An artificial intelligence-driven scoring system to measure histological disease activity in ulcerative colitis.

ABSTRACT:

Background and aims

Assessment and scoring of histological images in Ulcerative colitis (UC) is prone to inter- and intra-observer variability. This study aimed to investigate whether an artificial intelligence (AI) system developed using image processing and machine learning algorithms could measure histological disease activity based on the Nancy index.

Methods

A total of 200 histological images of patients with UC were used in this study. A novel AI algorithm was developed using state-of-the-art image processing and machine learning algorithms based on deep learning and feature extraction. The cell regions of each image, followed by the Nancy index, were manually annotated and measured independently by four histopathologists. Manual and AI-automated measurements of the Na

SUBMITTER: Peyrin-Biroulet L 

PROVIDER: S-EPMC11485311 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature