Implications of artificial intelligence in inflammatory bowel disease: Diagnosis, prognosis and treatment follow up

Table 1 Artificial intelligence implications in the interpretation of radiography in inflammatory bowel disease patients

Ref.	Purpose	AI/DL model	Design	Result
Stidham et al[11], 2020	To identify structural bowel damage in IBD patients using AI-guided CT image analysis	semi-automated	Retrospective	Structural bowel damage measurements collected by semi-automated approaches are comparable to those of experienced radiologists
Mahapatra et al[13], 2016	To evaluate and compare semi-automated to fully automated models in identifying affected bowel segments in MRI of IBD patients	Semi-automated	Retrospective	Semi-automated model outperformed the fully automated model in the ability to segment the affected bowel regions in CD patients using MRI data with less required training time, training samples and expert effort

IBD: Inflammatory bowel disease; AI: Artificial intelligence; DL: Deep learning; CD: Crohn’s Disease; CT: Computed tomography; MRI: Magnetic resonance imaging.

Table 2 Artificial intelligence implications in the interpretation of endoscopic and capsule images of inflammatory bowel disease patients

Ref.	Purpose	AI/DL model	Design	Result
Peng et al[15], 2015	To predict the seasonal variation effect on the onset, relapse and severity of IBD patients	ANN	Retrospective	Great accuracy in predicting the frequency of relapse (Mean square error = 0.009, Mean absolute percentage error = 17.1%)
Maeda et al[16], 2019	To predict the persistence of histologic inflammation in ulcerative colitis patients using endoscopy images	SVM	Retrospective	Sensitivity, specificity, and accuracy of 74%, 97%, and 91%, respectively
Gottlieb et al[20], 2020	Determine the severity of UC from full-length endoscopy videos	CNN	Prospective	Inter-rater agreement factor (QWK) of 0.844 for eMS and 0.855 for UCEIS
Takenaka et al[21], 2020	To identify histological remission using colonoscopy images	Deep Neural Network	Prospective	Histologic remission identified with 92.9% accuracy
Stidham et al[22], 2019	To identify remission from disease group using colonoscopy images	CNN	Retrospective	Successfully identified the remission from the moderate-to-severe disease group with an AUROC of 0.966, a sensitivity of 83.0%, a specificity of 96.0%, PPV of 0.87, and a NPV of 0.94

AI: Artificial intelligence; DL: Deep learning; ANN: Artificial neural networks; SVM: Support vector machines; AUROC: Area under the receiver operating characteristic curves; NPV: Negative predictive value; PPV: Positive predictive value; CNN: Convolutional neural network.

Table 3 Artificial intelligence implications in the interpretation genomic of inflammatory bowel disease patients

Ref.	Purpose	AI/DL model	Design	Result
Khorasani et al[24], 2020	To differentiate UC patients from healthy subjects using colon samples	SVM-DRPT	Retrospective	Predicted all active cases of UC with an average precision of 0.62 in the inactive cases
Wei et al[27], 2013	To predict the risk of IBD using genomic data of risk loci	Advanced ML techniques	Retrospective	Successfully predicted IBD with an unprecedented predictive power with AUCs of 0.86 for CD and 0.83 for UC

IBD: Inflammatory bowel disease; AI: Artificial intelligence; DL: Deep learning; CD: Crohn’s Disease; UC: Ulcerative colitis; ML: Machine learning; SVM-DRPT: Support vector machines-developed feature selection algorithm.

Table 4 Artificial intelligence implications in the treatment and prognosis of inflammatory bowel disease patients

Ref.	Purpose	AI/DL model	Design	Result
Waljee et al[28], 2018	To predict corticosteroid-free biologic remission	Random Forest modeling	Retrospective	At week 52, patients predicted to fail succeeded 6.7% of the time
Waljee et al[29], 2019	To predict long-term response to ustekinumab	Random Forest modeling	Retrospective	Per week-8 model, only 11% predicted to fail achieved remission
Waljee et al[30], 2010	To predict response to thiopurines	Random Forest modeling	Retrospective	The model was superior to metabolite measurement in predicting non-responders.
Waljee et al[31], 2018	To externally validate previously developed thiopurine algorithm	Random Forest modeling	Retrospective	The algorithm accurately predicted objective remission with AUROC 0.76
Waljee et al[32], 2017	To identify patients in objective remission on thiopurines and analyze if these patients had fewer clinical events per year	Random Forest modeling	Retrospective	AUROC for algorithm-predicted remission was 0.79 vs 0.49 for thiopurine metabolite proving model superiority

AUROC: Area under the receiver operating characteristic curves; AI: Artificial intelligence; DL: Deep learning.