Table 3.

Artificial intelligence in prediction of therapy response and clinical outcomes in inflammatory bowel disease

Ref.	AI classifier vs comparator	IBD type	Study design and sample size	Modality	Outcomes	Study results/validation cohort
Waljee et al[59], 2018	Random forest (RF). No comparator	CD/UC	Post-hoc analysis of prospective clinical trial, 594 CD patients	Veteran’s Health Administration Electronic Health Record (EHR)	Outpatient corticosteroids prescribed for IBD and inpatient hospitalizations associated with a diagnosis of IBD	AUC for the RF longitudinal model was 0.85 [95% confidence interval (CI): 0.84–0.85]. AUC for the RF longitudinal model using previous hospitalization or steroid use was 0.87 (95%CI: 0.87-0.88). Validation cohort included
Uttam et al[60], 2019	Support vector machines (SVM) vs nanoscale nuclear architecture mapping (NanoNAM)	CD/UC	Prospective cohort, 103 IBD patients	3-dimensional NanoNAM of normal-appearing rectal biopsies	Colonic neoplasia	NanoNAM detects colonic neoplasia with an AUC of 0.87 ± 0.04, sensitivity of 0.81 ± 0.09, and specificity of 0.82 ± 0.07 in the independent validation set. Validation cohort included
Waljee et al[61], 2017	RF. No comparator	CD/UC	Retrospective cohort, 1080 IBD patients	EHR, lab values	Remission and clinical outcomes with thiopurines	AUC for algorithm-predicted remission in the validation set was 0.79 vs 0.49 for 6-TGN. The mean number of clinical events per year in patients with sustained algorithm-predicted remission (APR) was 1.08 vs 3.95 in those that did not have sustained APR (P < 1 × 10-5). Validation cohort included
Popa et al[62], 2020	Neural network model. No comparator	UC	Prospective cohort, 55 UC patients	Clinical and biological parameters and the endoscopic Mayo score	Disease activity after one year of anti-TNF treatment	The classifier achieved an excellent performance predicting the disease activity at one year with an accuracy of 90% and AUC 0.92 on the test set and an accuracy of 100% and an AUC of 1 on the validation set. Validation cohort included
Douglas et al[45], 2018	RF. No comparator	Peds CD	Cross-sectional, 20 CD patients, 20 healthy controls	Shotgun metagenomics (MGS), 16S rRNA gene sequencing	Response to induction therapy	16S genera were again the top dataset (accuracy = 77.8%; P = 0.008) for predicting response to therapy. MGS strain (P = 0.029), genus (P = 0.013), and KEGG pathway (P = 0.018) datasets could also classify patients according to therapy response with accuracy = 72.2% for all three. Validation cohort included
Waljee et al[63], 2010	RF vs boosted trees, RuleFit	CD/UC	Cross-sectional, 774 IBD patients	EHR, lab values (thiopurine metabolites)	Response to thiopurine therapy	A RF algorithm using laboratory values and patient age differentiated clinical response from nonresponse in the model validation data set with an AUC of 0.856 (95%CI: 0.793-0.919). Validation cohort included
Menti et al[64], 2016	Naïve bayes vs Bayesian additive regression trees vs Bayesian networks	CD/UC	Retrospective cohort, 152 CD patients	Genomic DNA, genetic polymorphism	Presence of extra-intestinal manifestations in IBD patients	Bayesian networks achieved accuracy of 82% when considering only clinical factors and 89% when considering also genetic information, outperforming the other techniques. Validation cohort included
Waljee et al[65], 2017	RF vs baseline regression model	CD/UC	Retrospective cohort, 20368 IBD patients	EHR, lab values	Corticosteroid-free biologic remission with vedolizumab	The AUC for corticosteroid-free biologic remission at week 52 using baseline data was only 0.65 (95%CI: 0.53-0.77), but was 0.75 (95%CI: 0.64-0.86) with data through week 6 of vedolizumab. Validation cohort included
Morilla et al[66], 2019	Deep neural networks. No comparator	UC	Retrospective cohort, 47 UC patients	Colonic microrna profiles	Responses to therapy	A deep neural network-based classifier identified 9 microRNAs plus 5 clinical factors, routinely recorded at time of hospital admission, that were associated with responses of patients to treatment. This panel discriminated responders to steroids from non-responders with 93% accuracy (AUC, 0.91). Three algorithms, based on microRNA levels, identified responders to infliximab vs non-responders (84% accuracy, AUC 0.82) and responders to cyclosporine vs non-responders (80% accuracy, AUC 0.79). Validation cohort included
Wang et al[67], 2020	Back-propagation neural network (BPNN), SVM vs logistic regression	CD	Cross-sectional, 446 CD patients	EHR	Medication nonadherence to maintenance therapy	The average classification accuracy and AUC of the three models were 85.9% and 0.912 for BPNN, and 87.7% and 0.930 for SVM, respectively. Validation cohort included
Bottigliengo et al[68], 2019	Bayesian machine learning techniques (BMLTs) vs logistic regression	CD/UC	Retrospective cohort, 142 IBD patients	EHR, genetic polymorphisms	Presence of extra-intestinal manifestations in IBD patients	BMLTs had an AUC of 0.50 for classifying the presence of extra-intestinal manifestations. Validation cohort included
Ghoshal et al[69], 2020	Nonlinear artificial neural network (ANN) vs multivariate linear PCA	UC	Prospective cohort, 263 UC patients	EHR	Responses to therapy	The multilayer perceptron neural network was trained by back-propagation algorithm (10 networks retained out of 16 tested). The classification accuracy rate was 73% in correctly classifying response to medical treatment in UC patients. No validation cohort included
Sofo et al[70], 2020	SVM leave-one-out cross-validation. No comparator	UC	Retrospective cohort, 32 UC patients	EHR	Post-surgical complications after colectomy	Evaluating only preoperative features, machine learning algorithms were able to predict minor postoperative complications with a high strike rate (84.3%), high sensitivity (87.5%) and high specificity (83.3%) during the testing phase. Validation cohort included
Kang et al[71], 2017	ANN vs logistic regression	UC	Cross-sectional, 24 UC patients	Gene expression profiles	Response to anti-TNF	Balanced accuracy in cross validation test for predicting response to anti-TNF therapy in ulcerative colitis patient was 82%. Validation cohort included
Babic et al[72], 1997	CART vs back propagation neural network (BPNN)	CD/UC	Cross-sectional, 200 IBD patients	EHR	Quality of life	Best reached classification accuracy did not exceed 80% in any case. Other classifiers namely, K-nearest-neighbor, learning vector quantization and BPNN confirmed that outcome. Validation cohort included
Dong et al[73], 2019	RF, SVM, ANN vs logistic regression	CD	Retrospective cohort, 239 CD patients	EHR, laboratory tests	Crohn's related surgery	The results revealed that RF predictive model performed better than LR model in terms of accuracy (93.11% vs 91.15%), precision (53.42% vs 44.81%), F1 score (0.6016 vs 0.5763), TN rate (95.08% vs 92.00%), and the AUC (0.8926 vs 0.8809). The AUCs were excellent at 0.9864 in RF,0.9538 in LR, 0.8809 in DT, 0.9497 in SVM, and 0.9059 in ANN, respectively. Validation cohort included
Lerrigo et al[74], 2019	Latent Dirichlet allocation, unsupervised machine learning algorithm. No comparator	CD/UC	Retrospective cohort, 28623 IBD patients	Online posts from the Crohn’s and colitis foundation community forum	Impact of online community forums on well-being and their emotional content	10702 (20.8%) posts were identified expressing: gratitude (40%), anxiety/fear (20.8%), empathy (18.2%), anger/frustration (13.4%), hope (13.2%), happiness (10.0%), sadness/depression (5.8%), shame/guilt (2.5%), and/or loneliness (2.5%). A common subtheme was the importance of fostering social support. No validation cohort included

AI: Artificial intelligence; IBD: Inflammatory bowel disease; CD: Crohn’s disease; UC: Ulcerative colitis; AUC: Area under the curve; TNF: Tumor necrosis factor.