Artificial intelligence in gastric cancer: Application and future perspectives

Table 4 Applications of artificial intelligence in gastric cancer prognosis based on different study population

Ref.	Year	Country/region	Number of cases	Study population	Methods	Results
Jiang et al[36]	2018	China	786 cases	Hospital	SVM classifier	AUCs (up to 0.834)
Lu et al[37]	2017	China	939 patients	Hospital	MMHG	Accuracy (69.28%)
Korhani Kangi et al[38]	2018	Iran	339 patients	Hospital	ANN, BNN	Sensitivity (88.2% for ANN, 90.3% for BNN), specificity (95.4% for ANN, 90.9% for BNN)
Zhang et al[39]	2019	China	669 cases	Hospital	ML	AUCs (up to 0.831)
Liu et al[40]	2018	China	432 GC tissue samples	Hospital	SVM classifier	Accuracy (up to 94.19%)
Bollschweiler et al[41]	2004	Germany, Japan	135 cases	Cancer center	ANN	Accuracy (93%)
Hensler et al[42]	2005	Germany, Japan	4302 cases	Cancer center	QUEEN technique	Accuracy (72.73%)
Jagric et al[43]	2010	Slovenia	213 cases	Clinical center	Learning vector quantization neural networks	Sensitivity (71%), specificity (96.1%)

SVM: Support vector machine; AUC: Area under the curve; MMHG: Multimodal hypergraph learning framework; ANN: Artificial neural networks; BNN: Bayesian neural network models; ML: Machine learning; GC: Gastric cancer; QUEEN technique: Quality assured efficient engineering of feedforward neural networks with supervised learning.