ Digit Med"},{"key":"B24","doi-asserted-by":"publisher","first-page":"665","DOI":"10.3390\/biom10050665","article-title":"Clinically feasible and accurate view classification of echocardiographic images using deep learning","volume":"10","author":"Kusunose","year":"2020","journal-title":"Biomolecules"},{"key":"B25","doi-asserted-by":"publisher","first-page":"101942","DOI":"10.1016\/j.media.2020.101942","article-title":"Automated interpretation of congenital heart disease from multi-view echocardiograms","volume":"69","author":"Wang","year":"2021","journal-title":"Med Image Anal"},{"key":"B26","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1038\/s41591-021-01342-5","article-title":"An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease","volume":"27","author":"Arnaout","year":"2021","journal-title":"Nat Med"},{"key":"B27","doi-asserted-by":"publisher","first-page":"105623","DOI":"10.1016\/j.cmpb.2020.105623","article-title":"A novel U-net approach to segment the cardiac chamber in magnetic resonance images with ghost artifacts","volume":"196","author":"Zhao","year":"2020","journal-title":"Comput Methods Programs Biomed"},{"key":"B28","doi-asserted-by":"publisher","first-page":"1379","DOI":"10.1007\/s00246-021-02622-0","article-title":"Diagnosing atrial septal defect from electrocardiogram with deep learning","volume":"42","author":"Mori","year":"2021","journal-title":"Pediatr Cardiol"},{"key":"B29","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1016\/s0894-7317(14)80375-1","article-title":"Color Doppler echocardiographic assessment of atrial septal defect size: correlation with surgical measurements","volume":"4","author":"Faletra","year":"1991","journal-title":"J Am Soc Echocardiogr"}],"container-title":["Frontiers in Cardiovascular Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2023.985657\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T08:19:41Z","timestamp":1682065181000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2023.985657\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,10]]},"references-count":29,"alternative-id":["10.3389\/fcvm.2023.985657"],"URL":"https:\/\/doi.org\/10.3389\/fcvm.2023.985657","relation":{},"ISSN":["2297-055X"],"issn-type":[{"value":"2297-055X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,10]]},"article-number":"985657"}}