Artificial intelligence in liver ultrasound

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.