Artificial intelligence in small intestinal diseases: Application and prospects

Table 1 Applications of artificial intelligence in organ segmentation of the small intestine

Ref.	Diagnostic method	AI technology	Training set	Validating set	Outcomes
Tong et al[11]	CT	ML	90 images	-	DSC of duodenum: 69.26%
Kim et al[9]	CT	CNN	80 images	40 images	DSC of duodenum: 0.595
Peng et al[10]	CT	CNN	43 images	-	DSC of duodenum: 0.61
Fu et al[12]	MRI	CNN	100 images	20 images	Dice coefficient of duodenum: 65.50% ± 8.90%
					Dice coefficient of bowel: 86.60% ± 2.69%
Chen et al[13]	MRI	DL	66 images	36 images	DSC of duodenum: 0.80
Takiyama et al[15]	EGD	CNN	27335 images	17081 images	AUCs: 0.99
Igarashi et al[16]	EGD	ML	49174 images	36072 images	Accuracy (Ts: 0.993, Vs: 0.965)

AI: Artificial intelligence; AUCs: Area under the curves; CNN: Convolutional neural network; CT: Computed Tomography; DL: Deep learning; DSC: Dice similarity coefficient; EGD: Esophagogastroduodenoscopy; ML: Machine learning; MRI: Magnetic resonance imaging; Ts: Training set; Vs: Validating set.

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.

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.

Table 4 Applications of artificial intelligence in primary small intestinal tumor

Ref.	Diagnostic method	AI technology	Training set	Testing set	Outcomes
Inoue et al[88]	EGD	CNN	531 images	1080 images	Accuracy: 94.7%-100%
Liu et al[90]	CE	SVM	89 patients	-	Sen: 97.8%, Spe: 96.7%
Vieira et al[89,91]	CE	SVM	29 patients (936 images)	-	This SVM outperforms others by more than 5%
Barbosa et al[93]	CE	CNN	Ep: 104, Cp: 100	Ep: 92, Cp: 100	Sen: 98.7%, Spe: 96.6%
Panarelli et al[94]	MicroRNA sequencing	ML	84 samples	-	Accuracy (Ts: 98.5%, Vs: 94.4%)
Drozdov et al[95]	Gene expression profiling	ML	73 samples	-	Differentiated from normal cells (Sen: 100%, Spe: 92%), metastases prediction (Sen: 100%, Spe: 100%)
Kjellman et al[96]	Plasma protein multibiomarker	Random forestmodel	Ep:135, Cp: 143	-	AUCs: 0.97
Yan et al[97]	CT	Random forestmodel	213 patients	-	AUCs: 0.943

AI: Artificial intelligence; AUCs: Area under the curves; CE: Capsule endoscopy; CNN: Convolutional neural network; CT: Computed tomography; Cp: Control group; EGD: esophagogastroduodenoscopy; Ep: Experimental group; ML: Machine learning; SVM: Support vector machine; Sen: Sensitivity; Spe: Specificity; Ts: Training set; Vs: Validating set.