Optical diagnosis of colorectal polyps using convolutional neural networks

Table 2 Summary of the per-polyp results of studies on convolutional neural network algorithms for the optical diagnosis of colorectal polyps (cross-validation results not included)

Ref.	Image Modality (testing)	Sensitivity (%)	Specificity (%)	PPV (%)	NPV (%)	Accuracy for neoplasia (%)	PIVI 1 achieved (%)	PIVI 2 achieved (%)
Komeda et al[37]	Not specified	-	-	-	-	70	-	-
Chen et al[25]	Magnified NBI	96.3	78.1	89.6	91.5	90.1	-	Yes (91.5)
Byrne et al[23]	NBI-NF	98	83	90	97	94	-	Yes (97)
Zachariah et al[26]	NBI	-	-	-	96.5	93.1	Yes (98.3)	Yes (96.5)
	WLI	-	-	-	88.9	92.8	Yes (90.8)	No (88.9)
Ozawa et al[38]1	NBI	97	-	84	88	-	-	-
	WLI	98	-	85	88	-	-	-
Jin et al	NBI-NF	83.3	91.7	93.3	78.6	86.7	-	-
Song et al[39]	NBI-NF (test set 1)	84.1	74	88.3	67.7	-	-	-
	NBI-NF (test set 2)	88.5	72.1	88.6	84.7	-	-	-
Rodriguez-Diaz et al[28]	NBI-NF (90%) + NBI (10%)	95	88	-	93	-	Yes (94 (20/90 LC))	Yes (98 (6/68 LC))
van der Zander et al[27]	WLI + BLI	95.6	93.3	97.7	87.5	95.0	-	No (87.5)

¹Per frame analysis reported only.

WLI: White light imaging; BLI: Blue light imaging; NBI: Narrow band imaging; NBI-NF: Narrow band imaging–near focus; PIVI: Preservation and Incorporation of Valuable endoscopic Innovations; PPV: Positive predictor value; NPV: Negative predictor value; LC: Low-confidence.