Global Roll-Out and Continued Multi-Site Validation of the Artificial Intelligence Histology Instrument for Qualitative Assessment of Histopathology in Ulcerative Colitis.

BACKGROUND: Histological assessment of mucosal biopsies in patients with ulcerative colitis (UC) can determine the activity and extent of disease and assess response to treatment. However, its widespread adoption is limited by the time required for the advanced GI specialty training to handle and review histopathology digital images, inter- and intra-observer variability, and cost associated with the interpretation of data. Artificial intelligence, and specifically machine learning‒driven medical image processing, have emerged to help standardise and automate histopathologic assessments. METHODS: In this global study conducted with participation of 38 sites in 19 countries (global roll-out phase), we collected histopathologic slides prepared from biopsy samples from patients with UC to train an AI model to recognise various cell types and assign a disease activity score based on the Nancy histological index (NHI). Results were compared with findings from a previous iteration of the machine learning model (pilot roll-out phase). RESULTS: In total, 850 tiles were analysed and used for training, validation, and testing. Model quality, assessed using the Nancy metric, improved from 61.50% in the pilot roll-out phase to 74.82% in the current global roll-out phase. Cell detection quality (F1-score metric) also increased from 27.50% (pilot roll-out) to 58.80% (global roll-out). CONCLUSIONS: In this global roll-out, the quality of the AI model was significantly improved for both NHI scores and cell detection. Further development and implementation of the model at the participating international sites continues and may lead to a valuable and scalable tool for the analysis of disease activity in UC.