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

Table 1 Summary of artificial intelligence applications in predicting Helicobacter pylori infection

Ref.	Endoscopic modality	Training dataset	Validation dataset	Accuracy	Sensitivity	Specificity	PPV
Huang et al[78], 2004	WLI	30 patients	74 patients	85.1 (avg)1	78.8 (avg)	90.2 (avg)	-
Shichijo et al[79], 2017	WLI	32208 images, 1768 patients	11481 images, 397 patients	87.7	88.9	87.4	-
Itoh et al[81], 2018	WLI	149 images, 139 patients	30 images, 30 patients	-	86.7	86.7	-
Nakashima et al[84], 2018	WLI, BLI and LCI	162 patients	60 patients	-	96.7	-	-
Shichijo et al[80], 2019	WLI	98564 images, 4494 patients	23699 images, 847 patients	Infected: 66.0; post-eradication: 86.0	-	-	-
Zheng et al[82], 2019	WLI	11729 images, 1507 patients	3755 images, 452 patients	84.5	81.4	90.1	-
Zhu et al[100], 2019	WLI	790 images	203 images	89.2	76.5	95.6	89.7
Nakashima et al[85], 2020	WLI, BLI and LCI	12887 images, 395 patients	120 patients	80.0 (avg)2	61.3 (avg)	89.4 (avg)	74.7 (avg)

¹Histological characteristics were assessed for the various antrum, body and cardia locations.

²White light imaging and linked color imaging-based images were both analyzed, with the linked color imaging obtaining significantly higher accuracy, sensitivity, specificity and positive predictive value. BLI: Blue laser imaging; LCI: Linked color imaging; PPV: Positive predictive value; WLI: White light imaging.