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

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.