{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T18:13:59Z","timestamp":1792692839815,"version":"4.5.0"},"reference-count":35,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T00:00:00Z","timestamp":1650585600000},"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":[[2022,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    The use of artificial intelligence (AI) algorithms in diagnostic radiology is a developing area in veterinary medicine and may provide substantial benefit in many clinical settings. These range from timely image interpretation in the emergency setting when no boarded radiologist is available to allowing boarded radiologists to focus on more challenging cases that require complex medical decision making. Testing the performance of artificial intelligence (AI) software in veterinary medicine is at its early stages, and only a scant number of reports of validation of AI software have been published. The purpose of this study was to investigate the performance of an AI algorithm (Vetology AI\n                    <jats:sup>\u00ae<\/jats:sup>\n                    ) in the detection of pleural effusion in thoracic radiographs of dogs. In this retrospective, diagnostic case\u2013controlled study, 62 canine patients were recruited. A control group of 21 dogs with normal thoracic radiographs and a sample group of 41 dogs with confirmed pleural effusion were selected from the electronic medical records at the Cummings School of Veterinary Medicine. The images were cropped to include only the area of interest (i.e., thorax). The software then classified images into those with pleural effusion and those without. The AI algorithm was able to determine the presence of pleural effusion with 88.7% accuracy (\n                    <jats:italic>P<\/jats:italic>\n                    &lt; 0.05). The sensitivity and specificity were 90.2% and 81.8%, respectively (positive predictive value, 92.5%; negative predictive value, 81.8%). The application of this technology in the diagnostic interpretation of thoracic radiographs in veterinary medicine appears to be of value and warrants further investigation and testing.\n                  <\/jats:p>","DOI":"10.1111\/vru.13089","type":"journal-article","created":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T10:40:56Z","timestamp":1650624056000},"page":"573-579","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Accuracy of artificial intelligence software for the detection of confirmed pleural effusion in thoracic radiographs in dogs"],"prefix":"10.1111","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7494-8588","authenticated-orcid":false,"given":"Thiago Rinaldi","family":"M\u00fcller","sequence":"first","affiliation":[{"name":"Department Clinical Sciences Tufts University Cummings School of Veterinary Medicine  North Grafton Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mauricio","family":"Solano","sequence":"additional","affiliation":[{"name":"Department Clinical Sciences Tufts University Cummings School of Veterinary Medicine  North Grafton Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mirian Harumi","family":"Tsunemi","sequence":"additional","affiliation":[{"name":"Department of Biostatistics S\u00e3o Paulo State University. R. Prof. Dr. Ant\u00f4nio Celso Wagner Zanin  S\u00e3o Paulo Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,4,22]]},"reference":[{"key":"e_1_2_9_2_1","first-page":"673","article-title":"A review of machine learning applications in veterinary field","volume":"23","author":"Ci_han P","year":"2017","journal-title":"Kafkas Univ Vet Fak Der"},{"key":"e_1_2_9_3_1","first-page":"1","article-title":"Artificial intelligence evaluating primary thoracic lesions has an overall lower error rate compared to veterinarians or veterinarians in conjunction with the artificial intelligence","author":"Boissady E","year":"2020","journal-title":"Vet Radiol Ultrasound"},{"key":"e_1_2_9_4_1","first-page":"1803.04873","article-title":"Using convolutional neural networks for determining reticulocyte percentage in cats","author":"Vinicki K","year":"2018","journal-title":"arXiv"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"e_1_2_9_6_1","unstructured":"Vetology launches pet X\u2010ray\u2010reading ai software[accessed 20.01.21] https:\/\/www.veterinarypracticenews.com\/vetology\u2010launches\u2010pet\u2010X\u2010ray\u2010reading\u2010aisoftware\/;2018."},{"key":"e_1_2_9_7_1","unstructured":"Artificial intelligence powers new X\u2010ray software[accessed 20.01.21] https:\/\/todaysveterinarybusiness.com\/artificial\u2010intelligence\u2010powers\u2010new\u2010X\u2010raysoftware\/;2018"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2019.108774"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0000000000000509"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0000000000000341"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1148\/rg.2017170077"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018171820"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015142346"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2017162326"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002697"},{"key":"e_1_2_9_16_1","doi-asserted-by":"crossref","unstructured":"PourhomayounM ShakibiM.Predicting mortality risk in patients with COVID\u201019 using machine learning to help medical decision\u2010making.Smart Health 2021;20.https:\/\/doi.org\/10.1016\/j.smhl.2020.100178.","DOI":"10.1016\/j.smhl.2020.100178"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0145118"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djy222"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13264"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tvjl.2018.05.009"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1111\/vru.12901"},{"key":"e_1_2_9_23_1","first-page":"262","article-title":"Use of deep learning to detect cardiomegaly on thoracic radiographs in dogs","author":"Burti S.","year":"2020","journal-title":"The Veterinary Journal"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010021\u201083515\u20103"},{"key":"e_1_2_9_25_1","first-page":"140","article-title":"Cytologic examination of body cavity fluids","volume":"3","author":"O'Brien PJ","year":"1988","journal-title":"Semin Vet Med Surg (Small Anim)"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.5326\/JAAHA-MS-5558"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0195-5616(85)50111-4"},{"key":"e_1_2_9_28_1","first-page":"121","article-title":"Pleural effusions \u2013 pathophysiology and diagnostic considerations","volume":"10","author":"Forrester SD","year":"1988","journal-title":"Comp Cont Ed Pract Vet"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1740-8261.2011.01854.x"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-323-48247-9.00046-2"},{"key":"e_1_2_9_31_1","unstructured":"AbadiM BarhamP ChenJ et\u00a0al. \u201cTensorFlow: A System for Large\u2010Scale Machine Learning TensorFlow: A system for large\u2010scale machine learning \u201d in12th USENIX Symposium on Operating Systems Design and Implementation (OSDI \u201916) 2016 pp.265\u2013284"},{"key":"e_1_2_9_32_1","unstructured":"SimonyanK ZissermanA. \u201cVery deep convolutional networks for large\u2010scale image recognition.\u201dPresented as a conference paper international: conference on learning representation ICLR 2015 San Diego. 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