Artificial Intelligence for Classification of Endoscopic Severity of Inflammatory Bowel Disease: A Systematic Review and Critical Appraisal
Bibliographic record
Abstract
BACKGROUND: Endoscopic scoring indices for ulcerative colitis and Crohn's disease are subject to inter-endoscopist variability. There is increasing interest in the development of deep learning models to standardize endoscopic assessment of intestinal diseases. Here, we summarize and critically appraise the literature on artificial intelligence-assisted endoscopic characterization of inflammatory bowel disease severity. METHODS: A systematic search of Ovid MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and IEEE Xplore was performed to identify reports of AI systems used for endoscopic severity classification of IBD. Selected studies were critically appraised for methodological and reporting quality using APPRAISE-AI. RESULTS: Thirty-one studies published between 2019 and 2024 were included. Of 31 studies, 28 studies examined endoscopic classification of ulcerative colitis and 3 examined Crohn's disease. Researchers sought to accomplish a wide range of classification tasks, including binary and multilevel classification, based on still images or full-length colonoscopy videos. Overall scores for study quality ranged from 41 (moderate quality) to 64 (high quality) out of 100, with 28 out of 31 studies within the moderate quality range. The highest-scoring domains were clinical relevance and reporting quality, while the lowest-scoring domains were robustness of results and reproducibility. CONCLUSIONS: Multiple AI models have demonstrated the potential for clinical translation for ulcerative colitis. Research concerning the endoscopic severity assessment of Crohn's disease is limited and should be further explored. More rigorous external validation of AI models and increased transparency of data and codes are needed to improve the quality of AI studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".