Application of convolutional neural network in detecting and classifying gastric cancer

Table 1 Detailed information on studies concerning automatic detection by convolutional neural network in gastric cancer

Ref.	Endoscopic images	Training dataset	Test dataset	Resolution	Sensitivity %	Specificity %	Accuracy/AUC %	PPV %	NPV %
Hirasawa et al[10] (2018)	WLI/NBI/chromoendoscopy images	13584	2296	300 × 300	92.2	NA	NA	30.6	NA
Ishioka et al[21] (2019)	Video images	NA	68	NA	94.1	NA	NA	NA	NA
Li et al[23] (2020)	M-NBI images	20000	341	512 × 512	91.18	90.64	90.91	90.64	91.18
Ikenoyama et al[24](2021)	WLI/NBI/chromoendoscopy images	13584	2940	300 × 300	58.4	87.3	75.7	26.0	96.5

AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value; WLI: White-light imaging; NBI: Narrow-band imaging; M-NBI: Magnifying narrow-band imaging; NA: Not applicable.