Artificial intelligence in histopathology of inflammatory bowel disease: Toward objective and reproducible assessment of disease activity

Table 2 Summary of key studies on artificial intelligence in inflammatory bowel disease histopathology

Ref.	AI task	Cohort (disease)	Key finding	Implication
Rymarczyk et al[31], 2024	Crypt segmentation using U-Net	385 WSIs (UC and CD)	Quantified crypt distortion correlated with endoscopic severity and predicted clinical outcomes	AI provides objective architectural metrics of chronic damage
Rymarczyk et al[31], 2024	WSI classification for Geboes score	913 WSIs (UC)	CNN achieved AUC > 0.98 for discriminating active disease (Geboes ≥ 3B)	High accuracy in automating a complex histological score
Minea et al[32], 2025	WSI classification for therapy response	161 patients (UC)	Baseline histology-based CNN predicted vedolizumab response (AUC 0.79)	Histology contains prognostic signals for biologic therapy outcomes
Villanacci et al[33], 2023[33]	Feature-based model for relapse risk	88 patients (CD)	AI-quantified density of submucosal lymphoid aggregates predicted post-surgical relapse	Identified a novel histologic prognostic biomarker
Rubin et al[34], 2025	Multicenter validation of a Nancy Index predictor	583 WSIs from five centers (UC)	Model generalized well across centers (weighted kappa 0.70 with experts)	Demonstrates potential for cross-institutional standardization

AI: Artificial intelligence; AUC: Area under the curve; CD: Crohn’s disease; CNN: Convolutional neural network; UC: Ulcerative colitis; WSI: Whole-slide imaging.