{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T21:54:53Z","timestamp":1781819693426,"version":"3.54.5"},"reference-count":32,"publisher":"Elsevier BV","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":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007225","name":"Socialist Republic of Vietnam Ministry of Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007225","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Array"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.array.2026.100874","type":"journal-article","created":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T02:39:56Z","timestamp":1777603196000},"page":"100874","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Deep learning for heart failure prediction from chest X-ray in AF"],"prefix":"10.1016","volume":"30","author":[{"given":"Dai-Hua","family":"Tsai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng-Nan","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pang-Shuo","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jien-Jiun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cho-Kai","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juey-Jen","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhamad","family":"Faisal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Chih","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jenq-Shiou","family":"Leu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4853-8665","authenticated-orcid":false,"given":"Chia-Ti","family":"Tsai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.array.2026.100874_bib1","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1536\/ihj.19-714","article-title":"Diagnosing heart failure from chest X-Ray images using deep learning","volume":"61","author":"Matsumoto","year":"2020","journal-title":"Int Heart J"},{"key":"10.1016\/j.array.2026.100874_bib2","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1007\/s00392-021-01836-9","article-title":"Prognostic value of the chest X-ray in patients hospitalised for heart failure","volume":"110","author":"Pan","year":"2021","journal-title":"Clin Res Cardiol"},{"key":"10.1016\/j.array.2026.100874_bib3","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1007\/s10554-023-03039-x","article-title":"CheXNet and feature pyramid network: a fusion deep learning architecture for multilabel chest X-Ray clinical diagnoses classification","volume":"40","author":"Hasanah","year":"2024","journal-title":"Int J Cardiovasc Imag"},{"key":"10.1016\/j.array.2026.100874_bib4","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"Hosny","year":"2018","journal-title":"Nat Rev Cancer"},{"key":"10.1016\/j.array.2026.100874_bib5","doi-asserted-by":"crossref","DOI":"10.3390\/s19173722","article-title":"Automated lung nodule detection and classification using deep learning combined with multiple strategies","volume":"19","author":"Nasrullah","year":"2019","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.array.2026.100874_bib6","doi-asserted-by":"crossref","first-page":"672","DOI":"10.3748\/wjg.v25.i6.672","article-title":"Artificial intelligence in medical imaging of the liver","volume":"25","author":"Zhou","year":"2019","journal-title":"World J Gastroenterol"},{"key":"10.1016\/j.array.2026.100874_bib7","doi-asserted-by":"crossref","DOI":"10.3390\/jimaging7020019","article-title":"Deep learning for Brain Tumor Segmentation: a Survey of state-of-the-art","volume":"7","author":"Magadza","year":"2021","journal-title":"J Imaging"},{"key":"10.1016\/j.array.2026.100874_bib8","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.gie.2020.06.059","article-title":"Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis","volume":"93","author":"Hassan","year":"2021","journal-title":"Gastrointest Endosc"},{"key":"10.1016\/j.array.2026.100874_bib9","first-page":"1198","article-title":"Deep learning for detection of elevated pulmonary artery wedge pressure using standard chest X-Ray","volume":"37","author":"Hirata","year":"2021","journal-title":"Can J Cardiol"},{"key":"10.1016\/j.array.2026.100874_bib10","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1161\/CIRCULATIONAHA.119.042996","article-title":"Interdependence of atrial fibrillation and heart failure with a preserved ejection fraction reflects a common underlying atrial and ventricular myopathy","volume":"141","author":"Packer","year":"2020","journal-title":"Circulation"},{"key":"10.1016\/j.array.2026.100874_bib11","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1016\/j.jfma.2021.07.009","article-title":"Prognostic factors of functional outcome in post-acute stroke in the rehabilitation unit","volume":"121","author":"Chen","year":"2022","journal-title":"J Formos Med Assoc"},{"key":"10.1016\/j.array.2026.100874_bib12","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2022.845949","article-title":"Continuity and completeness of electronic health record data for patients treated with oral hypoglycemic agents: findings from healthcare delivery systems in Taiwan","volume":"13","author":"Hsu","year":"2022","journal-title":"Front Pharmacol"},{"key":"10.1016\/j.array.2026.100874_bib13","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1016\/j.jacc.2018.02.006","article-title":"The Taiwan heart registries: its influence on cardiovascular patient care","volume":"71","author":"Wu","year":"2018","journal-title":"J Am Coll Cardiol"},{"key":"10.1016\/j.array.2026.100874_bib14","doi-asserted-by":"crossref","first-page":"1640","DOI":"10.1161\/01.CIR.0000124487.36586.26","article-title":"Renin-angiotensin system gene polymorphisms and atrial fibrillation","volume":"109","author":"Tsai","year":"2004","journal-title":"Circulation"},{"key":"10.1016\/j.array.2026.100874_bib15","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1111\/jth.13735","article-title":"C-reactive protein gene polymorphism predicts the risk of thromboembolic stroke in patients with atrial fibrillation: a more than 10-year prospective follow-up study","volume":"15","author":"Chang","year":"2017","journal-title":"J Thromb Haemostasis"},{"key":"10.1016\/j.array.2026.100874_bib16","series-title":"COVID-19 radiography database","year":"2020"},{"key":"10.1016\/j.array.2026.100874_bib17","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1002\/ejhf.2333","volume":"24","author":"McDonagh","year":"2022","journal-title":"Eur J Heart Fail"},{"key":"10.1016\/j.array.2026.100874_bib18","first-page":"2278","volume":"86","author":"LeCun","year":"1998"},{"key":"10.1016\/j.array.2026.100874_bib19","doi-asserted-by":"crossref","DOI":"10.3390\/ijerph17228303","article-title":"Facilitating the development of deep learning models with visual analytics for electronic health records","volume":"17","author":"Hur","year":"2020","journal-title":"Int J Environ Res Publ Health"},{"key":"10.1016\/j.array.2026.100874_bib20","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1038\/nrcardio.2010.165","article-title":"Epidemiology and risk profile of heart failure","volume":"8","author":"Bui","year":"2011","journal-title":"Nat Rev Cardiol"},{"key":"10.1016\/j.array.2026.100874_bib21","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/9288452","article-title":"Machine learning-based automated diagnostic systems developed for heart failure prediction using different types of data modalities: a systematic review and future directions","volume":"2022","author":"Javeed","year":"2022","journal-title":"Comput Math Methods Med"},{"key":"10.1016\/j.array.2026.100874_bib22","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1056\/NEJMicm0708489","article-title":"Images in clinical medicine. Kerley's A, B, and C lines","volume":"360","author":"Koga","year":"2009","journal-title":"N Engl J Med"},{"key":"10.1016\/j.array.2026.100874_bib23","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s10741-006-9481-0","article-title":"Diagnosis of heart failure in primary care","volume":"11","author":"Fonseca","year":"2006","journal-title":"Heart Fail Rev"},{"key":"10.1016\/j.array.2026.100874_bib24","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.31083\/j.rcm2204121","article-title":"Artificial intelligence in the diagnosis and detection of heart failure: the past, present, and future","volume":"22","author":"Yasmin","year":"2021","journal-title":"Rev Cardiovasc Med"},{"key":"10.1016\/j.array.2026.100874_bib25","doi-asserted-by":"crossref","first-page":"1975","DOI":"10.1093\/eurheartj\/ehy404","article-title":"Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging","volume":"40","author":"Al'Aref","year":"2019","journal-title":"Eur Heart J"},{"key":"10.1016\/j.array.2026.100874_bib26","doi-asserted-by":"crossref","DOI":"10.1177\/1179546820927404","article-title":"Artificial intelligence, machine learning, and cardiovascular disease","volume":"14","author":"Mathur","year":"2020","journal-title":"Clin Med Insights Cardiol"},{"key":"10.1016\/j.array.2026.100874_bib27","doi-asserted-by":"crossref","DOI":"10.3389\/fmedt.2021.779800","article-title":"Multistage classification of current density distribution maps of various heart states based on correlation analysis and k-NN algorithm","volume":"3","author":"Udovychenko","year":"2021","journal-title":"Front Med Technol"},{"key":"10.1016\/j.array.2026.100874_bib28","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1007\/s12350-014-0027-x","article-title":"Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population","volume":"22","author":"Arsanjani","year":"2015","journal-title":"J Nucl Cardiol"},{"key":"10.1016\/j.array.2026.100874_bib29","doi-asserted-by":"crossref","first-page":"e75","DOI":"10.1016\/j.cmpb.2010.06.021","article-title":"Image processing and machine learning for fully automated probabilistic evaluation of medical images","volume":"104","author":"Sajn","year":"2011","journal-title":"Comput Methods Progr Biomed"},{"key":"10.1016\/j.array.2026.100874_bib30","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s10741-023-10360-z","article-title":"Diagnosis of heart failure with preserved ejection fraction: a systematic narrative review of the evidence","volume":"29","author":"Formiga","year":"2024","journal-title":"Heart Fail Rev"},{"key":"10.1016\/j.array.2026.100874_bib31","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1016\/j.jacc.2016.08.048","article-title":"Heart failure with preserved ejection fraction and atrial fibrillation: vicious twins","volume":"68","author":"Kotecha","year":"2016","journal-title":"J Am Coll Cardiol"},{"key":"10.1016\/j.array.2026.100874_bib32","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.hfc.2021.03.001","article-title":"Atrial fibrillation and heart failure with preserved ejection fraction: two chronic troublemakers","volume":"17","author":"Kim","year":"2021","journal-title":"Heart Fail Clin"}],"container-title":["Array"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2590005626001979?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2590005626001979?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T21:11:21Z","timestamp":1781817081000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2590005626001979"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":32,"alternative-id":["S2590005626001979"],"URL":"https:\/\/doi.org\/10.1016\/j.array.2026.100874","relation":{},"ISSN":["2590-0056"],"issn-type":[{"value":"2590-0056","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep learning for heart failure prediction from chest X-ray in AF","name":"articletitle","label":"Article Title"},{"value":"Array","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.array.2026.100874","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"100874"}}