Development and Validation of a Deep Neural Network for Accurate Identification of Endoscopic Images From Patients With Ulcerative Colitis and Crohn's Disease.

ABSTRACT:

Background and aim

The identification of ulcerative colitis (UC) and Crohn's disease (CD) is a key element interfering with therapeutic response, but it is often difficult for less experienced endoscopists to identify UC and CD. Therefore, we aimed to develop and validate a deep learning diagnostic system trained on a large number of colonoscopy images to distinguish UC and CD.

Methods

This multicenter, diagnostic study was performed in 5 hospitals in China. Normal individuals and active patients with inflammatory bowel disease (IBD) were enrolled. A dataset of 1,772 participants with 49,154 colonoscopy images was obtained between January 2018 and November 2020. We developed a deep learning model based on a deep convolutional neural network (CNN) in the examination. To gener

SUBMITTER: Ruan G 

PROVIDER: S-EPMC8974241 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature