ArticleTherapeutic advances in gastrointestinal endoscopy
Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data.
Article in Therapeutic advances in gastrointestinal endoscopy. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 60 citations in OpenAlex.
- Artificial intelligence in endoscopy related to inflammatory bowel disease: A systematic review.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2024Pooled it
- A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.Inflammatory bowel diseases · 2022Pooled it
- An automatic severity grading system using confocal laser endomicroscopy to evaluate inflammatory activity of ulcerative colitis: a prospective study.Scientific reports · 2025Article
- Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis.World journal of gastroenterology · 2025Review
- Artificial Intelligence in Inflammatory Bowel Disease Endoscopy.Diagnostics (Basel, Switzerland) · 2025Review
- Article
- A hybrid approach of vision transformers and CNNs for detection of ulcerative colitis.Scientific reports · 2024Article
- Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications.Journal of clinical medicine · 2024Review
- The Role of Artificial Intelligence in the Diagnosis and Treatment of Ulcerative Colitis.Diagnostics (Basel, Switzerland) · 2024Review
- Artificial intelligence and machine learning technologies in ulcerative colitis.Therapeutic advances in gastroenterology · 2024Review
- A systematic review and meta-analysis of artificial intelligence-diagnosed endoscopic remission in ulcerative colitis.iScience · 2023Article
- CB-HRNet: A Class-Balanced High-Resolution Network for the evaluation of endoscopic activity in patients with ulcerative colitis.Clinical and translational science · 2023Article
- New Technologies in Digestive Endoscopy for Ulcerative Colitis Patients.Biomedicines · 2023Review
- Deep learning model for distinguishing Mayo endoscopic subscore 0 and 1 in patients with ulcerative colitis.Scientific reports · 2023Article
- Artificial intelligence in inflammatory bowel disease: implications for clinical practice and future directions.Intestinal research · 2023Review
- The Role of Artificial Intelligence in Monitoring Inflammatory Bowel Disease-The Future Is Now.Diagnostics (Basel, Switzerland) · 2023Review
- A systematic review on application of deep learning in digestive system image processing.The Visual computer · 2023Review
- Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions.NPJ digital medicine · 2022Review
- Chemoprevention of Colitis-Associated Dysplasia or Cancer in Inflammatory Bowel Disease.Gut and liver · 2022Review
- PHFBioengineering (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe Mayo Clinic Endoscopic Subscore is a commonly used grading system to assess the severity of ulcerative colitis. Correctly grading colonoscopies using the Mayo Clinic Endoscopic Subscore is a challenging task, with suboptimal rates of interrater and intrarater variability observed even among experienced and sufficiently trained experts. In recent years, several machine learning algorithms have been proposed in an effort to improve the standardization and reproducibility of Mayo Clinic Endoscopic Subscore grading.
methodsHere we propose an end-to-end fully automated system based on deep learning to predict a binary version of the Mayo Clinic Endoscopic Subscore directly from raw colonoscopy videos. Differently from previous studies, the proposed method mimics the assessment done in practice by a gastroenterologist, that is, traversing the whole colonoscopy video, identifying visually informative regions and computing an overall Mayo Clinic Endoscopic Subscore. The proposed deep learning-based system has been trained and deployed on raw colonoscopies using Mayo Clinic Endoscopic Subscore ground truth provided only at the colon section level, without manually selecting frames driving the severity scoring of ulcerative colitis. RESULTS AND
conclusionOur evaluation on 1672 endoscopic videos obtained from a multisite data set obtained from the etrolizumab Phase II Eucalyptus and Phase III Hickory and Laurel clinical trials, show that our proposed methodology can grade endoscopic videos with a high degree of accuracy and robustness (Area Under the Receiver Operating Characteristic Curve = 0.84 for Mayo Clinic Endoscopic Subscore ⩾ 1, 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 2 and 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 3) and reduced amounts of manual annotation. PLAIN LANGUAGE SUMMARY:
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.