Copyright
©The Author(s) 2021.
World J Gastroenterol. May 7, 2021; 27(17): 1920-1935
Published online May 7, 2021. doi: 10.3748/wjg.v27.i17.1920
Published online May 7, 2021. doi: 10.3748/wjg.v27.i17.1920
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 |
- Citation: Gubatan J, Levitte S, Patel A, Balabanis T, Wei MT, Sinha SR. Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions. World J Gastroenterol 2021; 27(17): 1920-1935
- URL: https://www.wjgnet.com/1007-9327/full/v27/i17/1920.htm
- DOI: https://dx.doi.org/10.3748/wjg.v27.i17.1920