{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T11:07:33Z","timestamp":1771153653364,"version":"3.50.1"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T00:00:00Z","timestamp":1771113600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T00:00:00Z","timestamp":1771113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 112-2221-E-018-018"],"award-info":[{"award-number":["NSTC 112-2221-E-018-018"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 113-2221-E-025-007"],"award-info":[{"award-number":["NSTC 113-2221-E-025-007"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Health Inf Sci Syst"],"DOI":"10.1007\/s13755-026-00435-0","type":"journal-article","created":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T10:53:17Z","timestamp":1771152797000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ACD2W-InceptionNeXt: adjacent class distinguished and class distance weighted InceptionNeXt-based computer-aided mayo endoscopic scoring system for still images and video segments"],"prefix":"10.1007","volume":"14","author":[{"given":"Yuan\u2011Yen","family":"Chang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying-Yuan","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han-Po","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hsu-Heng","family":"Yen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4882-7033","authenticated-orcid":false,"given":"Yao-Sian","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,15]]},"reference":[{"issue":"4","key":"435_CR1","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1016\/j.cgh.2020.12.017","volume":"20","author":"R Khanna","year":"2022","unstructured":"Khanna R, Ma C, Jairath V, Vande Casteele N, Zou G, Feagan BG. Endoscopic assessment of inflammatory bowel disease activity in clinical trials. Clin Gastroenterol Hepatol. 2022;20(4):727-36 e2. https:\/\/doi.org\/10.1016\/j.cgh.2020.12.017.","journal-title":"Clin Gastroenterol Hepatol"},{"issue":"3","key":"435_CR2","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1093\/ecco-jcc\/jjab169","volume":"16","author":"D Chen","year":"2022","unstructured":"Chen D, Fulmer C, Gordon IO, Syed S, Stidham RW, Vande Casteele N, et al. Application of artificial intelligence to clinical practice in inflammatory bowel disease\u2014what the clinician needs to know. J Crohns Colitis. 2022;16(3):460\u201371. https:\/\/doi.org\/10.1093\/ecco-jcc\/jjab169.","journal-title":"J Crohns Colitis"},{"key":"435_CR3","doi-asserted-by":"crossref","unstructured":"Alammari A, Islam AR, Oh J, Tavanapong W, Wong J, Groen PCD. Classification of ulcerative colitis severity in colonoscopy videos using CNN. In: Proceedings of the 9th international conference on information management and engineering. Barcelona: Association for Computing Machinery; 2017. p. 139\u201344.","DOI":"10.1145\/3149572.3149613"},{"key":"435_CR4","doi-asserted-by":"crossref","unstructured":"Dahal A, Oh J, Tavanapong W, Wong J, Groen PCD. Detection of ulcerative colitis severity in colonoscopy video frames. In: 2015 13th International workshop on content-based multimedia indexing (CBMI). 2015. p. 1\u20136.","DOI":"10.1109\/CBMI.2015.7153617"},{"issue":"5","key":"435_CR5","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2019.3963","volume":"2","author":"RW Stidham","year":"2019","unstructured":"Stidham RW, Liu W, Bishu S, Rice MD, Higgins PDR, Zhu J, et al. Performance of a deep learning model vs human reviewers in grading endoscopic disease severity of patients with ulcerative colitis. JAMA Netw Open. 2019;2(5):e193963. https:\/\/doi.org\/10.1001\/jamanetworkopen.2019.3963.","journal-title":"JAMA Netw Open"},{"issue":"1","key":"435_CR6","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-06726-2","volume":"12","author":"RT Sutton","year":"2022","unstructured":"Sutton RT, Zai Ane OR, Goebel R, Baumgart DC. Artificial intelligence enabled automated diagnosis and grading of ulcerative colitis endoscopy images. Sci Rep. 2022;12(1):2748. https:\/\/doi.org\/10.1038\/s41598-022-06726-2.","journal-title":"Sci Rep"},{"key":"435_CR7","doi-asserted-by":"publisher","unstructured":"Yao H, Najarian K, Gryak J, Bishu S, Rice MD, Waljee AK, et al. Fully automated endoscopic disease activity assessment in ulcerative colitis. Gastrointest Endosc. 2021;93(3):728\u201336 e1. https:\/\/doi.org\/10.1016\/j.gie.2020.08.011","DOI":"10.1016\/j.gie.2020.08.011"},{"key":"435_CR8","doi-asserted-by":"publisher","unstructured":"Gottlieb K, Requa J, Karnes W, Gudivada RC, Shen J, Rael E, et al. Central reading of ulcerative colitis clinical trial videos using neural networks. Gastroenterology. 2021;160(3):710\u20139 e2. https:\/\/doi.org\/10.1053\/j.gastro.2020.10.024","DOI":"10.1053\/j.gastro.2020.10.024"},{"issue":"5","key":"435_CR9","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1093\/ibd\/izy325","volume":"25","author":"DC de Jong","year":"2019","unstructured":"de Jong DC, Lowenberg M, Koumoutsos I, Ray S, Mawdsley J, Anderson S, et al. Validation and investigation of the operating characteristics of the ulcerative colitis endoscopic index of severity. Inflamm Bowel Dis. 2019;25(5):937\u201344. https:\/\/doi.org\/10.1093\/ibd\/izy325.","journal-title":"Inflamm Bowel Dis"},{"key":"435_CR10","doi-asserted-by":"crossref","unstructured":"Shi X, Cao W, Raschka SJPA, Applications. Deep neural networks for rank-consistent ordinal regression based on conditional probabilities. arXiv2023. p. 941\u201355.","DOI":"10.1007\/s10044-023-01181-9"},{"issue":"9","key":"435_CR11","doi-asserted-by":"publisher","first-page":"1431","DOI":"10.1093\/ibd\/izac226","volume":"29","author":"G Polat","year":"2023","unstructured":"Polat G, Kani HT, Ergenc I, Ozen Alahdab Y, Temizel A, Atug O. Improving the computer-aided estimation of ulcerative colitis severity according to Mayo endoscopic score by using regression-based deep learning. Inflamm Bowel Dis. 2023;29(9):1431\u20139. https:\/\/doi.org\/10.1093\/ibd\/izac226.","journal-title":"Inflamm Bowel Dis"},{"key":"435_CR12","doi-asserted-by":"crossref","unstructured":"Polat G, Ergenc I, Kani HT, Alahdab YO, Atug O, Temizel A. Class distance weighted cross-entropy loss for ulcerative colitis severity estimation. In: Annual conference on medical image understanding and analysis. Berlin: Springer; 2022. p. 157\u201371.","DOI":"10.1007\/978-3-031-12053-4_12"},{"key":"435_CR13","doi-asserted-by":"crossref","unstructured":"Wang J, Cheng Y, Chen J, Chen T, Chen D, Wu J. Ord2Seq: regarding ordinal regression as label sequence prediction. In: 2023 IEEE\/CVF international conference on computer vision (ICCV). 2023. p. 5842\u201352.","DOI":"10.1109\/ICCV51070.2023.00539"},{"key":"435_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.2973812","author":"D Chang","year":"2020","unstructured":"Chang D, Ding Y, Xie J, Bhunia AK, Li X, Ma Z, et al. The devil is in the channels: mutual-channel loss for fine-grained image classification. IEEE Trans Image Process. 2020. https:\/\/doi.org\/10.1109\/TIP.2020.2973812.","journal-title":"IEEE Trans Image Process"},{"key":"435_CR15","doi-asserted-by":"crossref","unstructured":"Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: co-designing and scaling ConvNets with masked autoencoders. In: 2023 IEEE\/CVF conference on computer vision and pattern recognition (CVPR). 2023. p. 16133\u201342.","DOI":"10.1109\/CVPR52729.2023.01548"},{"key":"435_CR16","doi-asserted-by":"crossref","unstructured":"Liu Z, Mao H, Wu CY, Feichtenhofer C, Darrell T, Xie S. A ConvNet for the 2020s. In: 2022 IEEE\/CVF conference on computer vision and pattern recognition (CVPR). 2022. p. 11966\u201376.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"435_CR17","unstructured":"Touvron H, Cord M, Douze M, Massa F, Sablayrolles A, J\u00e9gou H. Training data-efficient image transformers & distillation through attention. In: International conference on machine learning. PMLR. 2021. p. 10347\u201357."},{"key":"435_CR18","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, et al. Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision. 2021. p. 10012\u201322.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"435_CR19","doi-asserted-by":"crossref","unstructured":"Yu W, Zhou P, Yan S, Wang X. InceptionNeXt: when inception meets ConvNeXt. In: 2024 IEEE\/CVF conference on computer vision and pattern recognition (CVPR). 2024. p. 5672\u201383.","DOI":"10.1109\/CVPR52733.2024.00542"},{"key":"435_CR20","unstructured":"Polat G, Kani HT, Ergenc I, Alahdab YO, Temizel A, Atug O. Labeled images for ulcerative colitis (LIMUC) dataset. Zenodo 2022."},{"issue":"26","key":"435_CR21","doi-asserted-by":"publisher","first-page":"1625","DOI":"10.1056\/NEJM198712243172603","volume":"317","author":"KW Schroeder","year":"1987","unstructured":"Schroeder KW, Tremaine WJ, Ilstrup DM. Coated oral 5-aminosalicylic acid therapy for mildly to moderately active ulcerative colitis. A randomized study. N Engl J Med. 1987;317(26):1625\u20139. https:\/\/doi.org\/10.1056\/NEJM198712243172603.","journal-title":"N Engl J Med"},{"key":"435_CR22","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. p. 770\u20138.","DOI":"10.1109\/CVPR.2016.90"},{"key":"435_CR23","unstructured":"Goodfellow I, Warde-Farley D, Mirza M, Courville A, Bengio Y. Maxout networks. In: International conference on machine learning. PMLR 2013. p. 1319\u201327."},{"issue":"9","key":"435_CR24","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.1016\/0895-4356(93)90173-x","volume":"46","author":"P Graham","year":"1993","unstructured":"Graham P, Jackson R. The analysis of ordinal agreement data: beyond weighted kappa. J Clin Epidemiol. 1993;46(9):1055\u201362. https:\/\/doi.org\/10.1016\/0895-4356(93)90173-x.","journal-title":"J Clin Epidemiol"},{"issue":"8","key":"435_CR25","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006;27(8):861\u201374. https:\/\/doi.org\/10.1016\/j.patrec.2005.10.010.","journal-title":"Pattern Recognit Lett"},{"key":"435_CR26","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1007\/978-3-031-44992-5_10","volume-title":"Data engineering in medical imaging","author":"A Pyatha","year":"2023","unstructured":"Pyatha A, Xu Z, Ali S, et al. Vision transformer-based self-supervised learning for ulcerative colitis grading in colonoscopy. In: Bhattarai B, Ali S, Rau A, Nguyen A, Namburete A, Caramalau R, et al., editors. Data engineering in medical imaging. Cham: Springer; 2023. p. 102\u201310."},{"key":"435_CR27","unstructured":"Dermyer P, Kalra A, Schwartz M. EndoDINO: a foundation model for GI endoscopy. 2025. https:\/\/arxiv.org\/abs\/2501.05488"},{"key":"435_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102587","volume":"82","author":"M Turan","year":"2022","unstructured":"Turan M, Durmus F. UC-NfNet: deep learning-enabled assessment of ulcerative colitis from colonoscopy images. Med Image Anal. 2022;82:102587. https:\/\/doi.org\/10.1016\/j.media.2022.102587.","journal-title":"Med Image Anal"},{"key":"435_CR29","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering10121416","author":"S Wu","year":"2023","unstructured":"Wu S, Zhang R, Yan J, Li C, Liu Q, Wang L, et al. High-speed and accurate diagnosis of gastrointestinal disease: learning on endoscopy images using lightweight transformer with local feature attention. Bioengineering (Basel). 2023. https:\/\/doi.org\/10.3390\/bioengineering10121416.","journal-title":"Bioengineering (Basel)"},{"issue":"2","key":"435_CR30","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1364\/boe.473446","volume":"14","author":"J Gonzalez-Bueno Puyal","year":"2023","unstructured":"Gonzalez-Bueno Puyal J, Brandao P, Ahmad OF, Bhatia KK, Toth D, Kader R, et al. Spatio-temporal classification for polyp diagnosis. Biomed Opt Express. 2023;14(2):593\u2013607. https:\/\/doi.org\/10.1364\/boe.473446.","journal-title":"Biomed Opt Express"}],"container-title":["Health Information Science and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-026-00435-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13755-026-00435-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-026-00435-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T10:53:19Z","timestamp":1771152799000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13755-026-00435-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,15]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["435"],"URL":"https:\/\/doi.org\/10.1007\/s13755-026-00435-0","relation":{},"ISSN":["2047-2501"],"issn-type":[{"value":"2047-2501","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,15]]},"assertion":[{"value":"12 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no financial and personal relationships with other people or organizations that could inappropriately influence their work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"39"}}