Artificial intelligence using advanced imaging techniques and cholangiocarcinoma: Recent advances and future direction

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

Ref.	Year	Type of AI	Imaging modality	Training (#)	Testing (#)	AUC	Sensitivity (%)	Specificity (%)
Matake et al[47], 2006	2006	ANN	CT	120 - patients	120 - patients	0.934	81.9	94.4
Ji et al[48], 2019	2019	ANN	CT	177 - patients	70 - patients	0.961	72	76.2
Logeswaran[49], 2009	2009	MLP	MRI	120 - images	593 - images	N/A	N/A
Yang et al[37], 2020	2020	ANN	MRI	80 - patients	20 - patients	0.9 (LMN)	85.8 (LMN)	81.8 (LMN)
						0.8 (differentiation)	73.2 (differentiation)	68.8 (differentiation)
Ghandour et al[51], 2021	2021	CNN	Cholangioscopy	254 - patients	95 - patients	0.86	0.81	0.91
Robles-Medrana et al[38], 2021	2021	ML	Cholangioscopy	1714 – images	198 - images	N/A	92	N/A
Pereira et al[50], 2022	2022	CNN	Cholangioscopy	5180 - images	1295 - images	1	99.3	99.4
Pattanpairoj et al[45], 2015	2015	ANN	Multivariate	85 - patients	22 - patients	N/A	98.71	96.94
Shao et al[44], 2018	2018	ANN	Multivariate	231 - patients	57 - patients	0.9544	N/A	N/A
Ji et al[41], 2019	2019	N/A	Multivariate	103 - patients	52 - patients	0.8462	86.8	76.3
Xu et al[42], 2019	2019	SVM	Multivariate	106 - patients	42 - patients	0.842	89.36	57.63
Zhao et al[43], 2019	2019	N/A	Multivariate	92 - patients	33 - patients	0.949	0.938	0.839
Müller et al[46], 2021	2021	ANN	Multivariate	233 - patients	60 - patients	0.89	N/A	N/A

ANN: Artificial neural network; MLP: Multi-layer perceptron; CNN: Convolutional neural network; ML: Machine learning; SVM: Support vector machine; CT: Computed tomography; MRI: Magnetic resonance imaging; N/A: Not applicable.