{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T04:59:00Z","timestamp":1789707540010,"version":"4.0.1"},"reference-count":45,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","onlinejase.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of the American Society of Echocardiography"],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1016\/j.echo.2022.09.017","type":"journal-article","created":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T02:19:18Z","timestamp":1664504358000},"page":"96-104.e4","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":45,"title":["Machine Learning for Pediatric Echocardiographic Mitral Regurgitation Detection"],"prefix":"10.1016","volume":"36","author":[{"given":"Lindsay A.","family":"Edwards","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehreen","family":"Iqbal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amy","family":"Sanyahumbi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiying","family":"Hao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doff B.","family":"McElhinney","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"X. Bruce","family":"Ling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Craig","family":"Sable","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajia","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.echo.2022.09.017_bib1","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1016\/S1473-3099(05)70267-X","article-title":"The global burden of group A streptococcal diseases","volume":"5","author":"Carapetis","year":"2005","journal-title":"Lancet Infect Dis"},{"key":"10.1016\/j.echo.2022.09.017_bib2","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1111\/j.1540-8175.2009.01055.x","article-title":"High prevalence of rheumatic heart disease detected by echocardiography in school children","volume":"27","author":"Bhaya","year":"2010","journal-title":"Echocardiography"},{"key":"10.1016\/j.echo.2022.09.017_bib3","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1016\/j.amjcard.2010.01.364","article-title":"Prevalence of rheumatic heart disease in children and young adults in Nicaragua","volume":"105","author":"Paar","year":"2010","journal-title":"Am J Cardiol"},{"key":"10.1016\/j.echo.2022.09.017_bib4","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1038\/ncpcardio1185","article-title":"Evaluation of a screening protocol using auscultation and portable echocardiography to detect asymptomatic rheumatic heart disease in Tongan schoolchildren","volume":"5","author":"Carapetis","year":"2008","journal-title":"Nat Clin Pract Cardiovasc Med"},{"key":"10.1016\/j.echo.2022.09.017_bib5","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1056\/NEJMoa065085","article-title":"Prevalence of rheumatic heart disease detected by echocardiographic screening","volume":"357","author":"Marijon","year":"2007","journal-title":"N Engl J Med"},{"key":"10.1016\/j.echo.2022.09.017_bib6","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1038\/nrcardio.2012.7","article-title":"World Heart Federation criteria for echocardiographic diagnosis of rheumatic heart disease\u2014an evidence-based guideline","volume":"9","author":"Rem\u00e9nyi","year":"2012","journal-title":"Nat Rev Cardiol"},{"key":"10.1016\/j.echo.2022.09.017_bib7","doi-asserted-by":"crossref","first-page":"e002324","DOI":"10.1161\/CIRCIMAGING.114.002324","article-title":"Screening for rheumatic heart disease: evaluation of a focused cardiac ultrasound approach","volume":"8","author":"Mirabel","year":"2015","journal-title":"Circ Cardiovasc Imaging"},{"key":"10.1016\/j.echo.2022.09.017_bib8","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.echo.2015.01.001","article-title":"Simplified rheumatic heart disease screening criteria for handheld echocardiography","volume":"28","author":"Lu","year":"2015","journal-title":"J Am Soc Echocardiogr"},{"key":"10.1016\/j.echo.2022.09.017_bib9","doi-asserted-by":"crossref","first-page":"e939","DOI":"10.1542\/peds.2014-2774","article-title":"Handheld echocardiography versus auscultation for detection of rheumatic heart disease","volume":"135","author":"Godown","year":"2015","journal-title":"Pediatrics"},{"key":"10.1016\/j.echo.2022.09.017_bib10","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1017\/S1047951115001857","article-title":"Evaluation of a focussed protocol for hand-held echocardiography and computer-assisted auscultation in detecting latent rheumatic heart disease in scholars","volume":"26","author":"Z\u00fchlke","year":"2016","journal-title":"Cardiol Young"},{"key":"10.1016\/j.echo.2022.09.017_bib11","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1017\/S1047951117001676","article-title":"A focussed single-view hand-held echocardiography protocol for the detection of rheumatic heart disease","volume":"28","author":"Diamantino","year":"2018","journal-title":"Cardiol Young"},{"key":"10.1016\/j.echo.2022.09.017_bib12","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1017\/S1047951116002511","article-title":"Task shifting to clinical officer-led echocardiography screening for detecting rheumatic heart disease in Malawi, Africa","volume":"27","author":"Sims Sanyahumbi","year":"2017","journal-title":"Cardiol Young"},{"key":"10.1016\/j.echo.2022.09.017_bib13","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1017\/S1047951112001321","article-title":"Pilot study of nurse-led rheumatic heart disease echocardiography screening in Fiji\u2014a novel approach in a resource-poor setting","volume":"23","author":"Colquhoun","year":"2013","journal-title":"Cardiol Young"},{"key":"10.1016\/j.echo.2022.09.017_bib14","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1136\/heartjnl-2015-308236","article-title":"Handheld echocardiographic screening for rheumatic heart disease by non-experts","volume":"102","author":"Ploutz","year":"2016","journal-title":"Heart"},{"key":"10.1016\/j.echo.2022.09.017_bib15","doi-asserted-by":"crossref","first-page":"118","DOI":"10.4103\/0974-2069.157024","article-title":"Teaching focused echocardiography for rheumatic heart disease screening","volume":"8","author":"Engelman","year":"2015","journal-title":"Ann Pediatr Cardiol"},{"key":"10.1016\/j.echo.2022.09.017_bib16","doi-asserted-by":"crossref","first-page":"e386","DOI":"10.1016\/S2214-109X(16)30065-1","article-title":"Focused cardiac ultrasound screening for rheumatic heart disease by briefly trained health workers: a study of diagnostic accuracy","volume":"4","author":"Engelman","year":"2016","journal-title":"Lancet Glob Health"},{"key":"10.1016\/j.echo.2022.09.017_bib17","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1186\/s12872-016-0205-7","article-title":"Screening for rheumatic heart disease: quality and agreement of focused cardiac ultrasound by briefly trained health workers","volume":"16","author":"Engelman","year":"2016","journal-title":"BMC Cardiovasc Disord"},{"key":"10.1016\/j.echo.2022.09.017_bib18","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.1016\/j.amjcard.2016.03.006","article-title":"Efficacy of a standardized computer-based training curriculum to teach echocardiographic identification of rheumatic heart disease to nonexpert users","volume":"117","author":"Beaton","year":"2016","journal-title":"Am J Cardiol"},{"key":"10.1016\/j.echo.2022.09.017_bib19","doi-asserted-by":"crossref","first-page":"e004330","DOI":"10.1161\/CIRCIMAGING.115.004330","article-title":"Cognitive machine-learning algorithm for cardiac imaging: a pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy","volume":"9","author":"Sengupta","year":"2016","journal-title":"Circ Cardiovasc Imaging"},{"key":"10.1016\/j.echo.2022.09.017_bib20","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.media.2016.10.007","article-title":"Automatic apical view classification of echocardiograms using a discriminative learning dictionary","volume":"36","author":"Khamis","year":"2017","journal-title":"Med Image Anal"},{"key":"10.1016\/j.echo.2022.09.017_bib21","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1038\/s41746-018-0065-x","article-title":"Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease","volume":"1","author":"Madani","year":"2018","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.echo.2022.09.017_bib22","doi-asserted-by":"crossref","first-page":"6","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":"10.1016\/j.echo.2022.09.017_bib23","doi-asserted-by":"crossref","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":"10.1016\/j.echo.2022.09.017_bib24","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.inffus.2016.11.007","article-title":"A fused deep learning architecture for viewpoint classification of echocardiography","volume":"36","author":"Gao","year":"2017","journal-title":"Inform Fusion"},{"key":"10.1016\/j.echo.2022.09.017_bib25","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1093\/jamia\/ocab061","article-title":"Towards automatic diagnosis of rheumatic heart disease on echocardiographic exams through video-based deep learning","volume":"28","author":"Martins","year":"2021","journal-title":"J Am Med Inform Assoc"},{"key":"10.1016\/j.echo.2022.09.017_bib26","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1111\/chd.12353","article-title":"School and community screening shows Malawi, Africa, to have a high prevalence of latent rheumatic heart disease","volume":"11","author":"Sims Sanyahumbi","year":"2016","journal-title":"Congenit Heart Dis"},{"key":"10.1016\/j.echo.2022.09.017_bib27","series-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2261","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"10.1016\/j.echo.2022.09.017_bib28","series-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.echo.2022.09.017_bib29","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.echo.2022.09.017_bib30","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun ACM"},{"key":"10.1016\/j.echo.2022.09.017_bib31","first-page":"37","article-title":"Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation","volume":"2","author":"Powers","year":"2011","journal-title":"J Mach Learn Technol"},{"key":"10.1016\/j.echo.2022.09.017_bib32","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Der Maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.echo.2022.09.017_bib33","series-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2921","article-title":"Learning deep features for discriminative localization","author":"Zhou","year":"2016"},{"key":"10.1016\/j.echo.2022.09.017_bib34","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.1016\/S0735-1097(19)32217-X","article-title":"Computer deep learning for automatic identification of echocardiographic disease screening: data from the Atmosphere-Provar study","volume":"73","author":"Nascimento","year":"2019","journal-title":"J Am Coll Cardiol"},{"key":"10.1016\/j.echo.2022.09.017_bib35","doi-asserted-by":"crossref","DOI":"10.1016\/j.echo.2022.08.009","article-title":"An automated view classification model for pediatric echocardiography using artificial intelligence","author":"Gearhart","year":"2022","journal-title":"J Am Soc Echocardiogr"},{"key":"10.1016\/j.echo.2022.09.017_bib36","doi-asserted-by":"crossref","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":"10.1016\/j.echo.2022.09.017_bib37","doi-asserted-by":"crossref","first-page":"1592","DOI":"10.1136\/heartjnl-2021-319725","article-title":"Artificial intelligence for the echocardiographic assessment of valvular heart disease","volume":"108","author":"Nedadur","year":"2022","journal-title":"Heart"},{"key":"10.1016\/j.echo.2022.09.017_bib38","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.compbiomed.2016.03.026","article-title":"Automatic assessment of mitral regurgitation severity based on extensive textural features on 2D echocardiography videos","volume":"73","author":"Moghaddasi","year":"2016","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.echo.2022.09.017_bib39","first-page":"2602688","article-title":"Automatic assessment of mitral regurgitation severity using the Mask R-CNN algorithm with color Doppler echocardiography images","volume":"2021","author":"Zhang","year":"2021","journal-title":"Comput Math Methods Med"},{"key":"10.1016\/j.echo.2022.09.017_bib40","doi-asserted-by":"crossref","first-page":"e007928","DOI":"10.1161\/CIRCIMAGING.118.007928","article-title":"Simplified echocardiography screening criteria for diagnosing and predicting progression of latent rheumatic heart disease","volume":"12","author":"Nunes","year":"2019","journal-title":"Circ Cardiovasc Imaging"},{"key":"10.1016\/j.echo.2022.09.017_bib41","series-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2261","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"10.1016\/j.echo.2022.09.017_bib42","series-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.echo.2022.09.017_bib43","author":"Kingma"},{"key":"10.1016\/j.echo.2022.09.017_bib44","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.echo.2022.09.017_bib45","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun ACM"}],"container-title":["Journal of the American Society of Echocardiography"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0894731722004825?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0894731722004825?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T05:10:24Z","timestamp":1760245824000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0894731722004825"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["S0894731722004825"],"URL":"https:\/\/doi.org\/10.1016\/j.echo.2022.09.017","relation":{},"ISSN":["0894-7317"],"issn-type":[{"value":"0894-7317","type":"print"}],"subject":[],"published":{"date-parts":[[2023,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Machine Learning for Pediatric Echocardiographic Mitral Regurgitation Detection","name":"articletitle","label":"Article Title"},{"value":"Journal of the American Society of Echocardiography","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.echo.2022.09.017","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"2022 by the American Society of Echocardiography.","name":"copyright","label":"Copyright"}]}}