{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T16:27:05Z","timestamp":1792168025428,"version":"4.4.0"},"reference-count":29,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T00:00:00Z","timestamp":1750809600000},"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":["Journal of Biophotonics"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>Accurate differentiation between rheumatoid arthritis (RA) and knee synovitis (KS) is essential for guiding optimal treatment, yet conventional histopathology often relies on subjective interpretation and offers limited insight into tissue biochemistry. Here, we introduce TransCNN, a novel multimodal framework that integrates hyperspectral imaging (HSI) with deep learning to achieve objective, high\u2010precision diagnosis. Reflectance\u2010mode HSI across the 400\u20131000\u2009nm spectrum was performed on 95 synovial tissue specimens. Spectral data were denoised using Savitzky\u2013Golay filtering and distilled via principal component analysis to enhance feature separability. TransCNN employs convolutional neural networks to capture intricate spatial morphology and Transformer layers to model global spectral correlations, producing a unified spectral\u2010spatial representation. On an independent validation set, TransCNN achieved 91% accuracy, 89% F1\u2010score, 90% recall, and 89% precision, substantially surpassing traditional approaches. These findings demonstrate that TransCNN provides a noninvasive, highly sensitive tool for pathological diagnosis, facilitating more reliable, data\u2010driven decision\u2010making in rheumatologic practice.<\/jats:p>","DOI":"10.1002\/jbio.202500197","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T19:54:01Z","timestamp":1750881241000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Automated Classification of Rheumatoid Arthritis and Knee Synovitis From Hyperspectral Reflectance Data"],"prefix":"10.1002","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3318-0493","authenticated-orcid":false,"given":"Shuwang","family":"Sun","sequence":"first","affiliation":[{"name":"Shandong Provincial Third Hospital Shandong University  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Shandong Provincial Third Hospital Shandong University  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3385-4603","authenticated-orcid":false,"given":"Minmin","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Pathology Affiliated Hospital of Shandong University of Traditional Chinese Medicine  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Urology The First Affiliated Hospital of Shandong First Medical University &amp; Shandong Provincial Qianfoshan Hospital, Shandong Institute of Nephrology  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihui","family":"He","sequence":"additional","affiliation":[{"name":"Shandong First Medical University  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lining","family":"Zhao","sequence":"additional","affiliation":[{"name":"Shandong Provincial Third Hospital Shandong University  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,6,25]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.2174\/1573397113666170427125918"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0889-857X(05)70206-4"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1136\/annrheumdis-2014-205894"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2023.10.009"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40744-022-00475-4"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10067-019-04791-z"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultrasmedbio.2023.12.007"},{"issue":"6","key":"e_1_2_8_9_1","first-page":"1355","article-title":"Deep Learning\u2010Based Computer\u2010Aided Diagnosis of Rheumatoid Arthritis With Hand X\u2010Ray Images Conforming to Modified Total Sharp\/van der Heijde Score","volume":"10","author":"Wang H. 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