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

Abstract

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. 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 Nancy index score were conducted and assessed using the intraclass correlation coefficient (ICC). The 200-image dataset was divided into two groups (80% was used for training and 20% for testing). Intraclass correlation coefficient statistical analyses were performed to evaluate the AI tool and used as a reference to calculate the accuracy. The average ICC among the histopathologists was 89.3 and the average ICC between histopathologists and the AI tool was 87.2. The AI tool was found to be highly correlated with histopathologists. The high correlation of performance of the AI method suggests promising potential for inflammatory bowel disease clinical applications. A standardized automated histological AI-driven scoring system can potentially be used in daily inflammatory bowel disease practice to reduce training needs and resource use, eliminate the subjectivity of the pathologists, and assess disease severity for treatment decisions.