Artificial intelligence in small intestinal diseases: Application and prospects

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.