Artificial intelligence in gastric cancer: Application and future perspectives

Table 2 Applications of artificial intelligence in pathology and computerized tomography based on different study population

Ref.	Year	Country/region	Number of cases	Study population	Methods	Results
Li et al[22]	2018	China	700 slices	Publicly gastric slice dataset	GastricNet	Accuracy (100%)
Sharma et al[23]	2017	Germany	454 cases	Hospital	CNN	Accuracy (0.6990 for cancer classification), accuracy (0.8144 for necrosis detection)
Leon et al[24]	2019	Colombia	40 images	Department of pathology	Deep CNN	Accuracy (up to 89.72%)
Iizuka et al[25]	2020	Japan	1746 biopsy histopathology WSIs	Hospital, TCGA	CNN, RNN	AUCs (up to 0.98), accuracy (95.6%)
Yoshida et al[26]	2018	Japan	3062 gastric biopsy specimens	Cancer center	ML	Overall concordance rate (55.6%)
Garcia et al[27]	2017	Peru	3257 images	-	Deep CNN	Accuracy (96.88%)
Liang et al[28]	2019	China	1900 images	-	DL	IoU (0.883), accuracy (91.09%)
Qu et al[29]	2018	Japan	9720 images/19440 images	Hospital	DL	AUCs (up to 0.965)
Sun et al[30]	2019	China	500 pathological images	Hospital	DL	IoU (0.8265), accuracy (91.60%)
Cao et al[31]	2019	China	1399 pathological sections	-	the Mask R-CNN	AP value (61.2)

GastricNet: The deep learning framework; CNN: Convolutional neural networks; WSIs: Whole slide images; TCGA: The Cancer Genome Atlas; RNN: Recurrent neural networks; AUC: Area under the curve; ML: Machine learning; DL: Deep learning; IoU: Intersection over union coefficient; AP: Average precision.