Artificial intelligence in colonoscopy

Table 3 Narrow band imaging

Ref.	Study design	Algorithm type	Dataset	Results
Tischendorf et al[29]	Prospective Ex vivo	CAD – NBI (support vector machine)	209 polyp images	Accuracy: 85.3%
				Sensitivity: 90%
				Specificity: 70.2%
Gross et al[27]	Prospective Ex vivo	CAD – NBI (support vector machine)	434 polyp images	Accuracy: 93.1%
				Sensitivity: 95%
				Specificity: 90.3%
				NPV: 92.4%
Chen et al[31]	Retrospective	CAD – NBI (DCNN)	284 polyp images	Accuracy: 90.1%
				Sensitivity: 96.3%
				Specificity: 78.1%
				PPV: 89.6%
				NPV: 91.5%
Byrne et al[30]	Retrospective	CAD—NBI (DCNN)	125 polyp videos	Accuracy: 94%
				Sensitivity: 98%
				Specificity: 83%
				PPV: 90%
				NPV: 97%
Kominami et al[32]	Prospective	CAD –NBI (support vector machine)	118 polyps	Accuracy: 94.9%
				Sensitivity: 95.9%
				Specificity: 93.3%
				PPV: 95.9%
				NPV: 93.3%
Mori et al[33]	Prospective	CAD – NBI (support vector machine)	466 polyps	NPV: 95.2% to 96.5%
Song et al[35]	Prospective In vivo	CAD –NBI (DCNN)	363 polyps	Accuracy: 82.4%

CAD: Computer-aided diagnosis; NBI: Narrow band imaging; DCNN: Deep convolutional neural network.