ArticleBMC medical informatics and decision making2022
Automatically detecting Crohn's disease and Ulcerative Colitis from endoscopic imaging.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed, 31 citations in OpenAlex.
- Application of Explainable Artificial Intelligence Based on Visual Explanation in Digestive Endoscopy.Bioengineering (Basel, Switzerland) · 2025Review
- Explainable artificial intelligence for personalized management of inflammatory bowel disease: A minireview of recent advances.World journal of gastroenterology · 2025Review
- Artificial Intelligence in Inflammatory Bowel Disease Endoscopy.Diagnostics (Basel, Switzerland) · 2025Review
- Machine learning in the differential diagnosis of ulcerative colitis and Crohn's disease: a systematic review.Translational gastroenterology and hepatology · 2025Review
- Artificial intelligence in inflammatory bowel disease: innovations in diagnosis, monitoring, and personalized care.Therapeutic advances in gastroenterology · 2025Review
- Automated inflammatory bowel disease detection using wearable bowel sound event spotting.Frontiers in digital health · 2025Article
- A hybrid approach of vision transformers and CNNs for detection of ulcerative colitis.Scientific reports · 2024Article
- Immunomodulatory Effects of a Probiotic Mixture: Alleviating Colitis in a Mouse Model through Modulation of Cell Activation Markers and the Gut Microbiota.International journal of molecular sciences · 2024Article
- Artificial intelligence and machine learning technologies in ulcerative colitis.Therapeutic advances in gastroenterology · 2024Review
- Advancements in the use of AI in the diagnosis and management of inflammatory bowel disease.Frontiers in robotics and AI · 2024Review
- Endoscopy-based IBD identification by a quantized deep learning pipeline.BioData mining · 2023Article
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
Abstract
backgroundThe SI-CURA project (Soluzioni Innovative per la gestione del paziente e il follow up terapeutico della Colite UlceRosA) is an Italian initiative aimed at the development of artificial intelligence solutions to discriminate pathologies of different nature, including inflammatory bowel disease (IBD), namely Ulcerative Colitis (UC) and Crohn's disease (CD), based on endoscopic imaging of patients (P) and healthy controls (N).
methodsIn this study we develop a deep learning (DL) prototype to identify disease patterns through three binary classification tasks, namely (1) discriminating positive (pathological) samples from negative (healthy) samples (P vs N); (2) discrimination between Ulcerative Colitis and Crohn's Disease samples (UC vs CD) and, (3) discrimination between Ulcerative Colitis and negative (healthy) samples (UC vs N).
resultsThe model derived from our approach achieves a high performance of Matthews correlation coefficient (MCC) > 0.9 on the test set for P versus N and UC versus N, and MCC > 0.6 on the test set for UC versus CD.
conclusionOur DL model effectively discriminates between pathological and negative samples, as well as between IBD subgroups, providing further evidence of its potential as a decision support tool for endoscopy-based diagnosis.
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.