Artificial intelligence in medical imaging of the liver

Table 1 Clinical application of artificial intelligence

n	Task	Type	Accuracy	Sensitivity	Specificity	Ref.
1	Detecting fatty liver disease and making risk stratification	Deep learning based on US	100%	100%	100%	[42]
2	Detecting and distinguishing different focal liver lesions	Deep learning based on US	97.2%	98%	95.7%	[43]
3	Evaluating liver steatosis	Deep learning based on US	96.3%	100%	88.2%	[49]
4	Evaluating chronic liver disease	Machine learning algorithm based on SWE	87.3%	93.5%	81.2%	[12]
5	Discriminating liver tumors	DCCA-MKL framework based on US	90.41%	93.56%	86.89%	[50]
6	Predicting treatment response	Machine learning algorithm based on MRI	78%	62.5%	82.1%	[58]

DCCA-MKL: Deep canonical correlation analysis-multiple kernel learning; MRI: Magnetic resonance imaging; US: Ultrasound.

Table 2 Liver leision detection

n	Task	Type	Accuracy	Ref.
1	Detecting liver new tumors	Deep learning based on CT	86%	[36]
2	Predicting the primary origin of liver metastasis	Deep learning based on CT	56%	[40]
3	Detecting cirrhosis with liver capsules	Deep learning based on ultrasound	96.8%	[41]
4	Detecting fatty liver disease and making risk stratification	Deep learning based on ultrasound	100%	[42]
5	Detecting and distinguishing different focal liver lesions.	Deep learning based on ultrasound	97.2%	[43]
6	Detecting metastatic liver malignancy	Deep learning based on PET/CT	90.5%	[44]

CT: Computed tomography; PET: Positron emission tomography.

Table 3 Diffuse liver disease staging

n	Type	AUCs	Ref.
1	Deep learning based on MRI	F4: 0.84; ≥ F3: 0.84; ≥ F2: 0.85	[45]
2	Deep learning based on CT	F4: 0.73; ≥ F3: 0.76; ≥ F2: 0.74	[46]
3	Deep learning based on SWE	F4: 0.97; ≥ F3: 0.98; ≥ F2: 0.85	[32]

AUC: Area under the receiver operating characteristic curve; MRI: Magnetic resonance imaging; CT: Computed tomography; SWE: Shear wave elastography.