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

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.