Artificial intelligence in gastric cancer: Application and future perspectives

Table 1 Applications of artificial intelligence in endoscopy based on different study population

Ref.	Year	Country/region	Number of cases	Study population	Methods	Results
Liu et al[7]	2016	China	400 images	Hospital	JDPCA	AUCs (0.9532), accuracy (90.75%)
Ali et al[8]	2018	Pakistan	176 images	Public images dataset	G2LCM	AUC (0.91), accuracy (87%)
Luo et al[9]	2019	China	1036496 images	Hospital	GRAIDS	Accuracy (up to 97.7%)
Sakai et al[10]	2018	Japan	29037 images	Hospital	CNN	Accuracy (87.6%)
Yoon et al[11]	2019	South Korea	11539 images	Hospital	VGG model	AUCs (0.981 for detection), AUCs (0.851 for depth prediction)
Nakahira et al[12]	2019	Japan	107284 images	Cancer Institute	Deep neural network	Kappa value (0.27)
Zhu et al[13]	2019	China	993 images	Hospital	CNN-CAD system	AUCs (0.94), accuracy (89.16%)
Wang et al[14]	2019	China	104864 images	Hospital	MCNN	Sensitivity (79.622%), specificity (78.48%)
Guimarães et al[15]	2020	Germany	270 images	Medical center	DL	AUCs (0.98), accuracy (93%)
Miyaki et al[16]	2015	Japan	100 cases	Hospital	SVM	Average output value (0.846 ± 0.220)
Liu et al[17]	2018	China	1120 M-NBI images/3068 images	Hospital	Deep CNN	Top accuracy (98.5%)
Horiuchi et al[18]	2019	Japan	2828 images	Hospital	CNN	Accuracy (85.3%)
Li et al[19]	2019	China	2088 images	Hospital	CNN	Accuracy (90.91%)
Bergholt et al[20]	2011	Singapore	1063 in vivo Raman spectra	Hospital	ACO-LDA algorithms	Sensitivity (94.6%), specificity (94.6%)
Duraipandian et al[21]	2012	Singapore	2748 in vivo Raman spectra	Hospital	PLS-DA algorithms	Accuracy (85.6%), specificity (86.2%)

JDPCA: Joint diagonalization principal component analysis; AUC: Area under the curve; G2LCM: Gabor-based gray-level co-occurrence matrix; GRAIDS: Gastrointestinal Artificial Intelligence Diagnostic System; CNN: Convolutional neural network; VGG model: Visual geometry group; CNN-CAD system: Convolutional neural network computer-aided detection; MCNN: Multicolumn convolutional neural network; DL: Deep learning; SVM: Support vector machine; M-NBI: Narrow-band imaging; ACO-LDA: Ant colony optimization integrated with linear discriminant analysis; PLS-DA: Partial least squares-discriminant analysis.