Artificial intelligence and inflammatory bowel disease: Where are we going?

Table 2 Artificial intelligence application for histological assessment of ulcerative colitis

Ref.	Study design	Population	Outcome	Results
Vande Casteele et al[64], 2022	Cohort study	Colonic biopsies from 88 UC patients with histologically active disease	To assess a DL machine in quantifying eosinophils in colonic biopsies and validate against a pathologist’s count	The AI system highly agreed with manual eosinophil count by pathologists (ICC 0.81-0.92)
Peyrin-Biroulet et al[67], 2022	Cohort study	200 histological images of UC biopsies	To evaluate an AI algorithm in assessing histological disease activity according to the Nancy index	The CNN model had an excellent agreement with pathologists in the assessment of the Nancy index (ICC 0.84)
Villanacci et al[66], 2022	Cohort study	614 biopsies from 307 UC patients	To test a CNN-based CADe system for evaluating HR based on PHRI, Robarts, and Nancy indexes	The CADe system accurately assessed HR (sensitivity 89%, specificity 85% for PHRI) and similar performance for Nancy and Robarts

AI: Artificial intelligence; CADe: Computer-aided detection; CNN: Convolution neural networks; DL: Deep learning; HR: Histological remission; ICC: Interclass correlation coefficient; PHRI: PICaSSO histologic remission index; UC: Ulcerative colitis.