Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data.

ABSTRACT:

Introduction

The Mayo Clinic Endoscopic Subscore is a commonly used grading system to assess the severity of ulcerative colitis. Correctly grading colonoscopies using the Mayo Clinic Endoscopic Subscore is a challenging task, with suboptimal rates of interrater and intrarater variability observed even among experienced and sufficiently trained experts. In recent years, several machine learning algorithms have been proposed in an effort to improve the standardization and reproducibility of Mayo Clinic Endoscopic Subscore grading.

Methods

Here we propose an end-to-end fully automated system based on deep learning to predict a binary version of the Mayo Clinic Endoscopic Subscore directly from raw colonoscopy videos. Differently from previous studies, the proposed method mimics

SUBMITTER: Gutierrez Becker B 

PROVIDER: S-EPMC7917417 | biostudies-literature | 2021 Jan-Dec

REPOSITORIES: biostudies-literature