{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T06:07:52Z","timestamp":1783836472994,"version":"3.55.0"},"reference-count":33,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"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 Dermatology"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate evaluation of skin lesions is an essential component of dermatological examination, yet it can be time\u2010consuming and subject to interobserver variability. While artificial intelligence (AI) models have shown reliability in diagnosing specific skin diseases, lesion\u2010level identification remains underexplored in veterinary dermatology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To develop and evaluate a convolutional neural network (CNN)\u2010based AI model for the automated identification of four skin lesion types in dogs: erythema, lichenification, alopecia and erosion\/ulcer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Animals<\/jats:title>\n                    <jats:p>Clinical skin images were collected from dogs presented to a veterinary medical teaching hospital.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>Four EfficientNet models were independently trained, one for each lesion type. Model performance was evaluated by comparing the prediction results with veterinary surgeon\u2010labelled data, using six metrics: accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>All four CNN models achieved an accuracy of &gt;\u200990%, indicating a reliable performance across all lesion types. The alopecia model yielded the highest accuracy (98.12%) and F1 score (98.18%). The erythema and erosion\/ulcer models exhibited balanced performance across all metrics. For the lichenification model, the sensitivity (87.02%) and F1 score (89.88%) were the lowest among the four lesion types.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions and Clinical Relevance<\/jats:title>\n                    <jats:p>The CNN\u2010based AI models developed in this study demonstrated validity in identifying common canine skin lesions from clinical images. These models may facilitate rapid and objective dermatological evaluation, supporting clinical diagnosis and lesion monitoring throughout treatment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/vde.70083","type":"journal-article","created":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:19:39Z","timestamp":1777695579000},"page":"471-477","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence\u2010Based Identification of Common Canine Skin Lesions From Clinical Images"],"prefix":"10.1111","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3551-5576","authenticated-orcid":false,"given":"Soh\u2010Yoon","family":"Kang","sequence":"first","affiliation":[{"name":"Laboratory of Veterinary Dermatology and the Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University  Seoul Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9524-024X","authenticated-orcid":false,"given":"Yeong\u2010Hun","family":"Kang","sequence":"additional","affiliation":[{"name":"Laboratory of Veterinary Dermatology and the Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University  Seoul Korea"},{"name":"Western Animal Medical Center  Seoul Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyun\u2010Jung","family":"Kim","sequence":"additional","affiliation":[{"name":"AIFORPET Corporation  Seoul Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eun\u2010A.","family":"Huh","sequence":"additional","affiliation":[{"name":"AIFORPET Corporation  Seoul Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jong\u2010Soo","family":"Jeon","sequence":"additional","affiliation":[{"name":"AIFORPET Corporation  Seoul Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyun\u2010Su","family":"Kim","sequence":"additional","affiliation":[{"name":"AIFORPET Corporation  Seoul Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9045-4388","authenticated-orcid":false,"given":"Min\u2010Sun","family":"Kim","sequence":"additional","affiliation":[{"name":"Laboratory of Veterinary Dermatology and the Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University  Seoul Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4383-3823","authenticated-orcid":false,"given":"Anisha","family":"Tiwari","sequence":"additional","affiliation":[{"name":"Suvidha Pet Care and Derma Centre  Jammu Jammu and Kashmir India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7113-0361","authenticated-orcid":false,"given":"Cheol\u2010Yong","family":"Hwang","sequence":"additional","affiliation":[{"name":"Laboratory of Veterinary Dermatology and the Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University  Seoul Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41415-023-5845-2"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.25259\/IJDVL_518_19"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-50437-0"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.2460\/ajvr.22.03.0038"},{"key":"e_1_2_11_6_1","unstructured":"M.TanandQ.Le \u201cEfficientNet: Rethinking Model Scaling for Convolutional Neural Networks \u201dProceedings of the 36th International Conference on Machine Learning Long Beach CA USA: 2019; 6105\u20136114."},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuri.2021.100034"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3184113"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.5161"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12917-021-02775-3"},{"key":"e_1_2_11_11_1","first-page":"66","volume-title":"Muller and Kirk's Small Animal Dermatology","author":"Miller W. 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