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.