AI quantification of inflammatory and architectural features in ulcerative colitis distinguishes active disease from remission

ABSTRACT Background and Aims Artificial intelligence (AI) is increasingly applied to histological assessment in inflammatory bowel disease (IBD), but most approaches quantify features in isolation and ignore their anatomical location within the mucosa. We developed and validated PAIR-IBD (Perspectum AI Reading in IBD), an AI system that quantifies inflammatory cell populations, crypt injury, and epithelial damage within defined mucosal compartments to distinguish active disease, remission, and inconclusive cases in ulcerative colitis (UC). Methods A deep learning ensemble was trained on three IBD biopsy datasets to identify lymphocytes, neutrophils, eosinophils, and plasma cells, and to segment crypts, lamina propria (LP), and muscularis mucosae. Inflammatory cell densities and crypt injury metrics (mucin depletion, solidity, roughness, branching, and abscess formation) were quantified. PAIR-IBD outputs were compared between histologically active and remissive UC, evaluated in inconclusive cases, and correlated with manual pathology grading. Results Neutrophil density increased 3.5-fold in the LP and 15-fold within crypts in active UC (p