{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:26:07Z","timestamp":1792790767988,"version":"4.5.0"},"reference-count":36,"publisher":"Elsevier BV","issue":"7","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:00:00Z","timestamp":1779321600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/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"}]}],"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":[[2026,7]]},"DOI":"10.1016\/j.jacadv.2026.102914","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T12:10:48Z","timestamp":1781611848000},"page":"102914","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial Intelligence for the Detection of Hypertrophic Cardiomyopathy From Standard Electrocardiogram"],"prefix":"10.1016","volume":"5","author":[{"given":"Jakob","family":"Park","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Kermanshahchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher J.","family":"Love","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klevin Roger","family":"Reyes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Kukuev","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarit","family":"Meshesha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katia","family":"Kon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1637-5096","authenticated-orcid":false,"given":"Florian","family":"Rader","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"8","key":"10.1016\/j.jacadv.2026.102914_bib1","doi-asserted-by":"crossref","DOI":"10.1161\/JAHA.119.012250","article-title":"Prevalence of unexplained left ventricular hypertrophy by cardiac magnetic resonance imaging in MESA","volume":"8","author":"Massera","year":"2019","journal-title":"J Am Heart Assoc"},{"issue":"4","key":"10.1016\/j.jacadv.2026.102914_bib2","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"},{"issue":"1","key":"10.1016\/j.jacadv.2026.102914_bib3","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"},{"issue":"10","key":"10.1016\/j.jacadv.2026.102914_bib4","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"},{"issue":"17","key":"10.1016\/j.jacadv.2026.102914_bib5","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1161\/CIRCULATIONAHA.117.030616","article-title":"Population burden of sudden death associated with hypertrophic cardiomyopathy","volume":"136","author":"Aro","year":"2017","journal-title":"Circulation"},{"issue":"1","key":"10.1016\/j.jacadv.2026.102914_bib6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12938-025-01349-w","article-title":"Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management","volume":"24","author":"Wu","year":"2025","journal-title":"BioMed Engineering OnLine"},{"key":"10.1016\/j.jacadv.2026.102914_bib7","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.amjcard.2021.06.021","article-title":"Artificial intelligence-enabled electrocardiography to screen patients with dilated cardiomyopathy","volume":"155","author":"Shrivastava","year":"2021","journal-title":"Am J Cardiol"},{"issue":"suppl_1","key":"10.1016\/j.jacadv.2026.102914_bib8","article-title":"Artificial Intelligence (AI)-enhanced electrocardiography: a machine-learning model for differential diagnosis between hypertrophic cardiomyopathy, cardiac amyloidosis and Anderson-Fabry disease","volume":"45","author":"Ruotolo","year":"2024","journal-title":"Eur Heart J"},{"issue":"8","key":"10.1016\/j.jacadv.2026.102914_bib9","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1016\/j.jacep.2023.05.025","article-title":"A novel ECG-based deep learning algorithm to predict cardiomyopathy in patients with premature ventricular complexes","volume":"9","author":"Lampert","year":"2023","journal-title":"JACC Clin Electrophysiol"},{"issue":"7","key":"10.1016\/j.jacadv.2026.102914_bib10","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.2026.102914_bib11","doi-asserted-by":"crossref","DOI":"10.1161\/CIRCHEARTFAILURE.124.012667","article-title":"Detection of hypertrophic cardiomyopathy on electrocardiogram using artificial intelligence","author":"Hillis","year":"2025","journal-title":"Circ Heart Fail"},{"issue":"23","key":"10.1016\/j.jacadv.2026.102914_bib12","doi-asserted-by":"crossref","first-page":"2324","DOI":"10.1016\/j.jacc.2024.02.014","article-title":"2024 AHA\/ACC\/AMSSM\/HRS\/PACES\/SCMR guideline for the management of hypertrophic cardiomyopathy: a report of the American Heart Association\/American College of Cardiology Joint Committee on clinical practice guidelines","volume":"83","author":"Ommen","year":"2024","journal-title":"J Am Coll Cardiol"},{"issue":"5","key":"10.1016\/j.jacadv.2026.102914_bib13","doi-asserted-by":"crossref","DOI":"10.1056\/AIoa2400421","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 AI"},{"issue":"6","key":"10.1016\/j.jacadv.2026.102914_bib14","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.1016\/j.jacep.2025.02.024","article-title":"Real-world artificial intelligence\u2013based electrocardiographic analysis to diagnose hypertrophic cardiomyopathy","volume":"11","author":"Desai","year":"2025","journal-title":"JACC Clin Electrophysiol"},{"key":"10.1016\/j.jacadv.2026.102914_bib15","doi-asserted-by":"crossref","first-page":"403","DOI":"10.2147\/CLEP.S139300","article-title":"Misclassification of hypertrophic cardiomyopathy: validation of diagnostic codes","volume":"9","author":"Magnusson","year":"2017","journal-title":"Clin Epidemiol"},{"key":"10.1016\/j.jacadv.2026.102914_bib16","doi-asserted-by":"crossref","first-page":"743","DOI":"10.2147\/CLEP.S467341","article-title":"Validity of a hypertrophic cardiomyopathy diagnosis in adult patients in the Danish national patient register","volume":"16","author":"Severinsen","year":"2024","journal-title":"Clin Epidemiol"},{"issue":"2","key":"10.1016\/j.jacadv.2026.102914_bib17","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":"2025","journal-title":"Eur Heart J Qual Care Clin Outcomes"},{"key":"10.1016\/j.jacadv.2026.102914_bib18","doi-asserted-by":"crossref","first-page":"S14","DOI":"10.1016\/j.amjcard.2023.10.081","article-title":"Role of imaging in the diagnosis, evaluation, and management of hypertrophic cardiomyopathy","volume":"212","author":"Abraham","year":"2024","journal-title":"Am J Cardiol"},{"issue":"3","key":"10.1016\/j.jacadv.2026.102914_bib19","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1007\/s00330-020-07207-8","article-title":"Impact of cardiac magnetic resonance on the diagnosis of hypertrophic cardiomyopathy - a 10-year experience with over 1000 patients","volume":"31","author":"\u015apiewak","year":"2021","journal-title":"Eur Radiol"},{"key":"10.1016\/j.jacadv.2026.102914_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.jelectrocard.2025.153888","article-title":"A systematic review and meta-analysis on the performance of convolutional neural networks ECGs in the diagnosis of hypertrophic cardiomyopathy","volume":"89","author":"Queiroz","year":"2025","journal-title":"J Electrocardiol"},{"key":"10.1016\/j.jacadv.2026.102914_bib21","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.amjcard.2023.09.040","article-title":"Impact of a center of excellence in confirming or excluding a diagnosis of hypertrophic cardiomyopathy","volume":"208","author":"Farrar","year":"2023","journal-title":"Am J Cardiol"},{"issue":"6","key":"10.1016\/j.jacadv.2026.102914_bib22","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.1016\/j.jacep.2025.02.024","article-title":"Real-World artificial intelligence\u2013based electrocardiographic analysis to diagnose hypertrophic cardiomyopathy","volume":"11","author":"Desai","year":"2025","journal-title":"JACC Clin Electrophysiol"},{"issue":"2","key":"10.1016\/j.jacadv.2026.102914_bib23","doi-asserted-by":"crossref","DOI":"10.1056\/AIp2500808","article-title":"Designing clinically useful AI: a blueprint for impact","volume":"3","author":"Parsa","year":"2026","journal-title":"NEJM AI"},{"issue":"1","key":"10.1016\/j.jacadv.2026.102914_bib24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1756-0500-5-563","article-title":"Implications of the impact of prevalence on test thresholds and outcomes: lessons from tuberculosis","volume":"5","author":"Bentley","year":"2012","journal-title":"BMC Res Notes"},{"issue":"1","key":"10.1016\/j.jacadv.2026.102914_bib25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12874-023-02139-5","article-title":"Estimating cutoff values for diagnostic tests to achieve target specificity using extreme value theory","volume":"24","author":"Pugh","year":"2024","journal-title":"BMC Med Res Methodol"},{"issue":"9","key":"10.1016\/j.jacadv.2026.102914_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.hroo.2025.07.094","article-title":"ID: 4343067 artificial intelligence electrocardiography predicts undiagnosed hypertrophic cardiomyopathy in post-HOC analysis of cardiac arrest survivors","volume":"6","author":"Swain","year":"2025","journal-title":"Heart Rhythm O2"},{"issue":"4","key":"10.1016\/j.jacadv.2026.102914_bib27","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1093\/europace\/euae093","article-title":"Accurate diagnosis of apical hypertrophic cardiomyopathy using explainable advanced electrocardiogram analysis","volume":"26","author":"Hughes","year":"2024","journal-title":"EP Europace"},{"issue":"7","key":"10.1016\/j.jacadv.2026.102914_bib28","doi-asserted-by":"crossref","DOI":"10.1111\/echo.70235","article-title":"Apical hypertrophic cardiomyopathy: a clinical & multimodality imaging assessment","volume":"42","author":"Hamza","year":"2025","journal-title":"Echocardiography"},{"issue":"8","key":"10.1016\/j.jacadv.2026.102914_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.jacadv.2023.100582","article-title":"Patient-level artificial intelligence\u2013enhanced electrocardiography in hypertrophic cardiomyopathy: longitudinal treatment and clinical biomarker correlations","volume":"2","author":"Siontis","year":"2023","journal-title":"JACC Adv"},{"issue":"19","key":"10.1016\/j.jacadv.2026.102914_bib30","doi-asserted-by":"crossref","first-page":"5387","DOI":"10.1113\/JP287954","article-title":"Computational modelling of the impact of anatomical changes on ECGs in left ventricular hypertrophy","volume":"603","author":"Kariman","year":"2025","journal-title":"J Physiol"},{"issue":"13","key":"10.1016\/j.jacadv.2026.102914_bib31","doi-asserted-by":"crossref","first-page":"4718","DOI":"10.3390\/jcm14134718","article-title":"Advanced diagnosis of hypertrophic cardiomyopathy with AI-ECG and differences based on ethnicity and HCM subtype","volume":"14","author":"Lewontin","year":"2025","journal-title":"J Clin Med"},{"issue":"10","key":"10.1016\/j.jacadv.2026.102914_bib32","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"},{"issue":"6","key":"10.1016\/j.jacadv.2026.102914_bib33","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.cvdhj.2022.10.002","article-title":"Tandem deep learning and logistic regression models to optimize hypertrophic cardiomyopathy detection in routine clinical practice","volume":"3","author":"Maanja","year":"2022","journal-title":"Cardiovasc Digit Health J"},{"issue":"20","key":"10.1016\/j.jacadv.2026.102914_bib34","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1093\/eurheartj\/ehaf004","article-title":"Artificial intelligence applied to electrocardiogram to rule out acute myocardial infarction: the ROMIAE multicentre study","volume":"46","author":"Lee","year":"2025","journal-title":"Eur Heart J"},{"issue":"14","key":"10.1016\/j.jacadv.2026.102914_bib35","doi-asserted-by":"crossref","first-page":"E1028","DOI":"10.1161\/CIR.0000000000001201","article-title":"Use of artificial intelligence in improving outcomes in heart disease: a scientific statement from the American heart Association","volume":"149","author":"Armoundas","year":"2024","journal-title":"Circulation"},{"key":"10.1016\/j.jacadv.2026.102914_bib36","doi-asserted-by":"crossref","DOI":"10.1136\/heartjnl-2024-325608","article-title":"Clinical implementation of an AI-enabled ECG for hypertrophic cardiomyopathy detection","author":"Love","year":"2025","journal-title":"Heart"}],"container-title":["JACC: Advances"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2772963X26003352?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2772963X26003352?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T20:24:41Z","timestamp":1785529481000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2772963X26003352"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":36,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["S2772963X26003352"],"URL":"https:\/\/doi.org\/10.1016\/j.jacadv.2026.102914","relation":{},"ISSN":["2772-963X"],"issn-type":[{"value":"2772-963X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial Intelligence for the Detection of Hypertrophic Cardiomyopathy From Standard Electrocardiogram","name":"articletitle","label":"Article Title"},{"value":"JACC: Advances","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jacadv.2026.102914","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":"102914"}}