Application of artificial intelligence-driven endoscopic screening and diagnosis of gastric cancer

Table 2 Summary of artificial intelligence applications in prediction of invasion depth and differentiation of cancerous areas from noncancerous areas

Ref.	Application	Endoscopic modality	Training dataset	Validation dataset	Accuracy	Sensitivity	Specificity	PPV	NPV
Kubota et al[98], 2012	Prediction of invasion depth	WLI	344 patients, 902 images	-	77.2 (T1)	-	-	80.1 (T1)
Miyaki et al[137], 2013	Differentiation of cancerous areas from noncancerous areas	WLI and magnified FICE	493 images	46 images	85.9	84.8	87.0	86.7	85.1
Hirasawa et al[99], 2018	Differentiation of cancerous areas from noncancerous areas	WLI	13584 images	2296 images, 69 patients	92.2	92.2	-	30.6	-
Kanesaka et al[138], 2018	Detection of EGC	Magnified NBI	126 images	81 images	96.3	96.7	95.0	98.3	-
Horiuchi et al[103], 2020	Differentiation of EGC from gastritis	Magnified NBI	2570 images	258 images	85.3	95.4	71.0	82.3	91.7
Yoon et al[101], 2019	Detection of EGC and prediction of EGC invasion depth	WLI	11686 images, 800 patients	-		79.2	77.8	79.3	77.7
Horiuchi et al[105], 2020	Detection of EGC	Magnified NBI	2570 images	174 videos, 82 patients	85.1	87.4	82.8	83.5	86.7
Li et al[139], 2020	Differentiation of EGC from noncancerous lesions	Magnified NBI	2088 images	342 images	90.9	91.2	90.6	90.6	91.2
Nagao et al[102], 2020	Prediction of invasion depth	WLI, nonmagnifying NBI and indigo-carmine dye contrast imaging (Indigo)	16557 images, 1084 patients	-	94.4	89.2	98.7	98.3	91.7
Namikawa et al[104], 2020	Differentiation of cancerous areas from noncancerous areas	WLI, nonmagnifying NBI and indigo-carmine dye contrast imaging (Indigo)	18410 images	1459 images	95.9	99.0	93.3	92.5	-

EGC: Early gastric cancers; FICE: Flexible spectral imaging color enhancement; NBI: Narrow-band imaging; NPV: Negative predictive value; PPV: Positive predictive value; WLI: White light imaging.