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

Table 1 Core artificial techniques and their applications in inflammatory bowel disease histopathology

AI technique	Description	Primary application in IBD	Key advantage
Semantic segmentation	Classifies each pixel in an image into a predefined class (e.g., crypt epithelium, lamina propria, lumen)	Crypt segmentation and architectural analysis; Quantifies crypt density, distortion, branching, and atrophy	Provides a comprehensive, structural map of the mucosa; Enables precise measurement of architectural parameters
Instance segmentation	Identifies and delineates each individual object instance (e.g., each separate crypt, each inflammatory cell)	Individual crypt isolation and inflammatory cell detection/quantification	Allows for per-object analysis (size, shape of each crypt) and precise cell counting (neutrophils, eosinophils)
Object detection	Identifies and locates objects within an image using bounding boxes	Rapid identification of regions of interest, such as areas with severe activity or ulceration	Efficiently guides pathologist attention or focuses deeper analysis on most relevant areas
Whole-slide classification	Assigns a single label or score to an entire WSI	Direct prediction of global histological scores (e.g., Geboes ≥ 3.0, Nancy ≥ 2) or remission status	Automates scoring workflow, reduces time-to-diagnosis, and standardizes output

AI: Artificial intelligence; IBD: Inflammatory bowel disease; WSI: Whole-slide imaging.