{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T15:18:10Z","timestamp":1788016690819,"version":"build-2784847793"},"reference-count":36,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T00:00:00Z","timestamp":1776643200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T00:00:00Z","timestamp":1776643200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Vet Radiology Ultrasound"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Urothelial carcinoma (UC) is a highly malignant urinary cancer of the transitional epithelium in dogs. Recent advances in artificial intelligence (AI) and machine learning have shown substantial potential in veterinary medicine. The purpose of this study was to evaluate the accuracy of AI\u2010based software in detecting UC in dogs using abdominal radiography and to identify factors that influence the sensitivity of AI\u2010based diagnosis. Dogs underwent abdominal radiography, and ultrasound was retrospectively retrieved. Dogs with histologically confirmed UC and ultrasound changes were included in UC training and UC validation groups, whereas dogs without clinical suspicion of urinary neoplasia and without ultrasound findings consistent with UC were included as non\u2010UC training and non\u2010UC validation groups. Histological and imaging findings of UC were recorded. A convolutional neural network (CNN) was trained with 500 studies from the UC training and 500 studies from the non\u2010UC training groups. For validation, an additional 185 studies from the UC validation and 180 studies from the non\u2010UC validation groups were used to provide AI\u2010based diagnosis of UC by the trained CNN. The sensitivity, specificity, and accuracy of the AI\u2010based diagnosis of UC were 69%, 67%, and 68%, respectively. The software showed higher sensitivity in detecting more severe UC with mineralization. However, medial iliac lymphadenomegaly and ureteral obstruction did not improve the sensitivity of AI\u2010based diagnosis. In conclusion, well\u2010trained CNN demonstrated moderate accuracy for detecting UC using abdominal radiographs, with higher sensitivity in cases with more advanced disease. The unexpectedly superior performance of the ventrodorsal (VD) view warrants further investigation.<\/jats:p>","DOI":"10.1111\/vru.70172","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T12:27:15Z","timestamp":1776688035000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Investigation of the Accuracy and Contributing Factors of AI\u2010Based Diagnosis of Urothelial Carcinoma in Canine Abdominal Radiography"],"prefix":"10.1111","volume":"67","author":[{"given":"Masahiro","family":"Murakami","sequence":"first","affiliation":[{"name":"Department of Veterinary Clinical Sciences College of Veterinary Medicine Purdue University  West Lafayette Indiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kosuke","family":"Kinoshita","sequence":"additional","affiliation":[{"name":"Department of Veterinary Clinical Sciences College of Veterinary Medicine Purdue University  West Lafayette Indiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Sola","sequence":"additional","affiliation":[{"name":"Department of Comparative Pathobiology College of Veterinary Medicine Purdue University  West Lafayette Indiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony","family":"Lutz","sequence":"additional","affiliation":[{"name":"Vetology Innovations, LLC  San Diego California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tvjl.2015.01.017"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-323-59496-7.00030-X"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/ilar\/ilu018"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.2460\/javma.2004.224.1290"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/15287398909531360"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1939-1676.2000.tb02263.x"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.2460\/javma.239.3.350"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/vru.12085"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0140525X16001837"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/vru.12912"},{"key":"e_1_2_10_12_1","first-page":"673","article-title":"A Review of Machine Learning Applications in Veterinary Field","volume":"23","author":"Cihan P.","year":"2017","journal-title":"Kafkas Universitesi Veteriner Fakultesi Dergisi"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2022.110216"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20201407"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1097\/CORR.0000000000001685"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"e_1_2_10_17_1","unstructured":"K.Vinicki P.Ferrari M.Belic andR.Turk \u201cUsing Convolutional Neural Networks for Determining Reticulocyte Percentage in Cats \u201d preprint arXiv: 180304873 March 14 2018."},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1111\/1754-9485.13393"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gie.2019.12.049"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1177\/1024907920948994"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1799-6"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1111\/vru.12968"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1111\/vru.13089"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1002\/9780470376928.ch10"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.humpath.2012.01.008"},{"key":"e_1_2_10_26_1","unstructured":"J.Cho K.Lee E.Shin G.Choy andS.Do \u201cHow Much Data Is Needed to Train a Medical Image Deep Learning System to Achieve Necessary High Accuracy? 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