{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T03:17:17Z","timestamp":1782962237223,"version":"3.54.5"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["JP17ek0210082"],"award-info":[{"award-number":["JP17ek0210082"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Grants-in-Aid for Scientific Research, Kakenhi","award":["24K19071"],"award-info":[{"award-number":["24K19071"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Heart Vessels"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s00380-025-02546-2","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T01:50:52Z","timestamp":1747273852000},"page":"883-894","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Utility of convolutional neural network-enhanced electrocardiogram to diagnose and predict mitral regurgitation in patients with chronic atrial fibrillation"],"prefix":"10.1007","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1118-0472","authenticated-orcid":false,"given":"Mayu","family":"Sakuma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shinya","family":"Suzuki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naomi","family":"Hirota","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Motogi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takuya","family":"Umemoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroshi","family":"Nakai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wataru","family":"Matsuzawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tsuneo","family":"Takayanagi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akira","family":"Hyodo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keiichi","family":"Satoh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takuto","family":"Arita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naoharu","family":"Yagi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mikio","family":"Kishi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroaki","family":"Semba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroto","family":"Kano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shunsuke","family":"Matsuno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuko","family":"Kato","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takayuki","family":"Otsuka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junji","family":"Yajima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasuchika","family":"Takeishi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tokuhisa","family":"Uejima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuji","family":"Oikawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takeshi","family":"Yamashita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,15]]},"reference":[{"issue":"2","key":"2546_CR1","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1161\/01.CIR.90.2.830","volume":"90","author":"M Enriquez-Sarano","year":"1994","unstructured":"Enriquez-Sarano M, Tajik AJ, Schaff HV, Orszulak TA, Bailey KR, Frye RL (1994) Echocardiographic prediction of survival after surgical correction of organic mitral regurgitation. Circulation 90(2):830\u2013837","journal-title":"Circulation"},{"issue":"2","key":"2546_CR2","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1378\/chest.96.2.242","volume":"96","author":"TL Schreiber","year":"1989","unstructured":"Schreiber TL, Fisher J, Mangla A, Miller D (1989) Severe \u201csilent\u201d mitral regurgitation. A potentially reversible cause of refractory heart failure. Chest 96(2):242\u2013246","journal-title":"Chest"},{"issue":"6","key":"2546_CR3","doi-asserted-by":"publisher","first-page":"768","DOI":"10.1161\/CIRCULATIONAHA.108.832626","volume":"119","author":"HV Schaff","year":"2009","unstructured":"Schaff HV (2009) Asymptomatic severe mitral valve regurgitation: observation or operation? Circulation 119(6):768\u2013769","journal-title":"Circulation"},{"issue":"5","key":"2546_CR4","doi-asserted-by":"publisher","first-page":"483","DOI":"10.4065\/mcp.2009.0706","volume":"85","author":"K Maganti","year":"2010","unstructured":"Maganti K, Rigolin VH, Sarano ME, Bonow RO (2010) Valvular heart disease: diagnosis and management. Mayo Clin Proc 85(5):483\u2013500","journal-title":"Mayo Clin Proc"},{"key":"2546_CR5","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.jelectrocard.2020.02.008","volume":"59","author":"JM Kwon","year":"2020","unstructured":"Kwon JM, Kim KH, Akkus Z, Jeon KH, Park J, Oh BH (2020) Artificial intelligence for detecting mitral regurgitation using electrocardiography. J Electrocardiol 59:151\u2013157","journal-title":"J Electrocardiol"},{"issue":"1","key":"2546_CR6","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1186\/s12877-021-02391-8","volume":"21","author":"N Hirota","year":"2021","unstructured":"Hirota N, Suzuki S, Arita T, Yagi N, Otsuka T, Yamashita T (2021) Prediction of biological age and all-cause mortality by 12-lead electrocardiogram in patients without structural heart disease. BMC Geriatr 21(1):460","journal-title":"BMC Geriatr"},{"issue":"2","key":"2546_CR7","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.jjcc.2011.06.006","volume":"58","author":"S Suzuki","year":"2011","unstructured":"Suzuki S, Yamashita T, Otsuka T, Sagara K, Uejima T, Oikawa Y, Yajima J, Koike A, Nagashima K, Kirigaya H, Ogasawara K, Sawada H, Aizawa T (2011) Recent mortality of Japanese patients with atrial fibrillation in an urban city of Tokyo. J Cardiol 58(2):116\u2013123","journal-title":"J Cardiol"},{"issue":"4","key":"2546_CR8","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.echo.2017.01.007","volume":"30","author":"WA Zoghbi","year":"2017","unstructured":"Zoghbi WA, Adams D, Bonow RO, Enriquez-Sarano M, Foster E, Grayburn PA, Hahn RT, Han Y, Hung J, Lang RM, Little SH, Shah DJ, Shernan S, Thavendiranathan P, Thomas JD, Weissman NJ (2017) Recommendations for noninvasive evaluation of native valvular regurgitation: a report from the american society of echocardiography developed in collaboration with the society for cardiovascular magnetic resonance. J Am Soc Echocardiogr 30(4):303\u2013371","journal-title":"J Am Soc Echocardiogr"},{"key":"2546_CR9","first-page":"100883","volume":"37","author":"S Suzuki","year":"2021","unstructured":"Suzuki S, Yamashita T, Otsuka T, Arita T, Yagi N, Kishi M, Semba H, Kano H, Matsuno S, Kato Y, Uejima T, Oikawa Y, Matsuhama M, Iida M, Inoue T, Yajima J (2021) Identifying risk patterns in older adults with atrial fibrillation by hierarchical cluster analysis: a retrospective approach based on the risk probability for clinical events. Int J Cardiol Heart Vasc 37:100883","journal-title":"Int J Cardiol Heart Vasc"},{"key":"2546_CR10","doi-asserted-by":"publisher","first-page":"103600","DOI":"10.1016\/j.ebiom.2021.103600","volume":"72","author":"L Baecker","year":"2021","unstructured":"Baecker L, Garcia-Dias R, Vieira S, Scarpazza C, Mechelli A (2021) Machine learning for brain age prediction: introduction to methods and clinical applications. EBioMedicine 72:103600","journal-title":"EBioMedicine"},{"issue":"10201","key":"2546_CR11","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/S0140-6736(19)31721-0","volume":"394","author":"ZI Attia","year":"2019","unstructured":"Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, Carter RE, Yao X, Rabinstein AA, Erickson BJ, Kapa S, Friedman PA (2019) An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet 394(10201):861\u2013867","journal-title":"Lancet"},{"key":"2546_CR12","first-page":"100954","volume":"38","author":"S Suzuki","year":"2022","unstructured":"Suzuki S, Motogi J, Nakai H, Matsuzawa W, Takayanagi T, Umemoto T, Hirota N, Hyodo A, Satoh K, Otsuka T, Arita T, Yagi N, Yamashita T (2022) Identifying patients with atrial fibrillation during sinus rhythm on ECG: significance of the labeling in the artificial intelligence algorithm. Int J Cardiol Heart Vasc 38:100954","journal-title":"Int J Cardiol Heart Vasc"},{"key":"2546_CR13","first-page":"101172","volume":"44","author":"N Hirota","year":"2023","unstructured":"Hirota N, Suzuki S, Motogi J, Nakai H, Matsuzawa W, Takayanagi T, Umemoto T, Hyodo A, Satoh K, Arita T, Yagi N, Otsuka T, Yamashita T (2023) Cardiovascular events and artificial intelligence-predicted age using 12-lead electrocardiograms. Int J Cardiol Heart Vasc 44:101172","journal-title":"Int J Cardiol Heart Vasc"},{"key":"2546_CR14","doi-asserted-by":"crossref","unstructured":"Nagi J, Ducatelle F, Di Caro A, Ciresan D, Meier U, Giusti A, Nagi F, Schmidhuber J, Gambardella M (2011) Max-pooling convolutional neural networks for vision-based hand gesture recognition. In: IEEE international conference on signal and image processing applications, Kuala Lumpur, pp 342\u2013347","DOI":"10.1109\/ICSIPA.2011.6144164"},{"key":"2546_CR15","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. arXiv pre-print server","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3","key":"2546_CR16","doi-asserted-by":"publisher","first-page":"823","DOI":"10.2307\/2533545","volume":"53","author":"M Coffin","year":"1997","unstructured":"Coffin M, Sukhatme S (1997) Receiver operating characteristic studies and measurement errors. Biometrics 53(3):823\u2013837","journal-title":"Biometrics"},{"key":"2546_CR17","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1016\/j.jacep.2023.04.008","volume":"9","author":"H Gruwez","year":"2023","unstructured":"Gruwez H, Barthels M, Haemers P, Verbrugge FH, Dhont S, Meekers E, Wouters F, Nuyens D, Pison L, Vandervoort P, Pierlet N (2023) Detecting paroxysmal atrial fibrillation from an electrocardiogram in sinus rhythm: external validation of the AI approach. JACC Clin Electrophysiol 9:1771\u20131782","journal-title":"JACC Clin Electrophysiol"},{"key":"2546_CR18","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.ijcard.2020.12.065","volume":"329","author":"IZ Attia","year":"2021","unstructured":"Attia IZ, Tseng AS, Benavente ED, Medina-Inojosa JR, Clark TG, Malyutina S, Kapa S, Schirmer H, Kudryavtsev AV, Noseworthy PA, Carter RE, Ryabikov A, Perel P, Friedman PA, Leon DA, Lopez-Jimenez F (2021) External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction. Int J Cardiol 329:130\u2013135","journal-title":"Int J Cardiol"},{"issue":"8","key":"2546_CR19","doi-asserted-by":"publisher","first-page":"e008437","DOI":"10.1161\/CIRCEP.120.008437","volume":"13","author":"D Adedinsewo","year":"2020","unstructured":"Adedinsewo D, Carter RE, Attia Z, Johnson P, Kashou AH, Dugan JL, Albus M, Sheele JM, Bellolio F, Friedman PA, Lopez-Jimenez F, Noseworthy PA (2020) Artificial intelligence-enabled ECG algorithm to identify patients with left ventricular systolic dysfunction presenting to the emergency department with dyspnea. Circ Arrhythm Electrophysiol 13(8):e008437","journal-title":"Circ Arrhythm Electrophysiol"},{"issue":"1","key":"2546_CR20","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1038\/s41591-018-0240-2","volume":"25","author":"ZI Attia","year":"2019","unstructured":"Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satem G, Pellikka PA, Enriquez-Sarano M, Noseworthy PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Friedman PA (2019) Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med 25(1):70\u201374","journal-title":"Nat Med"},{"issue":"6","key":"2546_CR21","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1007\/s00380-024-02367-9","volume":"39","author":"N Hirota","year":"2024","unstructured":"Hirota N, Suzuki S, Motogi J, Umemoto T, Nakai H, Matsuzawa W, Takayanagi T, Hyodo A, Satoh K, Arita T, Yagi N, Kishi M, Semba H, Kano H, Matsuno S, Kato Y, Otsuka T, Uejima T, Oikawa Y, Hori T, Matsuhama M, Iida M, Yajima J, Yamashita T (2024) Evaluating convolutional neural network-enhanced electrocardiography for hypertrophic cardiomyopathy detection in a specialized cardiovascular setting. Heart Vessels 39(6):524\u2013538","journal-title":"Heart Vessels"},{"issue":"6","key":"2546_CR22","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.cvdhj.2022.10.002","volume":"3","author":"M Maanja","year":"2022","unstructured":"Maanja M, Noseworthy PA, Geske JB, Ackerman MJ, Arruda-Olson AM, Ommen SR, Attia ZI, Friedman PA, Siontis KC (2022) Tandem deep learning and logistic regression models to optimize hypertrophic cardiomyopathy detection in routine clinical practice. Cardiovasc Digit Health J 3(6):289\u2013296","journal-title":"Cardiovasc Digit Health J"},{"key":"2546_CR23","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.ijcard.2021.08.026","volume":"340","author":"KC Siontis","year":"2021","unstructured":"Siontis KC, Liu K, Bos JM, Attia ZI, Cohen-Shelly M, Arruda-Olson AM, Zanjirani Farahani N, Friedman PA, Noseworthy PA, Ackerman MJ (2021) Detection of hypertrophic cardiomyopathy by an artificial intelligence electrocardiogram in children and adolescents. Int J Cardiol 340:42\u201347","journal-title":"Int J Cardiol"},{"key":"2546_CR24","first-page":"101389","volume":"51","author":"T Arita","year":"2024","unstructured":"Arita T, Suzuki S, Motogi J, Umemoto T, Hirota N, Nakai H, Matsuzawa W, Takayanagi T, Hyodo A, Satoh K, Yagi N, Otsuka T, Kishi M, Kano H, Matsuno S, Kato Y, Uejima T, Oikawa Y, Hori T, Matsuhama M, Iida M, Yajima J, Yamashita T (2024) Identifying patients with acute aortic dissection using an electrocardiogram with convolutional neural network. Int J Cardiol Heart Vasc 51:101389","journal-title":"Int J Cardiol Heart Vasc"}],"container-title":["Heart and Vessels"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00380-025-02546-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00380-025-02546-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00380-025-02546-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T17:47:16Z","timestamp":1758822436000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00380-025-02546-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,15]]},"references-count":24,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["2546"],"URL":"https:\/\/doi.org\/10.1007\/s00380-025-02546-2","relation":{},"ISSN":["0910-8327","1615-2573"],"issn-type":[{"value":"0910-8327","type":"print"},{"value":"1615-2573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,15]]},"assertion":[{"value":"3 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Dr. Suzuki received research funding from Mitsubishi Tanabe Pharm and Daiichi Sankyo. Dr. Yamashita received research funds and\/or lecture fees from Daiichi Sankyo, Bayer Yakuhin, Bristol-Myers Squibb, Pfizer, Nippon Boehringer Ingelheim, Eisai, Mitsubishi Tanabe Pharm, Ono Pharmaceutical, and Toa Eiyo.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study was performed in accordance with the ethical norms based on the Declaration of Helsinki (revised in 2013) and Ethical Guidelines for Medical and Health Research Involving Human Subjects (Public Notice of the Ministry of Education, Culture, Sports, Science and Technology and the Ministry of Health, Labour and Welfare, Japan, issued in 2017). Written informed consent was obtained from all participants. The study protocol was reviewed by the Institutional Review Board of the Cardiovascular Institute.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics and informed consent"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}