Role of artificial intelligence in the characterization of indeterminate pancreatic head mass and its usefulness in preoperative diagnosis

Table 4 Studies on differentiation of indeterminate lesions using artificial intelligence algorithms on endoscopic ultrasonography images

No.	Ref.	Number of patients	Primary outcome	Sub type of AI used	Outcome
1	Zhu et al[78], 2013	262	PDAC vs CP	SVM	Accuracy: 94.2%
2	Zhu et al[131], 2015	100	AIP vs CP	SVM	Accuracy: 89.3%
3	Zhang et al[74], 2010	216	Normal pancreas vs PDAC	SVM	Accuracy: 97.98%
4	Ozkan et al[76], 2016	332	Recognition of pancreatic cancer amongst various age group	ANN	Accuracy: Average: 87.5% (all ages), Min: 88.46% (40-60 yr), Max: 92% (< 40 yr)
5	Kuwahara et al[83], 2019	50	Benign vs malignant IPMN	CNN	Accuracy: 94%
6	Das et al[75], 2008	56	PDAC vs normal pancreas vs CP	ANN	AUC: 0.93
7	Săftoiu et al[80], 2008	68	Benign vs malignant pancreatic lesion	ANN	Accuracy: 89.7%
8	Tonozuka et al[132], 2021	139	PDAC vs CP	CNN	AUC: 0.94
9	Marya et al[133], 2021	583	PDAC vs benign causes of pancreatic SOL	CNN	AUC: 0.976
10.	Xu et al[134], 2013	Systemic Analysis of 6 studies	Benign vs malignant pancreatic lesion	-	AUC: 0.962

AI: Artificial intelligence; AIP: Autoimmune pancreatitis; ANN: Artificial neural network; CNN: Convoluted neural network; CP: Chronic pancreatitis; IPMN: Intraductal papillary mucinous neoplasm; PDAC: Pancreatic ductal adenocarcinoma; SOL: Space occupying lesion; SVM: Support vector machine.