Artificial intelligence in small intestinal diseases: Application and prospects

Table 2 Applications of artificial intelligence in celiac disease

Ref.	Diagnostic method	AI technology	Training set	Testing set	Outcomes
Chetcuti et al[62]	CE	ML	81 patients	-	Accuracy: 75.3%
Li et al[63]	CE	Computer-assisted recognition	Ep: 240, Cp: 220	-	Accuracy: 93.9%
Vicnesh et al[64]	CE	Computerized algorithm	21 patients	-	Accuracy: 89.82%
Zhou et al[65]	CE	CNN	Ep: 6, Cp: 5	Ep: 5, Cp: 5	Accuracy: 100%
Gadermayr et al[59]	EGD	Computer-assisted	290 patients (2835 images)	-	Accuracy: 94%-100%
Das et al[67]	Mucosal biopsies	Computer-assisted	Ep: 124, Cp: 137	Ep: 120, Cp: 105	Sen: 90.3%, Spe: 93.5%, AUCs: 96.2%
Wei et al[66]	Mucosal biopsies	DL	212 images	-	Accuracy: 95.3%, AUCs > 0.95
Pastore et al[70]	Clinical data	Computer-assisted	100 patients	-	Reliability: 0.813
Tenório et al[60]	Clinical data	Decision trees, Bayesian inference, k-nearest neighbor algorithm, support vector machines, artificial neural networks	178 patients	38 patients	Accuracy: 80.0%, Sen: 0.78, Spe: 0.80, AUCs: 0.84
Virta et al[68]	Micro-CT	Computer-assisted point cloud analysis	81 patients	-	Accuracy: 100%
Sangineto et al[69]	Gene expression in PBMCs	ML, random forest algorithm	Ep: 17, Cp: 20	-	Accuracy: 100%

AI: Artificial intelligence; AUCs: Area under the curves; CE: Capsule endoscopy; CNN: Convolutional neural network; Cp: Control group; DL: Deep learning; EGD: Esophagogastroduodenoscopy; Ep: Experimental group; ML: Machine learning; micro-CT: X-ray microtomography; PBMCs: Peripheral blood mononuclear cells; Sen: Sensitivity; Spe: Specificity.