Potential applications of artificial intelligence in colorectal polyps and cancer: Recent advances and prospects

Table 2 Performance of artificial intelligence in the detection and classification of colorectal polyps

Ref.	Patients, n	Samples, n	Sensitivity, %	Specificity, %	Accuracy, %	NPV, %	PPV, %
Mori et al[13]	152	176	92.0	79.5	89.2	-
Misawa et al[14]	-	100	84.5	97.6	90.0	82.0	98.0
Kominami et al[15]	41	118	95.9	93.3	94.9	93.3	95.9
Mori et al[16]	123	205	89.0	88.0	89.0	76.0	95.0
Misawa et al[17]	58	64	94.3	71.4	87.8	83.3	89.2
Chen et al[18]	193	284	96.3	78.1	90.1	91.5	89.6
Misawa et al[19]	73	155	90.0	63.3	76.5	-	-
Shin et al[20]	-	366	95.9	95.9	95.9	-	96.4
Wang et al[21]	1138	27113	94.4	95.9	-	-	-
Kudo et al[22]	89	100	96.9 (stained)	100.0	98.0	94.6	100.0
			96.9 (NBI)	94.3	96.0	94.3	96.9
Min et al[23]	91	181	83.3	70.1	78.4	71.2	82.6
Sánchez-Montes et al[24]	-	225	92.3	89.2	91.1	87.1	93.6
Horiuchi et al[25]	77	258	80.0	95.3	91.5	93.4	85.2
Byrne et al[26]	-	106	98.0	83.0	94.0	97.0	90.0

NBI: Narrow-band imaging; NPV: Negative predictive value; PPV: Positive predictive value.