{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T16:23:32Z","timestamp":1783441412747,"version":"3.54.6"},"reference-count":22,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"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":["Veterinary Ophthalmology"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and evaluate a deep feature\u2010based normality modeling approach for automated out\u2010of\u2010distribution (OOD) detection in sheep retinal fundus images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Animals Studied<\/jats:title>\n                    <jats:p>\n                      Retinal fundus images from 75 adult sheep (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009271 images) and additional OOD images from non\u2010target species (cattle, dogs, and cats;\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009346 images).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Procedures<\/jats:title>\n                    <jats:p>Deep feature embeddings were extracted using a ResNet50 convolutional neural network pretrained on ImageNet. Normal retinal appearance was modeled in feature space using healthy images. Anomaly scores were calculated using a k\u2010nearest neighbor (k\u2009=\u20095) distance\u2010based approach. OOD detection was evaluated using receiver operating characteristic (ROC) analysis. The anomaly threshold was defined as the 95th percentile of validation scores.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed framework demonstrated a clear separation between in\u2010distribution sheep retinal images and OOD samples. The model achieved an area under the ROC curve of 1.00 (95% CI: 0.99\u20131.00). At the predefined threshold, all OOD images were correctly identified (100% detection rate), with a false alarm rate of 11.9% in the sheep test set.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Deep feature\u2010based normality modeling can characterize normal sheep retinal morphology and identify out\u2010of\u2010distribution samples. The proposed approach should be interpreted as a proof\u2010of\u2010concept screening and quality\u2010control tool, not a disease\u2010specific diagnostic system. Further validation using animal\u2010level partitioning and intra\u2010species pathological datasets is required to establish clinical utility. Normality modeling may hold promise for future screening of retinal disease within sheep; however, this has not yet been evaluated. Importantly, this study evaluates only cross\u2010species OOD detection and does not assess the detection of retinal abnormalities within sheep.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/vop.70220","type":"journal-article","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T04:27:28Z","timestamp":1782966448000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Feature\u2010Based Normality Modeling for Automated Out\u2010Of\u2010Distribution Detection in Sheep Retinal Fundus Images"],"prefix":"10.1111","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1490-8832","authenticated-orcid":false,"given":"B\u00fc\u015fra","family":"Kibar","sequence":"first","affiliation":[{"name":"Department of Surgery, Faculty of Veterinary Medicine Ayd\u0131n Adnan Menderes University  Ayd\u0131n T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2620-897X","authenticated-orcid":false,"given":"S\u0131tk\u0131can","family":"Okur","sequence":"additional","affiliation":[{"name":"Department of Surgery, Faculty of Veterinary Medicine Atat\u00fcrk University  Erzurum T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2787-1249","authenticated-orcid":false,"given":"B\u00fc\u015fra","family":"Baykal","sequence":"additional","affiliation":[{"name":"Department of Surgery, Faculty of Veterinary Medicine Atat\u00fcrk University  Erzurum T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4222-3718","authenticated-orcid":false,"given":"Taner","family":"Arslan","sequence":"additional","affiliation":[{"name":"Department of Surgery, Faculty of Veterinary Medicine Atat\u00fcrk University  Erzurum T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9402-5679","authenticated-orcid":false,"given":"\u00c7a\u011flar","family":"\u00d6zkalip\u00e7i","sequence":"additional","affiliation":[{"name":"Department of Surgery, Faculty of Veterinary Medicine Atat\u00fcrk University  Erzurum T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1088-7514","authenticated-orcid":false,"given":"Berfin","family":"Petek","sequence":"additional","affiliation":[{"name":"Department of Surgery, Faculty of Veterinary Medicine Ayd\u0131n Adnan Menderes University  Ayd\u0131n 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