Table 1.

Studies exploring artificial intelligence algorithms for the diagnosis of early lesions and prediction of pancreatic cancer

Ref.	Purpose of the model	Type of study	Type of model	Input data	Type of validation	No. cases
Qureshi et al[19], 2022	Identifying predictive features on prediagnostic CT scans for PDAC	Retrospective	NB	4000 radiomics from CT	External	72 (36 with PC)
Sekaran et al[22], 2020	Predicting PC	Retrospective	CNN	19000 images from CT	Internal	80 (NS)
Chen et al[36], 2018	Identification and classification methods for PC on MRI	Retrospective	CNN	863 images from MRI	Internal	40 (20 with PC)
Muhammad et al[18], 2019	Prediction of PC risk	Retrospective	CNN	18 features of epidemiologic and clinical data	External	800144 (898 with PC)
Alves et al[8], 2022	Detection and localization of small PDAC lesions on contrast-enhanced CT	Retrospective	DL	242 images from CT-CE	External	242 (119 with PC)
Kuwahara et al[35], 2019	Investigate the value of EUS in predicting malignancy in IPMN	Retrospective	CNN	3970 radiomics from EUS	Internal	50 (23 malignant)
Hussein et al[3], 2019	Identification of IPMN	Retrospective	CAD	171 MRI images	Internal	171 (133 IMPN)
Chakraborty et al[15], 2018	Identification of high-risk IPMN	Retrospective	SVM	103 CT images	Internal	103 (27 high-risk IMPN)
Liu et al[28], 2020	Classifying images as cancerous or noncancerous PC	Retrospective	CNN	21105 CT images	Internal and external	1242 (752 with PC)
Lee et al[44], 2022	Prediction of risk for PC	Retrospective	DNN	9 factors	Internal and external	2952 (738 with PC)

NB: Naïve Bayes; CT: Computed tomography; CNN: Convoluted neural network; PDAC: Pancreatic ductal adenocarcinoma; PC: Pancreatic cancer; NS: No specification; DL: Deep learning; EUS: Endoscopic ultrasound; IMPN: Intraductal papillary mucinous neoplasms; CAD: Computer-aided diagnosis; SVM: Support vector machine; MRI: Magnetic resonance imaging; DNN: Deep neural network.