Artificial intelligence for pancreatic cancer detection: Recent development and future direction

Table 1 Summary of studies assessing computed tomography and magnetic resonance using artificial intelligence-based approach for pancreatic cancer

Ref.	Overall dataset	Testing data	Model	Model performance on testing data
				Accuracy (%)	AUC	Sensitivity (%)	Specificity (%)	PPV (%)	NPV (%)
CT
Zhu et al[43], 2019 (United States)	439 cases	23 cases	CNN	NA	NA	94.1	98.5	NA	NA
Liu et al[42], 2019 (China)	338 patients	100 patients	CNN	NA	0.9632	NA	NA	NA	NA
Chu et al[44], 2019 (China)	380 patients	125 patients	ML	99.2%	0.999	100	98.5	NA	NA
Li et al[75], 2018 (China)	206 patients	No separate testing data (10-fold CV)	CNN	72.8%1	NA	NA	NA	NA	NA
Wei et al[37], 2018 (China)	260 patients	60 patients	SVM	NA	0.837	66.7	81.8	NA	NA
MR
Kaissis et al[49], 2020 (Germany)	207 patients	26 patients	ML	NA	0.93	84	92	NA	NA
Corral et al[50], 2019 (United States)	139 cases	No separate testing data (10-fold CV)	DL	NA	0.781	921	52%1	NA	NA
Gao et al[51], 2019 (China)	96 patients	No separate testing data (5-fold CV	DL	85.131	0.91171	NA	NA	NA	NA

¹The performance was based on n-fold cross-validation on training data.

AUC: Area under the curve; CNN: Convolutional neural network; CT: Computed tomography; CV: Cross-validation; DL: Deep learning; IPMN: Intraductal papillary mucinous neoplasm; MR: Magnetic resonance; NA: Not available; NN: Neural network; NPV: Negative predictive value; PCA: Principal component analysis; PPV: Positive predictive value; SVM: Support vector machine.