{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T18:14:02Z","timestamp":1792692842962,"version":"4.5.0"},"reference-count":16,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T00:00:00Z","timestamp":1723075200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100024370","name":"Ministero dell'Istruzione dell'Universita e della Ricerca","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100024370","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Research in Veterinary Science"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1016\/j.rvsc.2024.105377","type":"journal-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T09:26:58Z","timestamp":1723109218000},"page":"105377","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"special_numbering":"C","title":["Development of an artificial intelligence-based algorithm for predicting the severity of myxomatous mitral valve disease from thoracic radiographs by using two grading systems"],"prefix":"10.1016","volume":"178","author":[{"given":"Carlotta","family":"Valente","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marek","family":"Wodzinski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlo","family":"Guglielmini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helen","family":"Poser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Chiavegato","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Zotti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberto","family":"Venturini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tommaso","family":"Banzato","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.rvsc.2024.105377_bb0005","doi-asserted-by":"crossref","DOI":"10.3389\/fvets.2021.731936","article-title":"An AI based algorithm for the automatic classification of thoracic radiographs in cats","volume":"8","author":"Banzato","year":"2021","journal-title":"Front. Vet. Sci."},{"issue":"1","key":"10.1016\/j.rvsc.2024.105377_bb0010","doi-asserted-by":"crossref","first-page":"3964","DOI":"10.1038\/s41598-021-83515-3","article-title":"Automatic classification of canine thoracic radiographs using deep learning","volume":"11","author":"Banzato","year":"2021","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.rvsc.2024.105377_bb0015","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.jvc.2012.01.011","article-title":"Historical review, epidemiology and natural history of degenerative mitral valve disease","volume":"14","author":"Borgarelli","year":"2012","journal-title":"J. Vet. Cardiol."},{"issue":"6","key":"10.1016\/j.rvsc.2024.105377_bb0020","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1111\/jvim.14586","article-title":"Effect of Pimobendan in dogs with preclinical Myxomatous mitral valve disease and cardiomegaly: the EPIC study-a randomized clinical trial","volume":"30","author":"Boswood","year":"2016","journal-title":"J. Vet. Intern. Med."},{"issue":"3","key":"10.1016\/j.rvsc.2024.105377_bb0025","first-page":"311","article-title":"Allometric scaling of M-mode cardiac measurements in normal adult dogs","volume":"18","author":"Cornell","year":"2004","journal-title":"J. Vet. Intern. Med."},{"issue":"11","key":"10.1016\/j.rvsc.2024.105377_bb0030","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1016\/j.jacc.2018.12.054","article-title":"Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review","volume":"73","author":"Dey","year":"2019","journal-title":"J. Am. Coll. Cardiol."},{"issue":"1","key":"10.1016\/j.rvsc.2024.105377_bb0035","doi-asserted-by":"crossref","first-page":"14494","DOI":"10.1038\/s41598-022-18822-4","article-title":"An automated deep learning method and novel cardiac index to detect canine cardiomegaly from simple radiography","volume":"12","author":"Jeong","year":"2022","journal-title":"Sci. Rep."},{"issue":"3","key":"10.1016\/j.rvsc.2024.105377_bb0040","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1111\/jvim.15488","article-title":"ACVIM consensus guidelines for the diagnosis and treatment of myxomatous mitral valve disease in dogs","volume":"33","author":"Keene","year":"2019","journal-title":"J. Vet. Intern. Med."},{"issue":"6","key":"10.1016\/j.rvsc.2024.105377_bb0045","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1111\/vru.12901","article-title":"Pilot study: application of artificial intelligence for detecting left atrial enlargement on canine thoracic radiographs","volume":"61","author":"Li","year":"2020","journal-title":"Vet. Radiol. Ultrasound"},{"issue":"4","key":"10.1016\/j.rvsc.2024.105377_bb0050","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/S0001-2998(78)80014-2","article-title":"Basic principles of ROC analysis","volume":"8","author":"Metz","year":"1978","journal-title":"Semin. Nucl. Med."},{"issue":"4","key":"10.1016\/j.rvsc.2024.105377_bb0055","first-page":"429","article-title":"Evaluation of four 2-dimensional echocardiographic methods of assessing left atrial size in dogs","volume":"14","author":"Rishniw","year":"2000","journal-title":"J. Vet. Intern. Med."},{"key":"10.1016\/j.rvsc.2024.105377_bb0060","doi-asserted-by":"crossref","DOI":"10.3389\/fcvm.2020.618849","article-title":"The role of artificial intelligence in cardiovascular imaging: state of the art review","volume":"7","author":"Seetharam","year":"2020","journal-title":"Front. Cardiovasc. Med."},{"issue":"4","key":"10.1016\/j.rvsc.2024.105377_bib76","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1111\/j.1939-1676.1993.tb01015.x","article-title":"Committee of the Specialty of Cardiology, American College of Veterinary Internal Medicine","volume":"7","author":"Thomas","year":"1993","journal-title":"J. Vet. Intern. Med."},{"issue":"2","key":"10.1016\/j.rvsc.2024.105377_bb0065","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.210221","article-title":"Development and validation of artificial intelligence-based method for diagnosis of mitral regurgitation from chest radiographs","volume":"4","author":"Ueda","year":"2022","journal-title":"Radiol. Artif. Intell."},{"key":"10.1016\/j.rvsc.2024.105377_bb0070","doi-asserted-by":"crossref","first-page":"1227009","DOI":"10.3389\/fvets.2023.1227009","article-title":"Development of an artificial intelligence-based method for the diagnosis of the severity of myxomatous mitral valve disease from canine chest radiographs","volume":"10","author":"Valente","year":"2023","journal-title":"Front. Vet. Sci."},{"issue":"3","key":"10.1016\/j.rvsc.2024.105377_bb0075","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.1111\/jvim.16131","article-title":"The mitral INsufficiency echocardiographic score: a severity classification of myxomatous mitral valve disease in dogs","volume":"35","author":"Vezzosi","year":"2021","journal-title":"J. Vet. Intern. Med."}],"container-title":["Research in Veterinary Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0034528824002443?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0034528824002443?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T17:48:23Z","timestamp":1725990503000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0034528824002443"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":16,"alternative-id":["S0034528824002443"],"URL":"https:\/\/doi.org\/10.1016\/j.rvsc.2024.105377","relation":{},"ISSN":["0034-5288"],"issn-type":[{"value":"0034-5288","type":"print"}],"subject":[],"published":{"date-parts":[[2024,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Development of an artificial intelligence-based algorithm for predicting the severity of myxomatous mitral valve disease from thoracic radiographs by using two grading systems","name":"articletitle","label":"Article Title"},{"value":"Research in Veterinary Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.rvsc.2024.105377","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"105377"}}