Performance of a Deep Learning Model vs Human Reviewers in Grading Endoscopic Disease Severity of Patients With Ulcerative Colitis.

ABSTRACT:

Importance

Assessing endoscopic disease severity in ulcerative colitis (UC) is a key element in determining therapeutic response, but its use in clinical practice is limited by the requirement for experienced human reviewers.

Objective

To determine whether deep learning models can grade the endoscopic severity of UC as well as experienced human reviewers.

Design, setting, and participants

In this diagnostic study, retrospective grading of endoscopic images using the 4-level Mayo subscore was performed by 2 independent reviewers with score discrepancies adjudicated by a third reviewer. Using 16 514 images from 3082 patients with UC who underwent colonoscopy at a single tertiary care referral center in the United States between January 1, 2007, and December 31, 2017, a

SUBMITTER: Stidham RW 

PROVIDER: S-EPMC6537821 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature