Abstract
Accurate assessment of disease severity from endoscopy videos in ulcerative colitis (UC) is crucial for evaluating drug efficacy in clinical trials. Severity is often measured by the Mayo Endoscopic Subscore (MES) and Ulcerative Colitis Endoscopic Index of Severity (UCEIS) score. However, expert MES/UCEIS annotation is time-consuming and susceptible to inter-rater variability, factors addressable by automation. Automation attempts with frame-level labels face challenges in fully-supervised solutions due to the prevalence of video-level labels in clinical trials. CNN-based weakly-supervised models (WSL) with end-to-end (e2e) training lack generalization to new disease scores and ignore spatio-temporal information crucial for accurate scoring. To address these limitations, we propose “Arges”, a deep learning framework that utilizes a transformer with positional encoding to incorporate spatio-temporal information from frame features to estimate disease severity scores in endoscopy video. Extracted features are derived from a foundation model (ArgesFM), pre-trained on a large diverse dataset from multiple clinical trials (61M frames, 3927 videos). We evaluate four UC disease severity scores, including MES and three UCEIS component scores. Test set evaluation indicates significant improvements, with F1 scores increasing by 4.1% for MES and 18.8%, 6.6%, 3.8% for the three UCEIS component scores compared to state-of-the-art methods. Prospective validation on previously unseen clinical trial data further demonstrates the model’s successful generalization.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Allez, M., et al.: A phase 2b, randomised, double-blind, placebo-controlled, parallel-arm, multicenter study evaluating the safety and efficacy of tesnatilimab in patients with moderately to severely active Crohn’s disease. J. Crohn’s Colitis, jjad047 (2023)
Byrne, M.F., et al.: Application of deep learning models to improve ulcerative colitis endoscopic disease activity scoring under multiple scoring systems. J. Crohns Colitis 17(4), 463–471 (2023)
Caron, M., et al.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9650–9660 (2021)
Daperno, M., et al.: Inter-observer agreement in endoscopic scoring systems: preliminary report of an ongoing study from the Italian group for inflammatory bowel disease (IG-IBD). Dig. Liver Dis. 46(11), 969–973 (2014)
Dosovitskiy, A., et al.: An image is worth 16 \(\times \) 16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Hirsch, R., et al.: Self-supervised learning for endoscopic video analysis. In: Greenspan, H., et al. (eds.) International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 569–578. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-43904-9_55
Iacucci, M., et al.: A virtual chromoendoscopy artificial intelligence system to detect endoscopic and histologic activity/remission and predict clinical outcomes in ulcerative colitis. Endoscopy 55(04), 332–341 (2023)
Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, pp. 2127–2136. PMLR (2018)
Oquab, M., et al.: DINOv2: learning robust visual features without supervision. arXiv preprint arXiv:2304.07193 (2023)
Paul, S., Roy, S., Roy-Chowdhury, A.K.: W-TALC: weakly-supervised temporal activity localization and classification. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 588–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_35
Peyrin-Biroulet, L., et al.: Guselkumab in patients with moderately to severely active ulcerative colitis: QUASAR Phase 2b induction study. Gastroenterology 165(6), 1443–1457 (2023)
Polat, G., Ergenc, I., Kani, H.T., Alahdab, Y.O., Atug, O., Temizel, A.: Class distance weighted cross-entropy loss for ulcerative colitis severity estimation. In: Yang, G., Aviles-Rivero, A., Roberts, M., Schonlieb, C.B. (eds.) Annual Conference on Medical Image Understanding and Analysis, pp. 157–171. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-12053-4_12
Principi, M., et al.: Inter-observer agreement of a new endoscopic score for ulcerative colitis activity: preliminary experience. Diagnostics 10(4), 213 (2020)
Rubin, D.T., et al.: Development of a novel ulcerative colitis endoscopic Mayo score prediction model using machine learning. Gastro Hep Advances (2023)
Sands, B.E., et al.: Ustekinumab versus adalimumab for induction and maintenance therapy in biologic-naive patients with moderately to severely active Crohn’s disease: a multicentre, randomised, double-blind, parallel-group, phase 3b trial. Lancet 399(10342), 2200–2211 (2022)
Sands, B.E., et al.: Peficitinib, an oral janus kinase inhibitor, in moderate-to-severe ulcerative colitis: results from a randomised, phase 2 study. J. Crohns Colitis 12(10), 1158–1169 (2018)
Sands, B.E., et al.: Ustekinumab as induction and maintenance therapy for ulcerative colitis. N. Engl. J. Med. 381(13), 1201–1214 (2019)
Schroeder, K.W., Tremaine, W.J., Ilstrup, D.M.: Coated oral 5-aminosalicylic acid therapy for mildly to moderately active ulcerative colitis. N. Engl. J. Med. 317(26), 1625–1629 (1987)
Schwab, E., et al.: Automatic estimation of ulcerative colitis severity from endoscopy videos using ordinal multi-instance learning. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 10(4), 425–433 (2022)
Stidham, R.W., et al.: Using computer vision to improve endoscopic disease quantification in therapeutic clinical trials of ulcerative colitis. Gastroenterology 166(1), 155–167 (2024)
Stidham, R.W., et al.: Performance of a deep learning model vs human reviewers in grading endoscopic disease severity of patients with ulcerative colitis. JAMA Netw. Open 2(5), e193963–e193963 (2019)
Tian, Y., et al.: Contrastive transformer-based multiple instance learning for weakly supervised polyp frame detection. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 88–98. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16437-8_9
Travis, S.P., et al.: Developing an instrument to assess the endoscopic severity of ulcerative colitis: the Ulcerative Colitis Endoscopic Index of Severity (UCEIS). Gut 61(4), 535–542 (2012)
Vasilakakis, M.D., Diamantis, D., Spyrou, E., Koulaouzidis, A., Iakovidis, D.K.: Weakly supervised multilabel classification for semantic interpretation of endoscopy video frames. Evol. Syst. 11, 409–421 (2020)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Wang, L., Xiong, Y., Lin, D., Van Gool, L.: UntrimmedNets for weakly supervised action recognition and detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4325–4334 (2017)
Wang, Z., Liu, C., Zhang, S., Dou, Q.: Foundation model for endoscopy video analysis via large-scale self-supervised pre-train. In: Greenspan, H., et al. (eds.) International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 101–111. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-43996-4_10
Xu, Z., Ali, S., Gupta, S., Leedham, S., East, J.E., Rittscher, J.: Patch-level instance-group discrimination with pretext-invariant learning for colitis scoring. In: Lian, C., Cao, X., Rekik, I., Xu, X., Cui, Z. (eds.) International Workshop on Machine Learning in Medical Imaging, pp. 101–110. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-21014-3_11
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
All authors were employees of Janssen R&D, LLC, and may own company stock/stock options.
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chaitanya, K. et al. (2025). Arges: Spatio-Temporal Transformer for Ulcerative Colitis Severity Assessment in Endoscopy Videos. In: Xu, X., Cui, Z., Rekik, I., Ouyang, X., Sun, K. (eds) Machine Learning in Medical Imaging. MLMI 2024. Lecture Notes in Computer Science, vol 15242. Springer, Cham. https://doi.org/10.1007/978-3-031-73290-4_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-73290-4_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-73292-8
Online ISBN: 978-3-031-73290-4
eBook Packages: Computer ScienceComputer Science (R0)
