Current situation and prospect of artificial intelligence application in endoscopic diagnosis of Helicobacter pylori infection

Table 1 Characteristics of current studies about AI-assisted endoscopic diagnosis of Helicobacter pylori infection

Ref.	Type of AI	Type of endoscopy	Training set	Validation set	AUC	Sensitivity (%)	Specificity (%)	Accuracy (%)
Huang et al[39], 2004	RFSNN	WLI	30 patients	74 patients	NA	85.4	90.9	NA
Huang et al[40], 2008	SVM with SFFS	WLI	236 patients	236 patients	NA	82.6 (antrum); 89.1 (body); 100 (cardia)	94.0 (antrum); 85.8 (body); 72.0 (cardia)	87.8 (antrum); 87.6 (body); 86.7 (cardia)
	SVM without SFFS	WLI	236 patients	236 patients	NA	98.5 (antrum); 98.7 (body); 99.1 (cardia)	70.8 (antrum); 71.5 (body); 70.3 (cardia)	86.3 (antrum); 86.4 (body); 86.0 (cardia)
Shichijo et al[41], 2017	CNN (first)	WLI	1750 patients, 32208 images	397 patients, 11481 images	0.89	81.9	83.4	83.1
	CNN (second, constructed according to anatomical locations)	WLI	1750 patients, 32208 images	397 patients, 11481 images	0.93	88.9	87.4	87.7
Shichijo et al[42], 2019	CNN	WLI	5236 patients, 98564 images	847 patients, 23699 images	NA	NA	NA	48 (H. pylori-positive); 84 (H. pylori-eradicated); 80 (H. pylori-negative)
Zheng et al[45], 2019	CNN (first, single image for all image)	WLI	1507 patients, 76146 images	452 patients, 3755 images	0.93	81.4	90.1	84.5
	CNN (second, single image by different locations)	WLI	1507 patients, 76146 images	452 patients, 3755 images	0.90 (antrum); 0.91 (angularis); 0.94 (corpus); 0.82 (fundus)	76.1 (antrum); 78.8 (angularis); 81.6 (corpus); 72.4 (fundus)	88.5 (antrum); 90.5 (angularis); 92.1 (corpus); 80.5 (fundus)	80.3 (antrum); 82.8 (angularis); 85.6 (corpus); 75.3 (fundus)
	CNN (third, multiple images per patient)	WLI	1507 patients, 76146 images	452 patients, 3755 images	0.97	91.6	98.6	93.8
Yoshii et al[19], 2020	ML (model without H. pylori eradication history)	WLI	NA	498 patients	NA	91.6 (non-infection); 75.0 (past infection); 59.5 (current infection)	88.6 (non-infection); 89.9 (past infection); 94.7 (current infection)	88.6
	ML (model with H. pylori eradication history)	WLI	NA	498 patients	NA	94.0 (non-infection); 94.0 (past infection); 88.1 (current infection)	93.4 (non-infection); 100.0 (past infection); 94.7 (current infection)	93.4
Nakashima et al[49], 2018	CNN	WLI	162 patients, 1944 images	60 patients, 60 images	0.66	66.7	60.0	NA
	CNN	BLI-bright	162 patients, 1944 images	60 patients, 60 images	0.96	96.7	86.7	NA
	CNN	LCI	162 patients, 1944 images	60 patients, 60 images	0.95	96.7	83.3	NA
Yasuda et al[21], 2020	SVM	LCI	32 patients, 128 images	105 patients, 525 images	NA	90.4	85.7	87.6%

AI: Artificial intelligence; AUC: Area under curve; BLI: Blue laser imaging; CNN: Convolutional neural network; H. pylori: Helicobacter pylori; LCI: Linked color imaging; ML: Machine learning; NA: Not applicable; RFSNN: Refined feature selection with neural network; SFFS: Sequential forward floating selection; SVM: Support vector machine; WLI: White-light imaging.