{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T06:40:12Z","timestamp":1792132812480,"version":"4.4.0"},"reference-count":20,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,5]],"date-time":"2027-01-05T00:00:00Z","timestamp":1799107200000},"content-version":"am","delay-in-days":369,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/100000968","name":"American Heart Association Inc","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000968","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100019771","name":"European Union's Research and Innovation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100019771","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","jacc.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["JACC: Cardiovascular Imaging"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1016\/j.jcmg.2025.08.011","type":"journal-article","created":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T07:13:50Z","timestamp":1759302830000},"page":"18-29","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Deep Learning Model to Identify Mitral Valve Prolapse From the Echocardiogram"],"prefix":"10.1016","volume":"19","author":[{"given":"Mostafa A.","family":"Al-Alusi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9361-6397","authenticated-orcid":false,"given":"Emily S.","family":"Lau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aeron M.","family":"Small","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Reeder","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tal","family":"Shnitzer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carl T.","family":"Andrews","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8113-733X","authenticated-orcid":false,"given":"Shinwan","family":"Kany","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joel T.","family":"R\u00e4m\u00f6","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4346-6241","authenticated-orcid":false,"given":"Julian S.","family":"Haimovich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaan","family":"Khurshid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danita Y.","family":"Sanborn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael H.","family":"Picard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7987-4768","authenticated-orcid":false,"given":"Jennifer E.","family":"Ho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahnaz","family":"Maddah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick T.","family":"Ellinor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jcmg.2025.08.011_bib1","doi-asserted-by":"crossref","first-page":"2158","DOI":"10.1161\/CIRCULATIONAHA.113.006702","article-title":"Epidemiology and pathophysiology of mitral valve prolapse","volume":"129","author":"Delling","year":"2014","journal-title":"Circulation"},{"key":"10.1016\/j.jcmg.2025.08.011_bib2","doi-asserted-by":"crossref","first-page":"2904","DOI":"10.1016\/j.jacc.2018.09.048","article-title":"Arrhythmic mitral valve prolapse","volume":"72","author":"Miller","year":"2018","journal-title":"J Am Coll Cardiol"},{"key":"10.1016\/j.jcmg.2025.08.011_bib3","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.echo.2017.01.007","article-title":"Recommendations for noninvasive evaluation of native valvular regurgitation","volume":"30","author":"Zoghbi","year":"2017","journal-title":"J Am Soc Echocardiogr"},{"issue":"3","key":"10.1016\/j.jcmg.2025.08.011_bib4","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.jacc.2019.02.027","volume":"74","author":"Douglas","year":"2019","journal-title":"J Am Coll Cardiol"},{"key":"10.1016\/j.jcmg.2025.08.011_bib5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-019-0216-8","article-title":"Deep learning interpretation of echocardiograms","volume":"3","author":"Ghorbani","year":"2020","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.jcmg.2025.08.011_bib6","doi-asserted-by":"crossref","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":"10.1016\/j.jcmg.2025.08.011_bib7","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1016\/j.jacc.2023.09.800","article-title":"Deep learning-enabled assessment of left heart structure and function predicts cardiovascular outcomes","volume":"82","author":"Lau","year":"2023","journal-title":"J Am Coll Cardiol"},{"issue":"8","key":"10.1016\/j.jcmg.2025.08.011_bib8","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1016\/j.jcmg.2023.02.017","article-title":"Deep learning-based prediction of right ventricular ejection fraction using 2D echocardiograms","volume":"16","author":"Tokodi","year":"2023","journal-title":"JACC Cardiovasc Imaging"},{"issue":"6","key":"10.1016\/j.jcmg.2025.08.011_bib9","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1001\/jamacardio.2024.0595","article-title":"A multimodal video-based ai biomarker for aortic stenosis development and progression","volume":"9","author":"Oikonomou","year":"2024","journal-title":"JAMA Cardiology"},{"key":"10.1016\/j.jcmg.2025.08.011_bib10","doi-asserted-by":"crossref","first-page":"4592","DOI":"10.1093\/eurheartj\/ehad456","article-title":"Severe aortic stenosis detection by deep learning applied to echocardiography","volume":"44","author":"Holste","year":"2023","journal-title":"Eur Heart J"},{"key":"10.1016\/j.jcmg.2025.08.011_bib11","first-page":"1","article-title":"Vision-language foundation model for echocardiogram interpretation","author":"Christensen","year":"2024","journal-title":"Nat Med"},{"issue":"12","key":"10.1016\/j.jcmg.2025.08.011_bib12","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1161\/CIRCULATIONAHA.124.068996","article-title":"Deep learning for echo analysis, tracking, and evaluation of mitral regurgitation (DELINEATE-MR)","volume":"150","author":"Long","year":"2024","journal-title":"Circulation"},{"issue":"12","key":"10.1016\/j.jcmg.2025.08.011_bib13","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1161\/CIRCULATIONAHA.124.069047","article-title":"High-throughput deep learning detection of mitral regurgitation","volume":"150","author":"Vrudhula","year":"2025","journal-title":"Circulation"},{"key":"10.1016\/j.jcmg.2025.08.011_bib14","article-title":"Feasibility validation of automatic diagnosis of mitral valve prolapse from multi-view echocardiographic sequences based on deep neural network","volume":"2","author":"Wu","year":"2024","journal-title":"Eur Heart J Imaging Methods Pract"},{"key":"10.1016\/j.jcmg.2025.08.011_bib15","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1136\/bmj.39335.541782.AD","article-title":"Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies","volume":"335","author":"von Elm","year":"2007","journal-title":"BMJ"},{"key":"10.1016\/j.jcmg.2025.08.011_bib16","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.1016\/j.jcmg.2020.07.015","article-title":"Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): a checklist","volume":"13","author":"Sengupta","year":"2020","journal-title":"JACC Cardiovasc Imaging"},{"key":"10.1016\/j.jcmg.2025.08.011_bib17","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1038\/s41746-022-00590-0","article-title":"Cohort design and natural language processing to reduce bias in electronic health records research","volume":"5","author":"Khurshid","year":"2022","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.jcmg.2025.08.011_bib18","doi-asserted-by":"crossref","DOI":"10.2196\/38178","article-title":"One clinician is all you need-cardiac magnetic resonance imaging measurement extraction: deep learning algorithm development","volume":"10","author":"Singh","year":"2022","journal-title":"JMIR Med Inform"},{"key":"10.1016\/j.jcmg.2025.08.011_bib19","series-title":"MoViNets: Mobile Video Networks for Efficient Video Recognition","first-page":"16015","author":"Kondratyuk","year":"2021"},{"key":"10.1016\/j.jcmg.2025.08.011_bib22","doi-asserted-by":"crossref","first-page":"1688","DOI":"10.1161\/CIRCULATIONAHA.115.020621","article-title":"Evolution of mitral valve prolapse","volume":"133","author":"Delling","year":"2016","journal-title":"Circulation"}],"container-title":["JACC: Cardiovascular Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1936878X25004747?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1936878X25004747?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T19:33:14Z","timestamp":1767641594000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1936878X25004747"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":20,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["S1936878X25004747"],"URL":"https:\/\/doi.org\/10.1016\/j.jcmg.2025.08.011","relation":{},"ISSN":["1936-878X"],"issn-type":[{"value":"1936-878X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A Deep Learning Model to Identify Mitral Valve Prolapse From the Echocardiogram","name":"articletitle","label":"Article Title"},{"value":"JACC: Cardiovascular Imaging","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jcmg.2025.08.011","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 by the American College of Cardiology Foundation. Published by Elsevier.","name":"copyright","label":"Copyright"}]}}