Development and validation of a deep learning-based approach to predict the Mayo endoscopic score of ulcerative colitis.

ABSTRACT:

Background

The ulcerative colitis (UC) Mayo endoscopy score is a useful tool for evaluating the severity of UC in patients in clinical practice.

Objectives

We aimed to develop and validate a deep learning-based approach to automatically predict the Mayo endoscopic score using UC endoscopic images.

Design

A multicenter, diagnostic retrospective study.

Methods

We collected 15120 colonoscopy images of 768 UC patients from two hospitals in China and developed a deep model based on a vision transformer named the UC-former. The performance of the UC-former was compared with that of six endoscopists on the internal test set. Furthermore, multicenter validation from three hospitals was also carried out to evaluate UC-former's generalization performance.

Results

SUBMITTER: Qi J 

PROVIDER: S-EPMC10214058 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature