Table 3.

Summary of studies focused on artificial intelligence applications for automatic polyp detection, classification, and segmentation

Study	Screening test	Imaging modality	Data type	AI-based algorithm	Contribution	Acc	Sen	Spe
Wimmer et al[46]	Colonoscopy	WL, NBI	Images	k-nearest neighbours	Polyp classification: non-neoplastic, neoplastic	80%	-	-
Tajbakhsh et al[22]	Colonoscopy	WL	Images	Decision trees; Random forest	Automatic polyp detection	-	88%	-
Hu et al[21]	CT Colonography	Greyscale	Images	Random forest	Polyp classification: non-neoplastic, neoplastic	-	-	-
Zhang et al[50]	Colonoscopy	WL, NBI	Images	CNN: Caffenet	Polyp detection and classification: benign from malignant	86%	88%	-
Shin et al[23]	Colonoscopy	WL	Images	Support vector machine	Whole image classification: polyps from non-polyps	96%	96%	96%
Sánchez-González et al[32]	Colonoscopy	WL	Images	Random forest; CNN: Bayesnet	Polyp segmentation	97%	76%	99%
Tan et al[52]	CT Colonography	Greyscale	Images	Customized CNN	Polyp classification: adenoma from adenocarcinoma	87%	90%	71%
Fonolla et al[51]	Colonoscopy	WL, NBI, LCI	Images	CNN: EfficientNet	Polyp classification: benign from pre-malignant	95%	96%	93%
Hwang et al[46]	Colonoscopy	WL	Images	Customized CNN	Polyp detection and segmentation	-	-	-
Park et al[53]	Colonoscopy	WL	Images	Customized CNN	Whole image classification: normal, adenoma and adenocarcinoma	94%	~94%	-
Viscaino et al[54]	Colonoscopy	Greyscale	Images	Support vector machine; Decision treesk-nearest neighbours; Random forest	Whole image classification: polyp and non-polyp	97%	98%	96%

WL: White light; NBI: Narrow-band imaging; LCI: Linked colour imaging; Acc: Accuracy; Sen: Sensitivity; Spe: Specificity.