{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T02:56:40Z","timestamp":1789181800257,"version":"build-2803163510"},"reference-count":43,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2020,8,11]],"date-time":"2020-08-11T00:00:00Z","timestamp":1597104000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Vet Radiology Ultrasound"],"published-print":{"date-parts":[[2020,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Although deep learning has been explored extensively for computer\u2010aided medical imaging diagnosis in human medicine, very little has been done in veterinary medicine. The goal of this retrospective, pilot project was to apply the deep learning artificial intelligence technique using thoracic radiographs for detection of canine left atrial enlargement and compare results with those of veterinary radiologist interpretations. Seven hundred ninety\u2010two right lateral radiographs from canine patients with thoracic radiographs and contemporaneous echocardiograms were used to train, validate, and test a convolutional neural network algorithm. The accuracy, sensitivity, and specificity for determination of left atrial enlargement were then compared with those of board\u2010certified veterinary radiologists as recorded on radiology reports. The accuracy, sensitivity, and specificity were 82.71%, 68.42%, and 87.09%, respectively, using an accuracy driven variant of the convolutional neural network algorithm and 79.01%, 73.68%, and 80.64%, respectively, using a sensitivity driven variant. By comparison, accuracy, sensitivity, and specificity achieved by board\u2010certified veterinary radiologists was 82.71%, 68.42%, and 87.09%, respectively. Although overall accuracy of the accuracy driven convolutional neural network algorithm and veterinary radiologists was identical, concordance between the two approaches was 85.19%. This study documents proof\u2010of\u2010concept for application of deep learning techniques for computer\u2010aided diagnosis in veterinary medicine.<\/jats:p>","DOI":"10.1111\/vru.12901","type":"journal-article","created":{"date-parts":[[2020,8,12]],"date-time":"2020-08-12T04:51:06Z","timestamp":1597207866000},"page":"611-618","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Pilot study: Application of artificial intelligence for detecting left atrial enlargement on canine thoracic radiographs"],"prefix":"10.1111","volume":"61","author":[{"given":"Shen","family":"Li","sequence":"first","affiliation":[{"name":"William R. 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