Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

Table 6 Artificial intelligence applications in hepatology: Diagnosis

Ref.	Diagnostic Modality	AI classifier	Sizes of the training/validation sets	Outcomes	Performance
Choi et al[122]	CT imaging	CNN	7461/4216/2987/1727 patients	Liver fibrosis staging (F0-F4)	83.11,5, 80.81,6, 74.4-80.21,7
				Classification among significant fibrosis, advanced fibrosis, and cirrhosis	92.1-95.01,6,7, 0.95-0.972,6,7, 84.6-95.53,6,7, 89.9-96.64,6,7
Kuppili et al[123]	US imaging	ELM, SVM	63 patients	Diagnosis of FLD	ELM: 81.7-92.41,6, 0.81-0.922,6, 85.10-91.303,6, 78.52-92.104,6, SVM: 76.14-86.421,6, 0.74-0.862,6, 76.80-88.203,6, 74.52-86.304,6
Gatos et al[124]	US shear wave elastography imaging	SVM	126 patients	Classification of chronic liver disease from healthy patients	87.31,6, 0.872,6, 93.53,6, 81.24,6
Chen et al[125]	Real-time tissue elastography imaging, age, sex	Several	513 patient (training:validation = 3:1)	Classification of liver fibrosis	80.44-82.871,6, 79.67-92.973,6, 46.25-82.504,6
Matake et al[126]	Clinicopathological parameters, CT imaging	ANN	120 patients	Classification among four types of focal liver lesions	0.9612,6
Oestmann et al[127]	Multiphasic MRI scans	CNN	150/10 patients	Classification of HCC and non-HCC lesions	94.11,5, 87.31,6, 0.9122,6
					For HCC: 92.73,6, 82.04,6
					For non-HCC: 82.03,6, 92.74,6
Kim et al[128]	MRI scans	CNN	4555,6/547 patients	HCC detection	0.972,6, 943,6, 994,6, 0.902,7, 873,7, 934,7
Cucchetti et al[129]	Laboratory results, clinicopathological parameters, radiological data, histological data	ANN	175/75 patients	MVI	0.922,5, 91.01,6
				Histopathological Grade	0.942,5, 93.31,6
Urman et al[130]	Metabolomic and proteomic analyses of bile	Several	139 patients	Classification of CCA and pancreatic adenocarcinoma	0.98-1.002,6, 88-94.13,6, 92.3-1004,6
Negrini et al[131]	Plasma bile acids profiles	Several	112 patients (training:validation = 4:1)	Classification of CCA and benign biliary disease	68.2-86.41,6, 0.77-0.952,6, 64-793,6, 63-1004,6
Logeswaran[132]	MRCP	MLP	55/5937 images	CCA diagnosis	92.8-96.31,6, 83.64-90.141,7

¹Accuracy (%).

²Area under the receiver operating curve or c-index.

³Sensitivity (%).

⁴Specificity (%).

⁵Training.

⁶Internal validation.

⁷External validation/testing. ANN: Artificial neural network; CCA: Cholangiocarcinoma; CNN: Convolutional neural network; CT: Computed tomography; ELM: Extreme learning machine; FLD: Fatty liver disease; HCC: Hepatocellular carcinoma; MLP: Multi-layer perceptron; MRCP: Magnetic resonance cholangiopancreatography MRI: Magnetic resonance imaging; MVI: Microvascular invasion; SVM: Support vector machine; US: Ultrasound.