Artificial intelligence in liver ultrasound

Table 1 Studies using artificial intelligence based on ultrasound for fatty liver disease diagnosis

Task	Reference standard	Sample size	Method	Results	Ref.
Fatty liver disease diagnosis	Liver biopsy	55 patients with severe obesity, 38 of whom had fatty liver disease	Deep learning with B-mode image ultrasound	Sensitivity: 100%; specificity: 88%; accuracy: 96%; AUC: 0.98	[26]
Fatty liver disease diagnosis	Radiologist qualitative score	157 ultrasound liver images from unknown number of participants	Deep learning with B-mode image ultrasound	Sensitivity: 95%; specificity: 85%; accuracy: 90.6%; AUC: 0.96	[28]
NAFLD assessment	MRI proton density fat fraction	204 participants, 140 of whom had NAFLD, 64 control participants	One-dimensional CNNs	Sensitivity: 97%; specificity: 94%; accuracy: 96%; AUC: 0.98	[31]
NAFLD assessment	MRI proton density fat fraction	135 adult participants with known or suspected NAFLD	Transfer learning with a pretrained CNN by four ultrasound views of liver routinely obtained	SCC: 0.81; AUC: 0.91 (PDFF ≥ 5%)	[27]
NAFLD assessment	Liver biopsy	295 subjects, 198 mild fatty liver, one moderate degree of fatty liver	DCNN-based organ segmentation with Gaussian mixture modeling for automated quantification of the HRI	ICC of two radiologists and DCNN were 0.919, 0.916, 0.734	[33]
The severity of fatty liver	Abdominal ultrasound	21855 B-mode ultrasound images, 2070 patients with different severities from none to severe fatty liver	Pretrained CNN models with B-mode ultrasound images	The areas under the receiver operating characteristic curves were 0.974 (mild steatosis vs others), 0.971 (moderate steatosis vs others), 0.981 (severe steatosis vs others), 0.985 (any severity vs normal) and 0.996 (moderate-to-severe steatosis clinically abnormal vs normal-to-mild steatosis clinically normal)	[29]

AUC: Area under curve; CNN: Convolutional neural network; DCNN: Deep convolutional neural network; HRI: Hepato-renal index; ICC: Intraclass correlation; MRI: Magnetic resonance image; NAFLD: Nonalcoholic fatty liver disease; PDFF: Proton density fat fraction; SCC: Spearman correlation coefficient.

Table 2 Studies using artificial intelligence based on ultrasound for focal liver lesion diagnosis

Modality and task	Approach	Target disease: number of the case	Performance	Ref.
Classifying different FLLs based on B-mode	ANN	Cyst: 29; hemangioma: 37; malignant tumor: 33	Cyst vs hemangioma accuracy: 99.7%; cyst vs malignant tumor accuracy: 98.7%; hemangioma vs malignant tumor accuracy: 96.1%	[40]
Differentiating benign and malignant lesions based on B-mode	CNN	Benign lesions: 300; malignant lesions: 296	All lesion accuracy: 84%; uncertain set of lesion accuracy: 79%	[37]
Classifying different FLLs based on B-mode	ANN (sparse autoencoder)	Normal liver: 16; cyst: 44; hemangioma: 18; HCC: 30	overall accuracy: 97.2%; overall sensitivity: 98%; overall specificity: 95.7%	[41]
Differentiating benign and malignant lesions based on B-mode	FSVM	training set; DS1: benign lesions: 132, malignant lesions: 68; DS2: malignant liver cancer: 50, hepatocellular adenoma: 150, hemangioma: 35, focal nodular hyperplasia: 145, lipoma: 70	DS1: accuracy: 97%, sensitivity: 100%, specificity: 95.5%, AUC: 0.984; DS2: accuracy: 95.1%, sensitivity: 92.0%, specificity: 95.5%, AUC: 0.971	[36]
Classifying different FLLs based on B-mode	CNN	Non-tumorous liver: 258, hemangioma: 17, HCC: 6, cyst: 30, focal nodular hyperplasia: 8	AUC for tumor detection: 0.935; AUC for tumor discrimination (mean): 0.916	[42]
Diagnosing HCC based on B-mode	CNN	Malignant tumor: 1786; benign tumor: 427	AUC for EV: 0.924	[38]
Differentiating benign and malignant lesions based on B-mode	CNN	HCC: 6; cyst: 6600; hemangioma: 5374; focal fatty sparing: 5110; focal fatty infiltration: 934	IV: overall sensitivity: 83.9%; overall specificity: 97.1%; HCC detection rate: 85.3%; EV: overall sensitivity: 84.9%; overall specificity: 97.1%; HCC detection rate: 78.3%	[39]
Classifying different FLLs based on CEUS	ANN	hemangioma: 16; focal fatty liver: 23; HCC: 41; metastatic tumor: 32 (hypervascular: 20 hypovascular: 12)	Accuracy: 94.5%; sensitivity: 93.2%; specificity: 89.7%	[47]
Differentiating benign and malignant lesions based on CEUS	Deep belief networks	HCC: 6; hemangioma: 10; liver abscess: 4; metastases: 3; focal fatty sparing: 3	Accuracy: 83.4%; sensitivity: 83.3%; specificity: 87.5%	[59]
Differentiating benign and malignant lesions based on CEUS	SVM	Benign tumor: 30; malignant tumor: 22	Accuracy: 90.3%; sensitivity: 93.1%; specificity: 86.9%	[45]
Differentiating benign and malignant lesions based on CEUS	SVM	Benign tumor, HCC or metastatic tumor: 98	Benign vs malignant accuracy: 91.8%, sensitivity: 93.1%, specificity: 86.9%; benign vs HCC vs metastatic carcinoma: accuracy: 85.7%; sensitivity: 84.4%; specificity: 87.7%	[46]
Differentiating benign and malignant lesions based on CEUS	Deep canonical correlation analysis + multiple kernel learning	Benign tumor: 46; malignant tumor: 47	Accuracy: 90.4%; sensitivity: 93.6%; specificity: 86.9%	[43]
Differentiating benign and malignant lesions based on CEUS	3D-CNN	HCC: 2110; focal nodular hyperplasia: 2310	Accuracy: 93.1%; sensitivity: 94.5%; specificity: 93.6%	[44]
Differentiating benign and malignant lesions based on CEUS	Deep neural network	Focal nodular hyperplasia: 16; HCC: 30; hemangioma: 23; hypervascular metastasis: 11; hypovascular metastasis: 11	Top accuracy: 88%	[48]
Differentiating benign and malignant lesions based on CEUS	CNN	Development set: malignant tumor: 281, benign tumor: 82; testing set: malignant tumor: 164, benign tumor: 47	Accuracy: 91.0%; sensitivity: 92.7%; specificity: 85.1%; AUC: 0.934	[49]

ANN: Artificial neural network; AUC: Area under curve; CEUS: Contrast-enhanced ultrasound; CNN: Convolutional neural network; DS1: Database 1; DS2: Database 2; EV: External validation; FLL: Focal liver lesion; FSVM: Fuzzy support vector machine; HCC: Hepatocellular carcinoma; IV: Internal validation; SVM: Support vector machine; 3D: Three-dimensional.