Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions

Table 1 Artificial intelligence in diagnosis and risk prediction of inflammatory bowel disease

Ref.	AI classifier vs comparator	IBD type	Study design and sample size	Modality	Outcome	Study results/validation cohort
Mossotto et al[18], 2017	Support vector machines (SVM) vs linear discriminant	Peds CD/UC	Prospective cohort, 287 IBD patients	Endoscopic and histologic inflammation	Diagnosis of IBD	Diagnostic accuracy of 82.7% with an AUC of 0.87 in diagnosing Crohn's disease or ulcerative colitis. Validation cohort included
Wei et al[19], 2013	SVM with gradient boosted trees (GBT) vs simple log odds method	CD/UC	Cross-sectional, 30000 IBD patients, 22000 healthy controls	Genetics, ImmunoChip	Risk of IBD	The SVM demonstrated very comparable performance (AUC 0.862 and 0.826 for CD and UC, respectively), whereas GBT showed inferior performance (AUC 0.802 and0.782 for CD and UC, respectively. Validation cohort included
Romagnoni et al[20], 2019	Artificial neural networks (ANNs) vs penalized logistic regression (LR), and GBT	CD	Cross-sectional, 18227 CD patients, 34050 healthy controls	Genetics, ImmunoChip	Risk of IBD	Using single nucleotide polymorphisms (SNPs), final predictive model achieved AUC of 0.80. Validation cohort included
Isakov et al[21], 2017	Random forest (RF), SVM with svmPoly), extreme gradient boosting vs elastic net regularized generalized linear model (glmnet)	CD/UC	Cross-sectional, 180 CD patients, 149 UC patients, 90 healthy controls	Expression data (microarray and RNA-seq)	Risk of IBD	The method was used to classify a list of 16390 genes. Each gene received a score that was used to prioritize it according to its predicted association to IBD. The combined model demonstrated AUC, sensitivity, specificity, and accuracy values of 0.829, 0.577, 0.88, and 0.808, respectively. Validation cohort included
Yuan et al[22], 2017	Sequential minimal optimization vs DisGeNET (Version 4.0)	CD/UC	Cross-sectional, 59 CD patients, 26 UC patients, 42 healthy controls	Gene Expression datasets	Risk of IBD	By analyzing the gene expression profiles using minimum redundancy maximum relevance and incremental feature selection, 21 genes were obtained that could effectively distinguish samples from IBD and the non-IBD samples. Highest total prediction accuracy was 97.64% using the 1170th feature set. Validation cohort included
Hübenthal et al[23], 2015	SVM vs RF	CD/UC	Cross-sectional, 40 CD patients, 36 UC patients, 38 healthy controls	MicroRNAs	Diagnosis of IBD	Measured by the AUC the corresponding median holdout-validated accuracy was estimated as ranging from 0.75 to 1.00 and 0.89 to 0.98, respectively. In combination, the corresponding models provide tools for the distinction of CD and UC as well as CD, UC and healthy control with expected classification error rates of 3.1 and 3.3%, respectively. Validation cohort included
Tong et al[24], 2020	RF vs convolutional neural network (CNN)	CD/UC	Retrospective Cohort, 875 CD patients, 5128 UC patients	Colonoscopy Endoscopic Images	Diagnosis of IBD	RF sensitivities/specificities of UC/CD were 0.89/0.84, 0.83/0.82, and 0.72/0.77, respectively, while the values for the CNN of CD was 0.90/0.77. The precisions/recalls of UC-CD when employing RF were 0.97/0.97, 0.65/0.53, respectively, and when employing the CNN were 0.99/0.97 and 0.87/0.83, respectively. Validation cohort included
Smolander et al[25], 2019	Deep belief networks (DBNs) vs SVM	CD/UC	Cross-sectional, 59 CD patients, 26 UC patients, 42 healthy controls	Gene Expression datasets	Diagnosis of IBD	Using DBN only, accuracy for diagnosis of UC was 97.06% and CD was 97.07%. Using both DBN and SVM, accuracy for diagnosis of UC was 97.06% and CD was 97.03%. Validation cohort included
Abbas et al[26], 2019	RF vs network-based biomarker discovery	Peds CD/UC	Cross-sectional, 657 IBD patients, 316 healthy controls	Large dataset of new-onset pediatric IBD metagenomics biopsy samples	Diagnosis of IBD	For the diagnosis of IBD, highest AUC attained by top Random Forest classifiers was 0.77. No validation cohort included
Khorasani et al[27], 2020	SVM vs recently developed feature selection algorithm (robustness-performance tradeoff, RPT)	UC	Cross-sectional, 146 UC patients, 60 healthy controls	Gene Expression dataset	Diagnosis of IBD	Our model perfectly detected all active cases and had an average precision of 0.62 in the inactive cases. Validation cohort included
Rubin et al[28], 2019	CITRUS supervised machine learning algorithm. No comparator	CD/UC	Cross-sectional, 68 IBD patients	Peripheral blood mononuclear cells and intestinal biopsies mass cytometry	Diagnosis of IBD	An 8-parameter immune signature distinguished Crohn's disease from ulcerative colitis with an AUC = 0.845 (95%CI: 0.742-0.948). No validation cohort included
Pal et al[29], 2017	Naïve Bayes and with a consensus machine learning method vs Critical Assessment of Genome Interpretation (CAGI) 4 method	CD	Cross-sectional, 64 CD patients, 47 healthy controls	Genotypes from Exome Sequencing Data	Risk of IBD	The AUC for predicting risk of Crohn's disease using the SNP model was 0.72. No validation cohort included
Aoki et al[30], 2019	Deep CNN. No comparator	CD	Retrospective Cohort, 115 IBD patients	Wireless capsule endoscopy images	Diagnosis of IBD	The AUC for the detection of erosions and ulcerations was 0.958 (95%CI: 0.947-0.968). The sensitivity, specificity, and accuracy of the CNN were 88.2% (95%CI: 84.8-91.0), 90.9% (95%CI: 90.3-91.4), and 90.8% (95%CI: 90.2-91.3), respectively. Validation cohort included
Bielecki et al[31], 2012	SVM vs human reader (pathologist)	CD/UC	Cross-sectional, 14 CD patients, 13 UC patients, 11 healthy controls	Raman spectroscopic imaging of epithelium cells	Diagnosis of IBD	Raman maps of human colon tissue sections were analyzed by utilizing innovative chemometric approaches. Using SVM, it was possible to separate between healthy control patients, patients with Crohn's Disease, and patients with ulcerative colitis with an accuracy of 98.90%. No validation cohort included
Cui et al[32], 2013	Recursive SVM vs unsupervised learning strategy	CD/UC	Cross-sectional, 124 IBD patients, 99 healthy controls	16S rRNA gene analysis	Diagnosis of IBD	Selection level of 200 features results in the best leave-one-out cross-validation result (accuracy = 88%, sensitivity = 92%, specificity = 84%). Validation cohort included
Duttagupta et al[33], 2012	SVM. No comparator	UC	Cross-sectional, 20 UC patients, 20 healthy controls	MicroRNAs	Diagnosis of IBD	SVM classifier measurements revealed a predictive score of 92.8% accuracy, 96.2% specificity and 89.5% sensitivity in distinguishing ulcerative colitis patients from normal individuals. Validation cohort included
Daneshjou et al[34], 2017	Naïve bayes, neural networks, random forests vs CAGI methods	CD	Cross-sectional, 64 ICD patients, 47 healthy controls	Exome Sequencing	Diagnosis of IBD	In CAGI4, 111 exomes were derived from a mix of 64 Crohn’s disease patients. Top performing methods had an AUC of 0.87. Validation cohort included
Geurts et al[35], 2005	RF vs SVM	CD/UC	Prospective cohort, 30 CD patients, 30 CD patients	Proteomic Mass Spectrometry	Diagnosis of IBD	Random forest model to diagnosis IBD had a sensitivity of 81.67%, specificity of 81.17%. Support vector machine model to diagnosis IBD had a sensitivity of 87.92%, specificity of 87.87%. Validation cohort included
Li et al[36], 2020	RF vs ANN	UC	Cross-sectional, 193 UC patients, 21 healthy controls	Gene Expression Profiles	Diagnosis of IBD	The random forest algorithm was introduced to determine 1 downregulated and 29 upregulated differentially expressed genes contributing highest to ulcerative colitis occurrence. ANN was developed to calculate differentially expressed genes weights to ulcerative colitis. Prediction results agreed with that of an independent data set (AUC = 0.9506/PR-AUC = 0.9747). Validation cohort included
Wingfield et al[37], 2019	RF vs SVM	CD	Cross-sectional, 668 CD patients	Metagenomic Data	Diagnosis of IBD	Highest RPT measure for Crohn’s disease was random forest 0.60 and SVM 0.58. For ulcerative colitis, RPT was random forest 0.70 and SVM 0.48. Validation cohort included
Han et al[38], 2018	RF vs LR, CORG	CD/UC	Cross-sectional, 24 CD patients, 59 UC patients, 76 healthy controls	Gene Expression Profiles	Diagnosis of IBD	The gene-based feature sets had median AUC on the validation sets ranging from 0.6 to 0.76). Validation cohort included
Wang et al[39], 2019	AVADx (Analysis of Variation for Association with Disease) vs two GWAS-based CD evaluation methods	CD	Cross-sectional, 64 CD patients, 47 healthy controls	Whole Exome or Genome Sequencing Data	Diagnosis of IBD	AVADx highlighted known CD genes including NOD2and new potential CD genes. AVADx identified 16% (at strict cutoff) of CD patients at 99% precision and 58% of the patients (at default cutoff) with 82% precision in over 3000 individuals from separately sequenced panels. Validation cohort included

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