Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver

Table 1 Works proposed for automated image analysis

Image analysis	Anatomical area	Modality	AI model	Ref.
Segmentation	Pancreas	MRI	CNN	[33,34,110]
			UDCGAN	[111]
			3D-Unet	[112]
	Liver	CT	SSC (no AI)	[36]
			PA (Atlas-no AI)	[39]
		MRI	CNN	[37,38,42,113]
			GAN	[43]
Registration	Liver	CT, MRI	CNN	[47]
			SG-DIR (no AI)	[48]
			Cycle-GAN + UR-Net	[46]
		4D-MRI	Non-rigid	[49]

MRI: Magnetic resonance imaging; CT: Computerized tomography; 4D-MRI: Four-dimensional magnetic resonance imaging; CNN: Convolutional neural network; UDCGAN: U-Type densely connected generation adversarial network; SCC: Sparse shape composition; AI: Artificial intelligence; PA: Probabilistic atlas; GAN: Generation adversarial network; SG-DIR: Structure-guided deformable image registration; UR-Net: Unsupervised registration network.

Table 2 Summary of works based on artificial intelligence for automated diagnosis of pancreas and hepatobiliary system diseases

Anatomical area	Modality	AI model	What is diagnosed?	Ref.
Liver	Scintiscan	ANN	Chronic hepatitis and cirrhosis	[114]
	CT	ANN	HCC, intra-hepatic peripheral cholangiocarcinoma, hemangioma, metastases	[52]
		CNN	HCC, malignant liver tumors, indeterminate mases, hemangiomas, cysts	[53]
			Liver fibrosis	[50,115]
		SVM	Cirrhosis and HCC	[51]
			Malignant liver tumors	[54]
		KNN, SVM, RF	HCC	[116]
	MRI	CNN	HCC	[55]
			Simple cyst, cavernous hemangioma, FNH, HCC, ICC	[56,57]
		Extremely randomized trees	Adenomas, cysts, hemangiomas, HCC, metastases	[58]
	US	PNN	Benign and malignant focal liver lesions	[65]
		SVM	Fatty liver	[68]
			HCC	[66]
		CNN	Focal liver lesions: Angioma, metastasis, HCC, cyst, FNH	[67]
			Liver fibrosis stages	[69]
Biliary system	MRI	ANN	Cholangiocarcinoma	[59,60]
			Lymph node status in ICC	[117]
Pancreas	CT	Hybrid SVM-RF	Pancreas cancer	[76]
		SVM	Serous cystic neoplasms	[72]
		CNN	IPMN, mucinous cystic neoplasm, serous cystic neoplasm, solid pseudopapillary tumor	[73]
	MRI	SVM	IPMN	[78]
	US	ANN	Chronic pancreatitis, pancreatic adenocarcinoma	[81]
		CNN	Malignancy in IPMN	[82]
			Autoimmune pancreatitis, pancreatic ductal adenocarcinoma, chronic pancreatitis	[83]

CT: Computerized tomography; MRI: Magnetic resonance imaging; US: Ultrasound; ANN: Artificial neural network; CNN: Convolutional neural network; SVM: Support vector machine; KNN: K-nearest neighbors; RF: Random forest; PNN: Probabilistic neural network; HCC: Hepatocellular carcinoma; FNH: Focal nodular hyperplasia; ICC: Intrahepatic cholangiocarcinoma; IPMN: Intra-ductal papillary mucinous neoplasm.

Table 3 Summary of the works proposed to predict patient prognosis using artificial intelligence

Anatomical area	Pathology	Modality	AI model	What is prognosed?	Ref.
Liver	HCC	CT	ANN	Progression of hepatectomized patients with HCC	[85]
			CNN	Early recurrence of HCC	[88]
				Response to transarterial chemoembolization for patients with intermediate-stage HCC	[89]
			LASSO Cox regression	Early recurrence of HCC	[90]
				Recurrence of HCC after liver transplantation	[91]
				Recurrence of HCC after resection	[118]
		MRI	LR, RF	Response to intra-arterial treatment of HCC	[86,87]
		US	CNN, SVM	Response to transarterial chemoembolization for patients with HCC	[119]
Biliary system	Liver metastases, HCC, cholangiocarcinoma	CT	CNN	Prediction of hepatobiliary toxicity after liver SBRT	[92]
Pancreas	Postoperative pancreatic fistula	CT	RepTree	Prediction of postoperative pancreas fistulas after pancreatoduodenectomy	[93]

HCC: Hepatocellular carcinoma; CT: Computerized tomography; MRI: Magnetic resonance imaging; US: Ultrasound; ANN: Artificial neural network; CNN: Convolutional neural network; LR: Logistic regression; RF: Random forest; SVM: Support vector machine; SBRT: Stereotactic body radiotherapy.