Ultrasound-based artificial intelligence in gastroenterology and hepatology

Table 2 Application of ultrasound-based artificial intelligence in focal liver lesions

Ref.	Diseases: number of cases	Type of ultrasound	Algorithm of AI	Performance
Xi et al[42]	Benign lesions: 300	B-mode	CNN	All lesions
				Accuracy: 84%
				Uncertain set of lesions
	Malignant lesions: 296			Accuracy: 79%
Yang et al[43]	Benign tumor: 427	B-mode	CNN	AUC for EV: 0.924
				Sensitivity: 86.5%
	Malignant tumor: 1786
				Specificity: 85.5%
Virmani et al[44]	HCC: 27	B-mode	SVM	Accuracy of HCC: 91.6%
				Sensitivity
	Metastatic liver tumor: 24			HCC: 90%
				Metastatic liver tumor: 93.3%
Hwang et al[49]	Cyst: 29	B-mode	ANN	Accuracy: 96%
				Cyst vs hemangioma
				Cyst vs malignant
	Hemangioma: 37
				Hemangioma vs malignant
	Malignant: 33
Schmauch et al[50]	Non-tumorous liver: 258	B-mode	CNN	AUC
	Hemangioma: 17			FLL detection: 0.935
	Metastasis: 48
	HCC: 6
				FLL discrimination: 0.916
	Cyst: 30
	FNH: 8
Tiyarattanachai et al[51]	HCC: 2414	B-mode	CNN	Detection rate: 87.0%
	Cyst: 6600			Sensitivity: 83.9%
	Hemangioma: 5374
				Specificity: 97.1%
	Focal fatty sparing: 5110
	Focal fatty infiltration: 934
Gatos et al[47]	Benign FLL: 30	CEUS	SVM	Accuracy: 90.3%
				Sensitivity: 93.1%
	Malignant FLL: 22			Specificity: 86.9%
Kondo et al[46]	Benign FLL: 31	CEUS	SVM	Benign vs malignant
				Accuracy: 91.8%
				Sensitivity: 94%
				Specificity: 87.1%
				Accuracy
	Malignant FLL: 67
				Benign: 84.4%
				HCC: 87.7%
				Metastatic liver tumor: 85.7%
Guo et al[48]	Benign FLL: 46	CEUS	Deep canonical correlation analysis and multiple kernel learning	Accuracy: 90.4%
				Sensitivity: 93.6%
	Malignant FLL: 47			Specificity: 86.8%
Streba et al[52]	HCC: 41	CEUS	ANN	Training accuracy: 94.5%
	Hypervascular liver metastasis: 20			Testing accuracy: 87.1%
	Hypovascular liver metastasis: 12			Sensitivity: 93.2%
				Specificity: 89.7%
	Hemangioma: 16
	Focal fatty changes: 23
Căleanu et al[53]	HCC: 30	CEUS	Deep neural network	Accuracy: 88%
	Hypervascular liver metastasis: 11
	Hypovascular liver metastasis: 11
	Hemangioma: 23
	FNH: 16
Dong et al[56]	HCC: 322	B-mode	Radiomics	AUC: 0.81
Hu et al[57]	HCC: 482	CEUS	Radiomics	AUC: 0.731
	Training cohort: 341
	Validation cohort: 141
Zhang et al[58]	HCC: 313	CEUS	Radiomics	AUC
	Primary cohort: 192			Primary dataset: 0.849
	Validation cohort: 121			Validation dataset: 0.788
Liu et al[63]	HCC: 130	CEUS	Deep learning radiomics	AUC: 0.93
	Training cohort: 89
	Validation cohort: 41
Ma et al[66]	HCC: 318	CEUS	Radiomics	AUC: 0.89
	Training cohort: 255
	Validation cohort: 63
Liu et al[69]	HCC: 419	CEUS	Deep learning radiomics	C-index
	RFA: 214			RFA: 0.726
				SR: 0.741
	SR: 205

AI: Artificial intelligence; ANN: Artificial neural network; AUC: Area under the receiver operating characteristic curve; CEUS: Contrast-enhanced ultrasound; CNN: Convolutional neural network; EV: External validation; HCC: Hepatocellular carcinoma; FNH: Focal nodular hyperplasia; FLL: Focal liver lesion; RFA: Radiofrequency ablation; SR: Surgical resection; SVM: Support vector machine.