Machine Learning Models for the Assessment of the Mayo Endoscopic Score in Ulcerative Colitis Trial Endpoints: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The Mayo endoscopic score (MES) provides a criterion-based, but still subjective, human assessment of endoscopy and related endpoints in therapeutic clinical trials in ulcerative colitis (UC). A novel solution to address issues of reproducibility is the use of machine learning (ML) models to standardize MES evaluations. Broader applicability of this solution requires an understanding of the models and related performance characteristics. The objective of this study is to provide a systematic review on training and testing of ML MES prediction models on full-length endoscopic videos from patients with UC. METHODS: PubMed/MEDLINE, EMBASE, and Web of Science were systematically searched on December 31, 2024, and supplemented by reference checks and Google search to identify studies on training or testing of ML models to produce an automated MES grade on endoscopic procedure videos in UC. RESULTS: A total of 7 studies met the inclusion criteria, and of those, 5 were eligible for reporting on model performance. Accuracy in predicting ordinal MES grades (0, 1, 2, 3) ranged from 56.8% to 83.3%. Accuracy in predicting MES 0, 1 vs 2, 3 and MES 0 vs 1, 2, 3 (each aligned with a definition of endoscopic improvement and remission in trials) ranged from 84% to 90.2% and from 90% to 95.5%, respectively. CONCLUSIONS: Our review demonstrates strong performance characteristics of ML models to assess the MES on endoscopic videos in UC, potentially offering a standardized and reproducible solution to measure endoscopic severity. Further research will investigate the impact of this technology on clinical trial outcomes.
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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 | 0.023 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".