Detection, localisation, and quantification of neutrophils to assess disease activity and early response to therapy in ulcerative colitis: a novel AI-driven model.

Background: Histological remission (HR) is a key treatment goal in ulcerative colitis (UC), suggesting better disease management and treatment response. Absence of mucosal neutrophils is crucial to defining HR. However, the role of neutrophil quantity and localisation remains unclear. We aimed to develop a novel AI-driven pipeline to automate neutrophil detection, localisation, and quantification, supporting assessment of HR and treatment response. Methods: We developed an AI-driven pipeline by integrating and combining the outputs of two deep learning models to segment whole-slide images (WSIs) into epithelium, crypts and lamina propria and detect and quantify neutrophils. Optimal neutrophil density cut-offs to assess disease activity in UC patients from the AMAC phase 2 Mirikizumab trial (NCT02589665; 2015-2019) were identified and validated in the multicentre prospective PICaSSO cohort (2016-2019). Cut-offs to determine treatment response at weeks 12 and 52 were also evaluated. Findings: 303 WSIs of UC patients from the multicentre, randomised, double-blind, parallel-arm, placebo-controlled phase 2 clinical trial of Mirikizumab were analysed. The models yielded a 65.0% DICE Sørensen for region segmentation on average and 82.3% precision for neutrophil detection. A density cut-off Interpretation: This novel AI-based pipeline demonstrates strong potential to detect, localise, and quantify neutrophils to assess histological activity in clinical trials and real-world settings. Furthermore, it effectively stratifies treatment response, offering a reliable and objective framework for personalised UC management. Funding: This paper has been supported by research grants from Eli Lilly and Company.