Artificial intelligence in small intestinal diseases: Application and prospects

Table 3 Applications of artificial intelligence in small intestinal Crohn’s disease

Ref.	Diagnostic method	AI technology	Training set	Testing set	Outcomes
Yang et al[78]	Microultrasound	CNN	43 mice	-	AUCs: 0.8831
Shen et al[80]	Clinical data	Computerized algorithm	Ep1: 61, Cp1: 78	Ep2:42, Cp2: 57; Ep3:84, Cp3: 495	AUCs: 0.92
Bottigliengo et al[81]	Clinical data	BMLTs (NB, BN, BART)	152 patients	-	AUCs without genetic variables (NB: 0.71, BN: 0.50, BART: 0.76), AUCs with genetic variables (NB: 0.75, BN: 0.67, BART: 0.78)
Taylor et al[79]	Clinical data	ML (elastic net and random forest)	480 first-degree relatives	-	AUCs (elastic net): 0.80, AUCs (random forest): 0.87
Menti et al[82]	Clinical data	BMLTs	152 patients	-	Accuracy without genetic variables: 82%, accuracy with genetic variables: 89%
Klang et al[77]	CE	DL	49 patients (17640 images)	-	AUCs: 0.94-0.99, accuracy: 95.4%-96.7%
Parfеnov et al[76]	CE	Computerized algorithm	25 patients	-	44% patients confirmed only with the help of AI
Lamash et al[74,75]	MRI	CNN	15 patients	8 patients	Dice coefficients: 75%-97%

AI: Artificial intelligence; AUCs: Area under the curves; BART: Bayes additive return trees; BMLTs: Bayesian machine learning techniques; BN: Bayesian network; CE: Capsule endoscopy; DL: Deep learning; CNN: Convolutional neural network; Cp: Control group; Ep: Experimental group; ML: Machine learning; MRI: Magnetic resonance imaging; NB: Naive Bayes.