{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:50:39Z","timestamp":1790211039369,"version":"4.1.0"},"reference-count":26,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2022,1,19]],"date-time":"2022-01-19T00:00:00Z","timestamp":1642550400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Vet Radiology Ultrasound"],"published-print":{"date-parts":[[2022,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Application of artificial intelligence (AI) to improve clinical diagnosis is a burgeoning field in human and veterinary medicine. The objective of this prospective, diagnostic accuracy study was to determine the accuracy, sensitivity, and specificity of an AI\u2010based software for diagnosing canine cardiogenic pulmonary edema from thoracic radiographs, using an American College of Veterinary Radiology\u2010certified veterinary radiologist's interpretation as the reference standard. Five hundred consecutive canine thoracic radiographs made after\u2010hours by a veterinary Emergency Department were retrieved. A total of 481 of 500 cases were technically analyzable. Based on the radiologist's assessment, 46 (10.4%) of these 481 dogs were diagnosed with cardiogenic pulmonary edema (CPE+). Of these cases, the AI software designated 42 of 46 as CPE+ and four of 46 as cardiogenic pulmonary edema negative (CPE\u2212). Accuracy, sensitivity, and specificity of the AI\u2010based software compared to radiologist diagnosis were 92.3%, 91.3%, and 92.4%, respectively (positive predictive value, 56%; negative predictive value, 99%). Findings supported using AI software screening for thoracic radiographs of dogs with suspected cardiogenic pulmonary edema to assist with short\u2010term decision\u2010making when a radiologist is unavailable.<\/jats:p>","DOI":"10.1111\/vru.13062","type":"journal-article","created":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T02:45:59Z","timestamp":1642646759000},"page":"292-297","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Comparison of artificial intelligence to the veterinary radiologist's diagnosis of canine cardiogenic pulmonary edema"],"prefix":"10.1111","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6756-4324","authenticated-orcid":false,"given":"Eunbee","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Diagnostic Imaging The Animal Medical Center  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony J.","family":"Fischetti","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging The Animal Medical Center  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pratheev","family":"Sreetharan","sequence":"additional","affiliation":[{"name":"Vetology Innovations, LLC.  San Diego California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joel G.","family":"Weltman","sequence":"additional","affiliation":[{"name":"Department of Emergency and Critical Care The Animal Medical Center  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philip R.","family":"Fox","sequence":"additional","affiliation":[{"name":"Department of Cardiology The Animal Medical Center  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,1,19]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/jvim.15488"},{"key":"e_1_2_9_3_1","first-page":"457","volume-title":"Textbook of Canine and Feline Cardiology: Principles and Clinical Practice","author":"Buchanan JW","year":"1999"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-323-48247-9.00047-4"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1939-1676.2010.0592.x"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.2460\/javma.250.6.666"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1148\/rg.2017170077"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018171820"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020192224"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2019.108774"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0000000000000341"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0000000000000509"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tvjl.2018.05.009"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/vru.12901"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/vru.12912"},{"key":"e_1_2_9_16_1","unstructured":"Artificial intelligence. American College of Veterinary Radiology website. May 7 2021. Accessed May 12 2021.https:\/\/acvr.org\/artificial\u2010intelligence\u2010in\u2010veterinary\u2010diagnostic\u2010imaging\u2010and\u2010radiation\u2010oncology\/"},{"key":"e_1_2_9_17_1","unstructured":"SimonyanK ZissermanA Very deep convolutional networks for large\u2010scale image recognition. Paper presented at: International Conference on Learning Representations 2015; May 7\u20139 2015; San Diego USA."},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2014.02.013"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1740-8261.1966.tb01057.x"},{"key":"e_1_2_9_20_1","first-page":"1","article-title":"Does the computed tomographic appearance of the lung differ between young and old dogs?","author":"Hornby NL","year":"2017","journal-title":"Vet Radiol Ultrasound"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.diii.2018.04.007"},{"key":"e_1_2_9_22_1","first-page":"1","article-title":"The state of artificial intelligence\u2010based FDA\u2010approved medical devices and algorithms: an online database","volume":"118","author":"Benjamens S","year":"2020","journal-title":"NPJ Digit Med"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20190840"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(20)30160-6"},{"key":"e_1_2_9_25_1","first-page":"1","article-title":"Is artificial intelligence the new friend for radiologists? A review article","volume":"12","author":"Gampala S","year":"2020","journal-title":"Cureus"},{"key":"e_1_2_9_26_1","first-page":"2","article-title":"Active reprioritization of the reading worklist using artificial intelligence has a beneficial effect on the turnaround time for interpretation of head CT with intracranial hemorrhage","volume":"3","author":"O'Neill TJ","year":"2020","journal-title":"Radiology"},{"key":"e_1_2_9_27_1","first-page":"4","article-title":"Clinical performance and role of expert supervision of deep learning for cardiac ventricular volumetry: a validation study","volume":"2","author":"Retson TA","year":"2020","journal-title":"Radiology"}],"container-title":["Veterinary Radiology &amp; Ultrasound"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/vru.13062","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/vru.13062","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/vru.13062","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T00:29:12Z","timestamp":1692836952000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/vru.13062"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,19]]},"references-count":26,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,5]]}},"alternative-id":["10.1111\/vru.13062"],"URL":"https:\/\/doi.org\/10.1111\/vru.13062","archive":["Portico"],"relation":{},"ISSN":["1058-8183","1740-8261"],"issn-type":[{"value":"1058-8183","type":"print"},{"value":"1740-8261","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,19]]},"assertion":[{"value":"2021-05-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-12-08","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-01-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}