Artificial intelligence in inflammatory bowel disease: Current applications and future directions

Table 2 Artificial intelligence-enabled digital pathology for histological assessment in inflammatory bowel diseases

Ref.	Disease/number of patients	Type of study	Number of training samples	Number of test samples	AI/model	Main findings
Vande Casteele et al[66]	UC/88 patients	Retrospective, single center	20 tissue regions	88 biopsies	DL	Performance in identifying eosinophil counts: WSI (sensitivity = 86.4%, accuracy = 91.8%, F1 score = 0.89); Strong agreement with four human experts (ICC: 0.805-0.917)
Gui et al[69]	UC/307 patients	Prospective, multicenter	97 biopsies	41 biopsies	CAD-CNN	Discrimination between HR (PHRI < 1) and non-remission (PHRI ≥ 1) based on the presence of neutrophils (sensitivity = 78.0%, specificity = 91.7%, accuracy = 86.0%, ICC = 0.840)
Ohara et al[71]	UC/114 patients	Retrospective, single center	2300 WSIs	114 biopsies	DL	Rate of relapse higher for GCR ≤ 12% compared to GCR > 12% (45.0% vs 6.5%, P < 0.010)
Najdawi et al[68]	UC/577 patients	Retrospective, single center	512 WSIs	308 WSIs	CNN-RFC	HR prediction (NHI ≤ 1, accuracy = 97.0%) was comparable to expert pathologist assessments (κ = 0.910, Spearman’s correlation ρ = 0.890, P < 0.010)
Iacucci et al[70]	UC/273 patients	Prospective, multicenter	118 biopsies	375 biopsies (1); 154 biopsies (2)	CAD-CNN	(1) Performance in distinguishing HR from active inflammation: RHI (AUC = 0.850, accuracy = 80.0%, sensitivity = 94.0%, specificity = 76.0%); NHI (AUC = 0.860, accuracy = 81.0%, sensitivity = 89.0%, specificity = 79.0%); PHRI (AUC = 0.870, accuracy = 87.0%, sensitivity = 89.0%, specificity = 85.0%); and (2) The hazard ratio for disease recurrence according to PHRI was higher for AI assessment compared with pathologist evaluation (4.64 vs 3.56, P < 0.001)
Peyrin-Biroulet et al[73]	UC/NR	Retrospective, single center	160 WSIs	40 WSIs	CNN	The average ICC between histopathologists and the AI tool for histological assessment based on NHI (ICC = 0.872)
Ohara et al[72]	UC/96 patients	Prospective, single center	11260 patches	135 WSIs	DL	Histological evaluation based on neutrophil quantification in WSI (accuracy = 77.0%, F1 score = 79.0%). Prediction of histological scores (PHRI, NHI) by AI showed strong correlation with pathological diagnoses (Spearman’s ρ = 0.680-0.800, P < 0.050)
Klein et al[78]	CD/105 patients	Retrospective, single center	Biopsies NR	Biopsies NR	NNET	Differentiation of clinical phenotypes (sensitivity = 78.0%, specificity = 77.0%). Prediction of surgical intervention (sensitivity = 80.0%, specificity = 91.0%)
Kiyokawa et al[76]	CD/68 patients	Retrospective, single center	619464 tile images	308705 tile images	CNN	Adipocyte shrinkage and increased mast cell infiltration in sub-serosal adipose tissue anticipate postoperative recurrence (AUC = 0.995, accuracy = 96.9%, precision = 96.4%, sensitivity = 96.5%)
Wang et al[77]	CD/205 patients	Retrospective, multicenter	310 WSIs	278 WSIs	DL-CNN	Severity of myenteric plexitis (accuracy = 83.3%) and postoperative recurrence prediction (AUC = 0.980)
Rymarczyk et al[79]	UC/887 patients; CD/302 patients	Retrospective, multicenter	2696 biopsies	800 biopsies	RNN, FV + RF, SA-AbMILP	SA-AbMILP for automated histological assessment of: GS: Accuracy: 65.0%-85.0% (κ = 0.440-0.680); GHAS: Colon accuracy: 80.0%-89.0% (κ = 0.540-0.650); Ileum accuracy 65.0%-82.0% (κ = 0.460-0.670)
Furlanello et al[74]	IBD/52 patients	Prospective, single center	4981 WSIs	356 biopsies	DL	Automated quantification of basal plasmacytosis discriminates IBD from non-IBD (accuracy = 90.0%)

AI: Artificial intelligence; CD: Crohn’s disease; IBD: Inflammatory bowel disease; UC: Ulcerative colitis; NR: Not reported; WSI: Whole slide image; CNN: Convolutional neural network; CAD: Computer-aided diagnosis; DL: Deep learning; RFC: Random forests classifier; NNET: Neural network; RNN: Recurrent neural network; FV + RF: Fisher vector with random forest; SA-AbMILP: Self-attention-based multi-instance learning pooling; ICC: Interclass correlation coefficients; HR: Histological remission; PHRI: Paddington international virtual chromoendoscopy score histologic remission index; GCR: Goblet cell ratio; NHI: Nancy histological index; RHI: Robarts histologic index; AUC: Area under the curve; GS: Geboes score; GHAS: Global histological disease activity score.