Peer-Reviewed Publication
Inflamm Bowel Dis2026;32(1):159-168.January 1, 2026Journal Article
Machine Learning Models for the Assessment of the Mayo Endoscopic Score in Ulcerative Colitis Trial Endpoints: A Systematic Review.
David T Rubin1, Walter Reinisch2, Neeraj Narula3, Daniel R Colucci4, William Eastman5, Klaus Gottlieb5, Ana P Lacerda6, F Stephen Laroux7, Irene Modesto8, Emma E Navajas4, Charles C Owen5, Yeli Wang4, Shrujal Baxi4
1Inflammatory Bowel Disease Center, University of Chicago Medicine, Chicago, IL, United States.
2Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria.
3Farncombe Family Digestive Health Research Institute, Division of Gastroenterology, Department of Medicine, McMaster University, Hamilton, ON, Canada.
4Iterative Health Inc., Cambridge, MA, United States.
5Eli Lilly and Company, Indianapolis, IN, United States.
6AbbVie Inc., North Chicago, IL, United States.
7AbbVie Bioresearch Center, Worcester, MA, United States.
8Pfizer Inc., New York, NY, United States.
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 o…
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