{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T04:54:45Z","timestamp":1794027285871,"version":"4.8.0"},"reference-count":40,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T00:00:00Z","timestamp":1763251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Research Administration Division, Khon Kaen University","award":["RP68-5-001"],"award-info":[{"award-number":["RP68-5-001"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Cutan Med Surg"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Tinea corporis and tinea cruris are frequently misdiagnosed, and studies utilizing deep learning for their diagnosis remain scarce.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives:<\/jats:title>\n                    <jats:p>This study primarily aimed to develop a deep learning model trained on images from diverse clinical settings to distinguish dermatophytosis from non-dermatophytosis. A secondary objective was to compare its diagnostic performance with that of physicians.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>This retrospective diagnostic study analyzed clinical images of dermatophytosis (tinea corporis\/cruris) and non-dermatophytosis (eczema, psoriasis, and lichen planus). Dermatophytosis was confirmed by the presence of hyphae, whereas non-dermatophytosis was diagnosed based on clinical and histological findings. The deep learning model utilized a multitask learning approach, integrating classification and segmentation. Adaptive weighting facilitated area-of-interest segmentation, improving attention to relevant features. Model performance was compared with physicians using a 20-image quiz from the test dataset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>A total of 1400 images (600 dermatophytosis, 840 non-dermatophytosis) from 580 Thai patients, predominantly with Fitzpatrick skin types III and IV, were analyzed. In the test dataset, the model achieved an AUC of 0.84 (95% CI: 0.80\u20130.88), a sensitivity of 0.80 (95% CI: 0.74\u20130.85), a specificity of 0.71 (95% CI: 0.65\u20130.76), an accuracy of 0.74 (95% CI: 0.70\u20130.78), and an F1-score of 0.71 (95% CI: 0.66\u20130.76). AUC values for deep learning, dermatologists (n\u2009=\u200915), and non-dermatologists (n\u2009=\u200911) on the image quiz were 0.80, 0.78, and 0.75, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>The deep learning model demonstrated effective diagnostic performance for tinea corporis\/cruris in Asian skin types, performing comparably to dermatologists as evaluated using a 20-image quiz derived from the test dataset.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/12034754251391809","type":"journal-article","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T03:37:17Z","timestamp":1763350637000},"page":"233-241","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Learning for Diagnosis of Tinea Corporis and Tinea Cruris"],"prefix":"10.1177","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4374-1170","authenticated-orcid":false,"given":"Narachai","family":"Julanon","sequence":"first","affiliation":[{"name":"Division of Dermatology, Department of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7125-9886","authenticated-orcid":false,"given":"Anupol","family":"Panitchote","sequence":"additional","affiliation":[{"name":"Division of Critical Care Medicine, Department of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4358-7927","authenticated-orcid":false,"given":"Praisan","family":"Padungweang","sequence":"additional","affiliation":[{"name":"College of Computing, Khon Kaen University, Khon Kaen, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3417-6210","authenticated-orcid":false,"given":"Charoen","family":"Choonhakarn","sequence":"additional","affiliation":[{"name":"Division of Dermatology, Department of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8954-7672","authenticated-orcid":false,"given":"Suteeraporn","family":"Chaowattanapanit","sequence":"additional","affiliation":[{"name":"Division of Dermatology, Department of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,11,16]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/jof6040310"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00436246"},{"issue":"6","key":"e_1_3_3_4_2","first-page":"1132","article-title":"Survey of skin and nail fungal infections by subject age among Thai adults and the etiological organisms","volume":"50","author":"Bunyaratavej S","year":"2019","unstructured":"Bunyaratavej S, Limphoka P, Kiratiwongwan R, Leeyaphan C. 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