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

Table 6 Studies demonstrating impact of artificial intelligence on increasing efficacy of diagnostic modalities

No.	Ref.	Objective	Modality	Sensitivity	Specificity	Accuracy
1	Corral et al[67], 2019	Differentiate cystic SOL of pancreas	Fukuoka guideline	62%	77	77.5%
			Deep learning	75%	78%	78.3%
2	Kuwahara et al[83], 2019	Detection of malignant IPMN	Human pre-operative diagnosis (Clinical + lab + imaging)	95.7%	22.2%	56%
			Artificial intelligence	95.7%	92.66	94%
3	Gao et al[135], 2020	Ability to differentiate pancreatic disease	CE-MR	NA	NA	83.93%
			GAN	NA	NA	76.79%
4	Rigiroli et al[136], 2021	Detection of pancreatic cancer and SMA involvement	CT scan	NA	NA	71%
			Artificial intelligence	62%	77%	54%
5	Chen et al[137], 2023	Detection of pancreatic cancer	CT scan	89.9%	95.9%	AUC: 0.96
			CNN	90%	93%	NA
6	Tang et al[138], 2023	Pancreatic mass diagnosis	EUS FNA	81.6%	100%	87.9%
			CE EUS Master-guided FNA	90.9%	100%	93.8%

CE-MR: Contrast enhanced-magnetic resonance; CT: Computed tomography; CNN: Convoluted neural network; EUS: Endoscopic ultrasound; FNA: Fine needle aspiration; GAN: Generative adversarial network; IPMN: Intraductal papillary mucinous neoplasm; NA: Not available; SMA: Superior mesenteric artery; SOL: Space occupying lesion.