Deployment of an Artificial Intelligence Histology Tool to Aid Qualitative Assessment of Histopathology Using the Nancy Histopathology Index in Ulcerative Colitis

Inflamm Bowel Dis. 2024 Sep 16:izae204. doi: 10.1093/ibd/izae204. Online ahead of print.

Abstract

Background: Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by increased stool frequency, rectal bleeding, and urgency. To streamline the quantitative assessment of histopathology using the Nancy Index in UC patients, we developed a novel artificial intelligence (AI) tool based on deep learning and tested it in a proof-of-concept trial. In this study, we report the performance of a modified version of the AI tool.

Methods: Nine sites from 6 countries were included. Patients were aged ≥18 years and had UC. Slides were prepared with hematoxylin and eosin staining. A total of 791 images were divided into 2 groups: 630 for training the tool and 161 for testing vs expert histopathologist assessment. The refined AI histology tool utilized a 4-neural network structure to characterize images into a series of cell and tissue type combinations and locations, and then 1 classifier module assigned a Nancy Index score.

Results: In comparison with the proof-of-concept tool, each feature demonstrated an improvement in accuracy. Confusion matrix analysis demonstrated an 80% correlation between predicted and true labels for Nancy scores of 0 or 4; a 96% correlation for a true score of 0 being predicted as 0 or 1; and a 100% correlation for a true score of 2 being predicted as 2 or 3. The Nancy metric (which evaluated Nancy Index prediction) was 74.9% compared with 72.3% for the proof-of-concept model.

Conclusions: We have developed a modified AI histology tool in UC that correlates highly with histopathologists' assessments and suggests promising potential for its clinical application.

Keywords: artificial intelligence; histopathology; ulcerative colitis.

Plain language summary

This multicenter study deployed an artificial intelligence tool based on machine learning to scan histopathology slides from ulcerative colitis biopsies and assign a Nancy Histopathology Index. The performance of the tool was similar to that of a panel of expert histopathologists.

Grants and funding