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

Table 1 Role of artificial intelligence-based endoscopy in the evaluation of patients with inflammatory bowel diseases

Ref.	Disease/number of patients	Type of study	Endoscopic technique	Number of training samples	Number of test samples	AI/model	Main findings
Stidham et al[22]	UC/3082 patients	Retrospective, single center	WLE	14862 images	1652 images	DL-CNN	Discriminating ER (MES ≤ 1) from moderate-severe disease (MES ≥ 2) (AUC = 0.966, sensitivity = 83.0%, specificity = 96.0%). AI and pathologist agreement (κ = 0.840 vs κ = 0.860)
Maeda et al[38]	UC/187 patients	Retrospective, single center	Endocytoscopy	12900 still images	525 segments	CAD	Prediction of HR (GS ≥ 3.1) (sensitivity = 74.0%, specificity = 97.0%, precision = 91.0%, κ = 1.000)
Ozawa et al[40]	UC/955 patients	Retrospective, single center	WLE	26304 still images	3981 still images	CAD-CNN	AI performance for mucosal healing (MES ≤ 1, AUC = 0.980)
Takenaka et al[25]	UC/875 patients	Prospective, single center	WLE	40789 still images	4187 still images	DNUC	Evaluation of ER (UCEIS ≤ 2) (accuracy = 90.1%, ICC = 0.917). Evaluation of HR (GS < 3.1) (accuracy = 92.9%, κ = 0.859)
Bossuyt et al[41]	UC/35 patients	Prospective, multicenter	Prototype endoscope	NR	NR	CAD	RD for endoscopic/histological inflammation: Correlation with MES (r = 0.76), UCEIS (r = 0.74), RHI (r = 0.74). RD score (≤ 60) predicts HR (AUC = 0.950, sensitivity = 96.0%, specificity = 80.0%)
Yao et al[27]	UC/157 patients	Prospective, multicenter	WLE	NR	264 videos of high resolution	DL-CNN	The still image informative classifier had excellent performance (sensitivity = 0.902, specificity = 0.870). Correct prediction of MES: 78% of videos (κ = 0.840)
Gottlieb et al[30]	UC/249 patients	Prospective, multicenter	WLE	629 videos	157 videos	RNN	Endoscopic healing evaluation according to UCEIS (accuracy = 97.0%) and MES (accuracy = 95.5%). Agreement of the model with human experts for MES (QWK = 0.844) and UCEIS (QWK = 0.855)
Bossuyt et al[42]	UC/58 patients	Prospective, single center	SWE	NR	113 still images	CAD	AI algorithm yielded better HR accuracy (86.0%) than MES (74.0%) or UCEIS (79.0%)
Huang et al[43]	UC/54 patients	Retrospective, single center	Endoscopy HD	600 still images	256 still images	DNN, SVM, k-NN	Performance of the combined model for differentiation between MES ≤ 1 and MES 2 (AUC = 0.927, accuracy = 94.5%, sensitivity = 89.2%, specificity = 96.3%)
Takenaka et al[44]	UC/770 patients	Prospective, multicenter	WLE	NR	NR	DNUC	Prediction of HR (sensitivity = 97.9%, specificity = 94.6%). Agreement between the DNUC and experts for endoscopic assessment (ICC = 0.927)
Patel et al[45]	UC/73 patients	Prospective, single center	Endoscopy HD	55 video images	18 video images	MLA	Differentiation between: Remission (UCEIS: 0-1) and active inflammation (UCEIS ≥ 2) (accuracy = 90.0%, κ = 0.900); Mild (UCEIS: 2-3); And moderate-to-severe inflammation (UCEIS ≥ 4) (accuracy = 98.0%, κ = 0.960)
Kim et al[19]	UC/492 patients	Retrospective, single center	WLE	904 still images	80 still images	DL-CNN	Difference between MES 0 vs MES 1: Internal test. IBD experts (F1 score = 0.92, AUC = 0.970); External test. Hyper Kvasir dataset (F1 score = 0.89, AUC = 0.860)
Polat et al[20]	UC/564 patients	Retrospective, single center	WLE	11276 still images	1658 still images	DL-CNN	Excellent concordance between the five CNN networks and endoscopists for: MES evaluation (QWK: 0.847-0.854); And classification of remission cases (QWK: 0.834-0.852)
Wang et al[21]	UC/308 patients	Retrospective, single center	WLE	37515 still images	3191 still images	CNN	Diagnosis of ER (MES ≤ 1) (AUC = 0.980, accuracy = 95.1%, sensitivity = 92.9%, specificity = 95.4%, κ = 0.884)
Iacucci et al[46]	UC/283 patients	Prospective, multicenter	WLE, VCE	239 video images; 245 video images	242 video images; 244 video images	CNN	Detection of ER using VCE (PICaSSO ≤ 3) (AUC = 0.940, sensitivity = 79.0%, specificity = 95.0%, κ = 0.730) achieved better performance than WLE (UCEIS ≤ 1) (AUC = 0.850, sensitivity = 72.0%, specificity = 87.0%, κ = 0.510)
Byrne et al[47]	UC/NR	Prospective, single center	HD endoscopy	134 video images	NR	DL-CNN	Performance for disease severity discrimination: MES ≤ 1 vs MES ≥ 2 (AUC = 0.941, accuracy = 94.0%, sensitivity = 96.7%, specificity = 91.3%, QWK = 0.880); UCEIS ≤ 3 vs UCEIS > 3 (AUC = 0.936, accuracy = 94.0%, sensitivity = 93.9%, specificity = 93.4%, QWK = 0.870)
Stidham et al[31]	UC/748 patients	Prospective, multicenter	WLE	NR	NR	ML	CDS had better performance for detecting endoscopic changes than MES (Hedges’ g: 0.743 vs 0.460, P < 0.001)
Takabayashi et al[48]	UC/812 patients	Retrospective, multicenter	WLE	14208 still images	13826 still images	CNN	Disease severity grading-correlation between: UCEGS and MES (ρ = 0.890, P < 0.001); UCEGS and IBD experts (ρ: 0.960-0.987, P < 0.001)
Ogata et al[49]	UC/110 patients	Prospective, single center	WLE	74713 still images	11452 still images	CNN	Performance for evaluation ER based MES (sensitivity = 96.9%, specificity = 78.4%, accuracy = 93.4%). Interobserver/intraobservator agreement with AI/without AI (ICC: 0.84-0.86/0.89 vs 0.64-0.76/0.76)
Sinonquel et al[50]	UC/36 patients	Prospective, single center	SWE	NR	NR	CAD	Histological assessment using SWE-CAD (sensitivity = 96.1%, specificity = 85.5%, accuracy = 96.4%). The accuracy of classification into mild, moderate, and severe disease was 97.7%, 62.8% and 95.0%, respectively
Aoki et al[51]	CD/131 patients	Retrospective, single center	CE	5360 images	10440 images	CNN	Ulcer recognition in small bowel video frames (AUC = 0.958, sensitivity = 88.2%, specificity = 90.9%, accuracy = 90.8%)
Klang et al[52]	CD/49 patients	Retrospective, single center	CE	14112 images	3528 images	DL-CNN	Increased performance in ulcer detection (AUC = 0.990, accuracy: 95.4%-96.7%)
Klang et al[32]	CD/145 patients	Retrospective, single center	CE	27892 images	1449 images	DNN	Performance for: Stricture detection (AUC = 0.971, accuracy = 93.5%); Differential diagnosis between strictures and normal mucosa (AUC = 0.989); Discrimination between strictures and ulcers (AUC = 0.942)
Barash et al[53]	CD/49 patients	Retrospective, single center	CE	1242 images	248 images	CNN	Ability of ulcerative lesion classification: Grade 1 vs 3 (AUC = 0.958, accuracy = 91.0%, κ = 0.910); Grade 2 vs 3 (AUC = 0.939, accuracy = 79.0%, κ = 0.790); Grade 1 vs 2 (AUC = 0.565, accuracy = 62.4%, κ = 0.670)
Majtner et al[54]	CD/38 patients	Retrospective, single center	CE	5421 images	1549 images	DL	Performance in ulcer detection (sensitivity = 95.7%, specificity = 99.8%, accuracy = 98.4%). Agreement between the model and manual reading of ulcerations (κ = 0.720)
Udristoiu et al[55]	CD/54 patients	Retrospective, single center	pCLE	5081 images	1124 images	CNN	Differentiation between inflammation and intact colonic mucosa (AUC = 0.980, accuracy = 95.3%, specificity = 92.8%, sensitivity = 94.6%)
de Maissin et al[56]	CD/63 patients	Retrospective, multicenter	CE	2449 images	700 images	RNN	Performance for discriminating pathological vs non-pathological images (accuracy = 93.7%, sensitivity = 93.0%, specificity = 95.0%, κ = 0.790)
Ribeiro et al[57]	CD/124 patients	Retrospective, multicenter	CE	37319 images	124 images	CNN	Identification of colonic ulcerations and erosions (AUC = 1.000, accuracy = 99.6%, sensitivity = 96.9%, specificity = 99.9%)
Ferreira et al[58]	CD/NR	Retrospective, multicenter	CE	19740 images	4935 images	DL-CNN	Performance of the model for lesion detection (sensitivity = 90.0%, specificity = 96.0%, precision = 97.1%, accuracy = 92.4%)
Afonso et al[59]	CD/NR	Retrospective, single center	CE	4904 images	1226 images	CNN	Detection of ulcers and erosions in the small intestine mucosa (accuracy = 95.6%, sensitivity = 90.8%, specificity = 97.1%)
Martins et al[60]	CD/250 patients	Retrospective, single center	DAE	250 DAE images	6772 images	CNN	Identification of colonic ulcerations and erosions (AUC = 1.000, accuracy = 98.7%, sensitivity = 88.5%, specificity = 99.7%)
Brodersen et al[34]	CD/131 patients	Prospective, multicenter	CE	NR	NR	DL	The identification capacity for CD (sensitivity: 92.0%-96.0% and specificity: 90.0%-93.0%) and IBD (sensitivity: 97.0% and specificity: 90.0%-91.0%)
Xie et al[61]	CD/628 patients	Retrospective, single center	DBE	NR	28155 images	DL	The accuracy for detection of ulcers (96.3%), inflammatory stenosis (95.7%), and non-inflammatory stenosis (96.7%). The grading of ulcers based on surface area, size, and depth (precision between 85.2% and 87.8%)

AI: Artificial intelligence; CAD: Computer-aided diagnosis; CD: Crohn’s disease; CE: Capsule endoscopy; CNN: Convolutional neural network; DAE: Device-assisted enteroscopy; DBE: Double-balloon endoscopy; DL: Deep learning; DNUC: Deep neural network for evaluation of ulcerative colitis; HD: High definition; NR: Not reported; pCLE: Probe-based confocal laser endomicroscopy; SWE: Single-wavelength endoscopy; WLE: White-light endoscopy; VCE: Virtual chromoendoscopy; UC: Ulcerative colitis; k-NN: K-nearest neighbor network; RNN: Recurrent neural network; SVM: Support vector machine; ML: Machine learning; MLA: Machine learning algorithm; ER: Endoscopic remission; MES: Mayo endoscopic score; AUC: Area under the curve; GS: Geboes score; HR: Histological remission; UCEIS: Ulcerative colitis endoscopic index of severity; ICC: Intraclass correlation coefficient; RD: Red density; RHI: Robarts histological index; QWK: Quadratic weighted kappa; PICaSSO: Paddington international virtual chromoendoscopy score; IBD: Inflammatory bowel disease; UCEGS: Ulcerative colitis endoscopic gradation scale; CDS: Cumulative disease score; DNN: Deep neural network.

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.

Table 3 Artificial intelligence-assisted prediction of therapeutic response in inflammatory bowel diseases

Ref.	Disease/number of patients	Study design	Therapy	AI/model	Main findings
Waljee et al[91]	UC/491 patients	Retrospective, multicenter	VDZ	ML-RF	Long-term steroid-free ER prediction (AUC = 0.730) using laboratory data from first 6 weeks of VDZ
Waljee et al[88]	CD/401 patients	Retrospective, multicenter	UST	ML-RF	Week 8 CRP/ALB ratio predicts UST non-response (AUC = 0.780, sensitivity = 79.0%, specificity = 67.0%) vs baseline data (AUC = 0.590, sensitivity = 63.0%, specificity = 64.0%)
Con et al[81]	CD/146 patients	Retrospective, single center	IFX, ADA	DL-RNN	AI model using CRP < 5 mg/L better predicts post-therapy remission than conventional model (AUC: 0.754 vs 0.659, P = 0.036)
Li et al[92]	CD/174 patients	Retrospective, single center	IFX	ML-RF	Response to IFX predicted by clinical/serological data (AUC = 0.900, accuracy = 85.0%, sensitivity = 81.0%, specificity = 94.0%)
He et al[93]	CD/86 patients	Retrospective, single center	UST	ML	UST response prediction based on HSD3B1, MUC4, CF1, and CCL11 expression (AUC: 0.734-0.746)
Park et al[94]	CD/234 patients	Prospective, multicenter	anti-TNF	ML	The likelihood of a non-durable response associated with hyperexpression of DPY19 L3 (β = 2.703) and GSTT1 (β = 1.735), and decreasing NUCB1 concentration (β = -2.142)
Kellerman et al[33]	CD/101 patients	Retrospective, single center	ADA, IFX, VDZ	DL- TimeSformer	Prediction of biologic initiation in newly diagnosed patients (AUC = 0.860, accuracy: 81.0%-82.0%), outperforming human reader (AUC = 0.700) and FC (AUC = 0.740)
Iacucci et al[36]	IBD/29 patients	Prospective, single center	anti-TNF, anti-α4β7	CAD	pCLE-detected crypt/vessel abnormalities and fluorescein leakage predict therapy response in UC (AUC = 0.930, accuracy = 85.0%) and CD (AUC = 0.790, accuracy = 80.0%); better anti-TNF prediction in UC (AUC = 0.830) than in CD (AUC = 0.580)
Stidham et al[31]	UC/748 patients with induction; 348 patients with maintenance	Prospective, single center	UST	CAD	CDSs were significantly lower in UST vs placebo both at week 8 (141.9 vs 184.3, P < 0.0001) and week 44 (78.2 vs 151.5, P < 0.0001). Stratification by baseline CDS showed increased UST efficacy in patients with severe disease compared with mild disease (-85.0 vs -55.4, P < 0.0001)
Harun et al[87]	UC/1684 patients with induction; 463 patients with maintenance	Prospective, multicenter	Etrolizumab	ML-SHAP	Remission prediction post-induction (AUC = 0.740) and maintenance (AUC = 0.750) using combined demographic, clinical, physiologic, and histological data
Qiu et al[89]	CD/746 patients	Retrospective, single center	IFX	ML-SHAP	Response discrimination using integrated predictors (HB, WBC, ESR, ALB, PLT, age at diagnosis, Montreal classification) (training set AUC = 0.910, si-test set AUC = 0.710)

AI: Artificial intelligence; CD: Crohn’s disease; IBD: Inflammatory bowel disease; UC: Ulcerative colitis; VDZ: Vedolizumab; UST: Ustekinumab; ADA: Adalimumab; IFX: Infliximab; TNF: Tumor necrosis factor; ML: Machine learning; RF: Random forests; RNN: Recurrent neural network; SHAP: Shapley additive explanations; CAD: Computer-aided diagnosis; DL: Deep learning; ER: Endoscopic remission; AUC: Area under the curve; ALB: Albumin; CRP: C-reactive protein; FC: Fecal calprotectin; pCLE: Probe-based confocal laser endomicroscopy; CDS: Cumulative disease score; HB: Hemoglobin; WBC: White blood cells; ESR: Erythrocyte sedimentation rate; PLT: Platelets.

Table 4 Overview of machine learning models for diagnosis, prognosis, and treatment optimization in inflammatory bowel disease

Ref.	AI model	Field of application	Disease	Outcomes	Performance
Najdawi et al[68]	ML-RF	Histological assessment	UC	Evaluation of HR	Strong agreement with pathologists in relation to the NHI score (κ = 0.910, Spearman coefficient of ρ = 0.890) (P < 0.001)
Waljee et al[91]		Therapy	UC	Predicting corticosteroid-free ER with VDZ at week 52	AUC = 0.730, sensitivity = 72.0%, specificity = 68% according to the results at week 6
Waljee et al[88]		Therapy	CD	Anticipation of UST response at week 42	AUC = 0.780, sensitivity = 79.0%, specificity = 67.0% based on demographic and laboratory data up to week 8
Li et al[92]				Assessment therapeutic response to IFX	AUC = 0.900, accuracy = 85.0%, sensitivity = 81.0%, specificity = 94.0%
He et al[93]				Prediction of therapeutic response to UST based on expression profile of four genes	AUC: 0.734–0.746
Stidham et al[103]		Risk stratification	CD	Evaluation of surgical outcomes	AUC = 0.780
Maeda et al[90]	ML-SVM	Endoscopic assessment	UC	Evaluation of persistent inflammation	Sensitivity = 74.0%, specificity = 97.0%, precision = 91.0%
		Risk stratification	UC	Assessment of relapse risk	Increased rate in patients with active form (28.4%) compared with those in clinical remission (4.9%, P < 0.001)
Park et al[94]	ML-XGBoost	Therapy	UC	Remission prediction post-induction and maintenance for etrolizumab	AUC: 0.740-0.750
Harun et al[87]		Therapy	CD	Prediction of therapeutic response to anti-TNF	Non-response associated with hyperexpression of DPY19 L3 (β = 2.703) and GSTT1 (β = 1.735), and decreased NUCB1 concentration (β = -2.142)
Qiu et al[89]		Therapy	CD	Prediction of therapeutic response to IFX	AUC = 0.91
Takenaka et al[44]	DL-DNN	Endoscopic assessment	UC	Prediction of HR	Sensitivity = 97.9%, specificity = 94.6%, ICC = 0.927
Huang et al[43]		Endoscopic assessment	UC	Evaluation of mucosal healing	AUC = 0.927, accuracy = 93.8%, sensitivity = 84.6%, specificity = 96.9%
Klang et al[32]		Endoscopic assessment	CD	Identification of strictures	AUC = 0.989, precision = 93.5%
				Grading the severity of ulcerations	AUC = 0.992 (mild cases); AUC = 0.975 (moderate cases); AUC = 0.889 (severe cases)
Ozawa et al[40]	DL-CNN	Endoscopic assessment	UC	Discrimination between MES ≤ 1 and MES 2; diagnosis of ER (MES ≤ 1)	AUC = 0.980
Wang et al[21]		Endoscopic assessment	UC	Discrimination between MES ≤ 1 and MES 2; diagnosis of ER (MES ≤ 1)	AUC = 0.980, accuracy = 95.1%, sensitivity = 92.9%, specificity = 95.4%, κ = 0.884
Stidham et al[22]		Endoscopic assessment	UC	Discriminating ER from active endoscopic disease	AUC = 0.966, sensitivity = 83.0%, specificity = 96.0%. Excellent agreement between expert reviewers (κ = 0.860)
Gottlieb et al[30]		Endoscopic assessment	UC	Evaluation of mucosal healing	Accuracy: 95.5%-97.0%. Agreement with expert readers for MES (κ = 0.844) and UCEIS (0.855)
Takenaka et al[25]		Endoscopic assessment	UC	Predict of ER and HR	Accuracy = 90.1%, κ = 0.917 (UCEIS ≤ 2). Accuracy = 92.9%, κ = 0.859 (GS < 3.1)
Yao et al[27]	DL-CNN (Inception-V3)	Endoscopic assessment	UC	Assessment of disease severity	AUC = 0.939, sensitivity = 90.2%, specificity = 87.0%
Gui et al[69]	DL-CNN	Histological assessment	UC	Prediction of HR (PHRI < 1) according to the presence or absence of neutrophils	Sensitivity = 78.0%, specificity = 91.7%, accuracy = 86.0%, ICC = 0.84
Iacucci et al[70]		Histological assessment	UC	Prediction of HR (PHRI < 1) according to the presence or absence of neutrophils	AUC = 0.870, accuracy = 87.0%, sensitivity = 89.0%, specificity = 85.0%
Vande Casteele et al[66]		Histological assessment	UC	Quantification of eosinophils in colonic biopsies	The model had sensitivity = 0.86, specificity = 0.91, accuracy = 0.89
Udristoiu et al[55]	DL-CNN	Endoscopic assessment	CD	Differentiation between inflammation and intact colonic mucosa	AUC = 0.980, accuracy = 95.3%, specificity = 92.8%, sensitivity = 94.6%
Majtner et al[54]	DL-CNN (ResNet-50)	Endoscopic assessment	CD	Ulcer detection	The diagnostic accuracy was 98.5% for the small bowel and 98.1% for the colon
Kellerman et al[33]	DL-TimeSformer	Endoscopic assessment	CD	Prediction of biologic initiation in newly diagnosed patients	AUC = 0.860, accuracy: 81.0%-82.0%
Rymarczyk et al[79]	DL-CNN (SA-AbMILP)	Histological assessment	CD	Automatic histological assessment for GHAS and GS	Accuracy between 65.0%-89.0%
Furlanello et al[74]	DL-CNN (StarDist)	Histological assessment	IBD	Discriminates IBD from non-IBD mucosa	Accuracy = 90.0%
Kiyokawa et al[76]	DL-CNN (EfficientNet-b5)	Risk stratification	CD	Prediction of postoperative recurrence	AUC = 0.995, accuracy = 96.9%, precision = 96.4%
Con et al[81]	DL-RNN	Therapy	CD	Predicts post-therapy remission to anti-TNF	AUC = 0.754

AI: Artificial intelligence; ML: Machine learning; RF: Random forests; DL: Deep learning; ER: Endoscopic remission; AUC: Area under the curve; SVM: Support vector machine; XGBoost: Extreme gradient boosting; DNN: Deep neural network; CNN: Conventional neural network; SA-AbMILP: Self-attention-based multi-instance learning pooling; RNN: Recurrent neural network; CD: Crohn’s disease; IBD: Inflammatory bowel disease; UC: Ulcerative colitis; HR: Histological remission; VDZ: Vedolizumab; UST: Ustekinumab; IFX: Infliximab; TNF: Tumor necrosis factor; MES: Mayo endoscopic score; PHRI: Paddington international virtual chromoendoscopy score histologic remission index; GHAS: Global histological disease activity score; NHI: Nancy histological index; ICC: Intraclass correlation coefficient; UCEIS: Ulcerative colitis endoscopic index of severity; GS: Geboes score.