Artificial intelligence in the management of inflammatory bowel disease: What’s next?

Table 1 Performance of deep learning models in endoscopic assessment of mucosal healing

Ref.	Input	Model	Key performance metrics
Stidham et al[31]	Images	CNN	MES 0-1 vs MES 2-3: AUC = 0.97
Ozawa et al[32]	Images	CNN	MES 0 vs MES 1-3: AUC = 0.86; MES 0-1 vs MES 2-3: AUC = 0.98
Gottlieb et al[35]	Videos	RNN	EH by eMS: Accuracy = 95.52%; EH by UCEIS: Accuracy = 97.04%
Takenaka et al[34]	Images	DNUC	UCEIS ≤ 1: Accuracy = 90.1%
Higuchi et al[68]	Images1	CNN	MES 0-3: Accuracy = 91.3%-99.4%
Yao et al[33]	Videos	CNN	MES 0-1 vs MES 2-3: Accuracy = 83.7%
Iacucci et al[47]	Videos	CNN	PICaSSO ≤ 3 (VCE): AUC = 0.94; UCEIS ≤ 1: AUC = 0.85

¹Derived from capsule endoscopy.

Select studies showing the performance of common deep learning models in assessing endoscopic healing by Mayo endoscopic score, endoscopic Mayo score, ulcerative colitis endoscopic index of severity, and Paddington international virtual chromoendoscopy score. VCE: Virtual chromoendoscopy; CNN: Convolutional neural network; RNN: Recurrent neural network; DNUC: Deep neural network for the evaluation of ulcerative colitis; MES: Mayo endoscopic score; AUC: Area under the receiver operating characteristic curve; EH: Endoscopic healing; eMS: Endoscopic Mayo score; UCEIS: Ulcerative colitis endoscopic index of severity; PICaSSO: Paddington international virtual chromoendoscopy score.

Table 2 Performance of deep learning models in capsule endoscopy for the assessment of common pathologies in inflammatory bowel disease

Ref.	Model	Key performance metrics
Klang et al[58]	CNN	Ulcers vs normal mucosa: AUC = 0.94-0.99
Barash et al[61]	CNN	Ulcer grade 1 vs 3: AUC = 0.958; ulcer grade 1 vs 2: AUC = 0.565; ulcer grade 2 vs 3: AUC = 0.939
Ribeiro et al[67]	CNN	Ulcers and erosions: AUC = 1.00
Kratter et al[69]	CNN	Ulcer detection (cross-domain model): AUC = 0.921-0.948; ulcer detection (combined model): AUC = 0.984-0.998
Klang et al[59]	CNN	Strictures vs ulcers: AUC = 0.942; strictures vs mild ulcers: AUC = 0.992; strictures vs moderate ulcers: AUC = 0.975; strictures vs severe ulcers: AUC = 0.889
Majtner et al[65]	CNN	SB ulcers: Accuracy = 98.5%; LB ulcers: Accuracy = 98.1%
Ferreira et al[66]	CNN	SB and LB ulcers: AUC = 1.00

Select studies showing the performance of common deep learning models in capsule endoscopy in the evaluation of ulcers, erosions, and strictures. SB: Small bowel; LB: Large bowel; CNN: Convolutional neural networks; AUC: Area under the receiver operating characteristic curve.

Table 3 Performance of therapeutic prediction models in assessing response to common biologic therapies in inflammatory bowel disease

Ref.	Model	Drug name	Outcome	Key performance metrics
Telesco et al[110]	LR	Golimumab	Mucosal healing at week 6	AUC = 0.688
Chen et al[111]	ANN	Infliximab	PNR at weeks 6 and 14	RNA model: AUC = 0.850
Feng et al[112]	RF, ANN	Infliximab	PNR at week 14	AUC = 0.81
Ghiassian et al[118]	PNN	Infliximab	Inadequate response at weeks 4-6 and 8	AUC = 0.83
Mishra et al[120]	RF	Infliximab	Clinical remission at week 14	DNAm-DEGs: AUC = 0.88
Derakhshan Nazari et al[113]	LASSO, ensemble ML	Infliximab	Improvement in symptoms, endoscopic activity, histologic scores	Accuracy = 73%-95%
Ye et al[114]	LASSO, RF, SVM-RFE	Infliximab	Endoscopic or histological activity at weeks 6 and 14	AUC = 0.912-0.920
Zhang et al[121]	LASSO	Infliximab	PNR at week 14 vs NDR at weeks 22-52	AUC = 0.812-0.888
Konikoff et al[129]	CatBoost	Infliximab and vedolizumab	Drug sustainability at week 54	AUC = 0.86
Joustra et al[119]	Stability selected gradient boosting	Tofacitinib	Disease response after week 8	AUC = 0.74
Lees et al[126]	RF, LR	Tofacitinib	Partial Mayo score response at weeks 4 and 8	AUC = 0.74-0.85
Chaparro et al[124]	GAM	Ustekinumab	Clinical remission	AUC = 0.796
Doherty et al[122]	RF	Ustekinumab	Therapeutic response at week 6	AUC = 0.733-0.844
He et al[115]	LASSO	Ustekinumab	Disease response by CDAI	AUC = 0.734
Koustenis et al[116]	XGBoost, XRT, DRF GBM, GLM	Ustekinumab	Clinical remission at 6 months	AUC = 0.925-1.0
Morikubo et al[16]	SVM, RF, LR, KNN	Ustekinumab	Steroid-free clinical remission at week 22	Accuracy = 56.5%-69.6%
Chen et al[125]	ENRR, RF, SMOTE	Vedolizumab	Clinical remission at week 52	AUC = 0.811
Waljee et al[26]	RF	Vedolizumab	Corticosteroid-free biologic remission at week 52	AUC = 0.65-0.75
Waljee et al[123]	RF	Vedolizumab	Corticosteroid-free endoscopic remission at week 52	AUC = 0.62-0.73
Dulai et al[127]	LR (VDZ-CDST)	Vedolizumab	Clinical and corticosteroid-free remission, mucosal healing, deep remission at week 26	AUC = 0.66-0.75
Alric et al[128]	LR (VDZ-CDST)	Vedolizumab and ustekinumab	Steroid-free clinical remission at week 48	Vedolizumab: AUC = 0.69; ustekinumab: AUC = 0.52

Select studies showing the performance of machine learning models in predicting various forms of response to therapy such as primary non-response, non-durable response, clinical, endoscopic or histologic remission. LR: Logistic regression; ANN: Artificial neural network; RF: Random forest; PNN: Probabilistic neural network; LASSO: Least absolute shrinkage and selection operator; SVM: Support vector machine; RFE: Recursive feature elimination; GAM: Generalized additive model; XGBoost: Extreme gradient boosting; XRT: Extreme random trees; DRF: Distributed random forest; GBM: Gradient boosting machine; GLM: Generalized linear model; KNN: K-nearest neighbors; ENRR: Elastic-net-regularization regression; SMOTE: Synthetic minority over-sampling technique; VDZ-CDST: Vedolizumab clinical decision support tool; CDAI: Crohn’s disease activity index; DNAm: DNA methylation; DEGs: Differentially expressed genes; ML: Machine learning; AUC: Area under the receiver operating characteristic curve.