Development of a Novel Ulcerative Colitis Endoscopic Mayo Score Prediction Model Using Machine Learning.

ABSTRACT:

Background and aims

Endoscopic assessment is a co-primary end point in inflammatory bowel disease registration trials, yet it is subject to inter- and intraobserver variability. We present an original machine learning approach to Endoscopic Mayo Score (eMS) prediction in ulcerative colitis and report the model's performance in differentiating key levels of endoscopic disease activity on full-length procedure videos.

Methods

Seven hundred ninety-three full-length videos with centrally-read eMS were obtained from 249 patients with ulcerative colitis, who participated in a phase II trial evaluating mirikizumab (NCT02589665). A video annotation approach was established to extract mucosal features and associated eMS classification labels from each video to be used as inputs for m

SUBMITTER: Rubin DT 

PROVIDER: S-EPMC11307476 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature