{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T00:13:46Z","timestamp":1783728826821,"version":"3.55.0"},"reference-count":36,"publisher":"Elsevier BV","issue":"6","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000038","name":"U.S. Food and Drug Administration","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","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: Advances"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.jacadv.2026.102801","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T19:24:38Z","timestamp":1781724278000},"page":"102801","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P2","title":["Detection of Left Ventricular Outflow Obstruction From Standard B-Mode Echocardiogram Videos Using Deep Learning"],"prefix":"10.1016","volume":"5","author":[{"given":"Victoria","family":"Yuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6252-6134","authenticated-orcid":false,"given":"Hirotaka","family":"Ieki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christina","family":"Binder","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuki","family":"Sahashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul C.","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Ouyang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jacadv.2026.102801_bib1","doi-asserted-by":"crossref","DOI":"10.1016\/j.cpcardiol.2024.102786","article-title":"Mavacamten in hypertrophic obstructive cardiomyopathy: prospects for AI integration and mitigating healthcare disparities","volume":"49","author":"Sulaiman","year":"2024","journal-title":"Curr Prob Cardiol"},{"key":"10.1016\/j.jacadv.2026.102801_bib2","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1093\/ehjqcco\/qcae031","article-title":"Frequency of misdiagnosis in hypertrophic cardiomyopathy","volume":"11","author":"Nielsen","year":"2024","journal-title":"Eur Heart J Qual Care Clin Outcomes"},{"key":"10.1016\/j.jacadv.2026.102801_bib3","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1016\/S0140-6736(20)31792-X","article-title":"Mavacamten for treatment of symptomatic obstructive hypertrophic cardiomyopathy (EXPLORER-HCM): a randomised, double-blind, placebo-controlled, phase 3 trial","volume":"396","author":"Olivotto","year":"2020","journal-title":"Lancet"},{"key":"10.1016\/j.jacadv.2026.102801_bib4","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1056\/NEJMra1710575","article-title":"Clinical course and management of hypertrophic cardiomyopathy","volume":"379","author":"Maron","year":"2018","journal-title":"N Engl J Med"},{"key":"10.1016\/j.jacadv.2026.102801_bib5","series-title":"FDA approves new drug to improve heart function in adults with rare heart condition","year":"2022"},{"key":"10.1016\/j.jacadv.2026.102801_bib6","doi-asserted-by":"crossref","DOI":"10.1161\/JAHA.123.030607","article-title":"Long-term safety and efficacy of mavacamten in symptomatic obstructive hypertrophic cardiomyopathy: interim results of the PIONEER-OLE study","volume":"13","author":"Masri","year":"2024","journal-title":"J Am Heart Assoc"},{"key":"10.1016\/j.jacadv.2026.102801_bib7","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.jacc.2021.12.002","article-title":"Diagnosis and evaluation of hypertrophic cardiomyopathy: JACC state-of-the-art review","volume":"79","author":"Maron","year":"2022","journal-title":"J Am Coll Cardiol"},{"key":"10.1016\/j.jacadv.2026.102801_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.cpcardiol.2024.102786","article-title":"Mavacamten in hypertrophic obstructive cardiomyopathy: prospects for AI integration and mitigating healthcare disparities","volume":"49","author":"Sulaiman","year":"2024","journal-title":"Curr Probl Cardiol"},{"key":"10.1016\/j.jacadv.2026.102801_bib9","doi-asserted-by":"crossref","first-page":"1651","DOI":"10.1016\/j.amjcard.2016.02.044","article-title":"Occurrence of clinically diagnosed hypertrophic cardiomyopathy in the United States","volume":"117","author":"Maron","year":"2016","journal-title":"Am J Cardiol"},{"key":"10.1016\/j.jacadv.2026.102801_bib10","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1016\/j.echo.2024.02.010","article-title":"Obstruction in hypertrophic cardiomyopathy: many faces","volume":"37","author":"Abbasi","year":"2024","journal-title":"J Am Soc Echocardiogr"},{"key":"10.1016\/j.jacadv.2026.102801_bib11","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1080\/13696998.2023.2208966","article-title":"Frequency and clinicoeconomic impact of delays to definitive diagnosis of obstructive hypertrophic cardiomyopathy in the United States","volume":"26","author":"Naidu","year":"2023","journal-title":"J Med Econ"},{"key":"10.1016\/j.jacadv.2026.102801_bib12","article-title":"Stable rates of obstructive hypertrophic cardiomyopathy in a contemporary era","volume":"8","author":"Butzner","year":"2021","journal-title":"Front Cardiovasc Med"},{"key":"10.1016\/j.jacadv.2026.102801_bib13","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.ahj.2008.04.002","article-title":"Clinical characteristics and outcomes of patients with hypertrophic cardiomyopathy with latent obstruction","volume":"156","author":"Vaglio","year":"2008","journal-title":"Am Heart J"},{"key":"10.1016\/j.jacadv.2026.102801_bib14","first-page":"2232","article-title":"Hypertrophic cardiomyopathy is predominantly a disease of left ventricular outflow tract obstruction","volume":"114","author":"Cheitlin","year":"2006","journal-title":"Year B Cardiol"},{"key":"10.1016\/j.jacadv.2026.102801_bib15","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1001\/jamacardio.2021.6059","article-title":"High-throughput precision phenotyping of left ventricular hypertrophy with cardiovascular deep learning","volume":"7","author":"Duff","year":"2022","journal-title":"JAMA Cardiol"},{"key":"10.1016\/j.jacadv.2026.102801_bib16","doi-asserted-by":"crossref","first-page":"10","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.jacadv.2026.102801_bib17","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":"2024","journal-title":"Circulation"},{"key":"10.1016\/j.jacadv.2026.102801_bib18","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.jacadv.2026.102801_bib19","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.jacadv.2026.102801_bib20","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"},{"key":"10.1016\/j.jacadv.2026.102801_bib21","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1038\/s41586-025-09850-x","article-title":"Comprehensive echocardiogram evaluation with view primed vision language AI","volume":"650","author":"Vukadinovic","year":"2026","journal-title":"Nature"},{"key":"10.1016\/j.jacadv.2026.102801_bib22","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1001\/jama.2025.8731","article-title":"Complete AI-enabled echocardiography interpretation with multitask deep learning","volume":"334","author":"Holste","year":"2025","journal-title":"JAMA"},{"key":"10.1016\/j.jacadv.2026.102801_bib23","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1016\/j.jacc.2019.12.030","article-title":"Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram","volume":"75","author":"Ko","year":"2020","journal-title":"J Am Coll Cardiol"},{"key":"10.1016\/j.jacadv.2026.102801_bib24","article-title":"Calibration of ECG-based deep-learning algorithm scores for patients flagged as high risk for hypertrophic cardiomyopathy","volume":"2","author":"Lampert","year":"2025","journal-title":"NEJM"},{"key":"10.1016\/j.jacadv.2026.102801_bib25","doi-asserted-by":"crossref","DOI":"10.1161\/CIRCOUTCOMES.118.005289","article-title":"Automated and interpretable patient ECG profiles for disease detection, tracking, and discovery","volume":"12","author":"Tison","year":"2019","journal-title":"Circ Cardiovasc Qual Outcomes"},{"key":"10.1016\/j.jacadv.2026.102801_bib26","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1161\/CIRCULATIONAHA.121.058696","article-title":"Multinational federated learning approach to train ECG and echocardiogram models for hypertrophic cardiomyopathy detection","volume":"146","author":"Goto","year":"2022","journal-title":"Circulation"},{"key":"10.1016\/j.jacadv.2026.102801_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.jacadv.2025.101746","article-title":"Machine learning computer vision point of care decision support of echocardiographic identification of hypertrophic cardiomyopathy","volume":"4","author":"Vemulapalli","year":"2025","journal-title":"JACC Adv"},{"key":"10.1016\/j.jacadv.2026.102801_bib28","article-title":"A closer look at spatiotemporal convolutions for action recognition","author":"Tran","year":"2017","journal-title":"arXiv"},{"key":"10.1016\/j.jacadv.2026.102801_bib29","article-title":"Grad-CAM: visual explanations from deep networks via Gradient-based localization","author":"Selvaraju","year":"2016","journal-title":"arXiv"},{"key":"10.1016\/j.jacadv.2026.102801_bib30","doi-asserted-by":"crossref","first-page":"e23","DOI":"10.1016\/j.jtcvs.2021.04.001","article-title":"2020 AHA\/ACC guideline for the diagnosis and treatment of patients with hypertrophic cardiomyopathy: a report of the American College of Cardiology\/American Heart Association Joint Committee on Clinical Practice guidelines","volume":"162","author":"Ommen","year":"2021","journal-title":"J Thorac Cardiovasc Surg"},{"key":"10.1016\/j.jacadv.2026.102801_bib31","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1152\/physiolgenomics.00063.2020","article-title":"Machine learning-based classification and diagnosis of clinical cardiomyopathies","volume":"52","author":"Alimadadi","year":"2020","journal-title":"Physiol Genomics"},{"key":"10.1016\/j.jacadv.2026.102801_bib32","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-02971-z","article-title":"Machine learning of native T1 mapping radiomics for classification of hypertrophic cardiomyopathy phenotypes","volume":"11","author":"Antonopoulos","year":"2021","journal-title":"Sci Rep"},{"key":"10.1016\/j.jacadv.2026.102801_bib33","doi-asserted-by":"crossref","first-page":"213","DOI":"10.3389\/fphys.2018.00213","article-title":"Distinct ECG phenotypes identified in hypertrophic cardiomyopathy using machine learning associate with arrhythmic risk markers","volume":"9","author":"Lyon","year":"2018","journal-title":"Front Physiol"},{"key":"10.1016\/j.jacadv.2026.102801_bib34","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104648","article-title":"A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy","volume":"135","author":"Smole","year":"2021","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.jacadv.2026.102801_bib35","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1016\/j.jcmg.2024.04.013","article-title":"Machine learning in hypertrophic cardiomyopathy: nonlinear model from clinical and CMR features predicting cardiovascular events","volume":"17","author":"Zhao","year":"2024","journal-title":"JACC Cardiovasc Imaging"},{"key":"10.1016\/j.jacadv.2026.102801_bib36","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1016\/j.echo.2025.08.008","article-title":"Single-view echocardiographic analysis for left ventricular outflow tract obstruction prediction in hypertrophic cardiomyopathy: a deep learning approach","volume":"38","author":"Park","year":"2025","journal-title":"J Am Soc Echocardiogr"}],"container-title":["JACC: Advances"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2772963X2600222X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2772963X2600222X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T23:25:15Z","timestamp":1783725915000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2772963X2600222X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":36,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["S2772963X2600222X"],"URL":"https:\/\/doi.org\/10.1016\/j.jacadv.2026.102801","relation":{},"ISSN":["2772-963X"],"issn-type":[{"value":"2772-963X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Detection of Left Ventricular Outflow Obstruction From Standard B-Mode Echocardiogram Videos Using Deep Learning","name":"articletitle","label":"Article Title"},{"value":"JACC: Advances","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jacadv.2026.102801","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation.","name":"copyright","label":"Copyright"}],"article-number":"102801"}}