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

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.