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

Table 5 Studies on differentiation of indeterminate lesions using artificial intelligence algorithms on different biomarkers

No.	Ref.	Number of samples	Type of biomarker used	Sub-type of AI used	Conclusion
1	Chen et al[104], 2019	28	Exosomes	LDA	Accuracy: 100%
2	Zheng et al[105], 2022	220	Exosomes	ANN	AUC: 0.86
3	Ko et al[106], 2017	28	Exosomes	LDA	Accuracy: 100%
4	Cristiano et al[107], 2019	34	Cell-free DNA	GBM	AUC: 0.86
5	Yu et al[108], 2020	501	extracellular vesicles long RNA	SVM	AUC: 0.96
6	Gao et al[109], 2012	199	Proteomes	SVM, KNN, ANN	AUC: 0.971
7	Yu et al[110], 2005	100	Proteomes	DT	Sensitivity: 88.9%, specificity: 74.1%
8	Qiao et al[112], 2022	136	Proteomes	CNN	Accuracy: 87.63%
9	Alizadeh et al[113], 2020	671	Circulating micro RNA	ANN	Accuracy: 0.86

AI: Artificial intelligence; ANN: Artificial neural network; CNN: Convoluted neural network; DT: Digital transformation; KNN: K-nearest neighbor; GBM: Gradient boosting machine; LDA: Linear discriminant analysis; SVM: Support vector machine.