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

Background and AimsArtificial 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 equivocal cases in ulcerative colitis (UC). MethodsA 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. ResultsNeutrophil density increased 3.5-fold in the LP and 15-fold within crypts in active UC (p<0.0001). Eosinophil density doubled and LP lymphocytes increased 1.4-fold. Active UC showed increased mucin depletion, crypt branching, and crypt abscesses, with reduced crypt solidity (p<0.0001 for all). PAIR-IBD metrics correlated with manual inflammatory and crypt injury scores (rs=0.23-0.72) and global indices (rs=0.27-0.65). Up to 89% of inconclusive cases aligned with remission-like profiles based on multiple independent AI metrics. ConclusionPAIR-IBD provides spatially resolved, quantitative assessment of inflammation and epithelial injury in UC, improving disease stratification and resolution of equivocal histology, with potential to support scoring consensus and improve accuracy of histological endpoints in clinical trials.