Ultrasound-based artificial intelligence in gastroenterology and hepatology

Table 1 Application of ultrasound-based artificial intelligence in diffuse liver diseases

Ref.	Diseases: number of cases	Type of ultrasound	Algorithm of AI	Performance
Byra et al[21]	Severely obese patients: 55	B-mode	CNN	Sensitivity: 100%
				Specificity: 88%
				Accuracy: 96%
				AUC: 0.98
	Fatty liver disease: 38
Biswas et al[22]	Normal patients: 27	B-mode	Deep learning	Accuracy: 100%
	Fatty liver disease: 36			AUC: 1.0
Han et al[24]	NAFLD: 140	B-mode	CNN	Sensitivity: 97%
				Specificity: 94%
				Accuracy: 96%
	Control: 64
				AUC: 0.98
Yeh et al[28]	Postsurgical human liver samples: 20	B-mode	SVM	F2 accuracy: 91%
				F3 accuracy: 85%
				F4 accuracy: 81%
				F6 accuracy: 72%
Zhang et al[29]	Liver fibrosis or cirrhosis: 239	Duplex	ANN	Sensitivity: 95%
				Specificity: 85%
	Training group: 179
	Validation group: 60			Accuracy: 88%
Gao et al[30]	S0: 4	B-mode	ANN	S0 accuracy: 100%
	S1: 16			S1 accuracy: 90%
				S2 accuracy: 70%
				S3 accuracy: 90%
	S2: 8			S4 accuracy: 100%
	S3: 5
	S4: 4
Lee et al[31]	Patients: 3446	B-mode	CNN	AUC: 0.86
	Internal validation set: 263
	Internal test set: 266
	External test set: 572
Gatos et al[34,35]	Chronic liver disease: 70	Shear-wave elastography	SVM	Sensitivity: 94%
	Healthy: 56			Specificity: 81%
				Accuracy: 87%
Wang et al[36]	Liver fibrosis: 398	Shear-wave elastography	Deep learning radiomic	F4 AUC: 0.97
	Training group: 266
	Validation group: 132			F3 AUC: 0.98
				F2 AUC: 0.85
Xue et al[38]	Liver fibrosis: 401	Elastography	CNN by TL radiomics	S2 AUC: 0.95
				S3 AUC: 0.93
	Patient without fibrosis: 65
				S4 AUC: 0.93

AI: Artificial intelligence; ANN: Artificial neural network; AUC: Area under the receiver operating characteristic curve; CNN: Convolutional neural network; NAFLD: Nonalcoholic fatty liver disease; SVM: Support vector machine; TL: Transfer learning.

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.

Table 3 Application of ultrasound-based artificial intelligence in gastrointestinal disease

Ref.	Diseases: number of cases	Type of ultrasound	Algorithm of AI	Performance
Kim et al[76]	GISTs: 125	B-mode EUS	CNN	Sensitivity: 83.0%
	Leiomyomas: 33			Specificity: 75.5%
				Accuracy: 79.2%
	Schwannomas: 21
Norton et al[80]	Chronic pancreatitis: 14	B-mode EUS	Basic neural network	Sensitivity: 89%
	Pancreatic cancer: 21			Accuracy: 80%
Das et al[81]	Chronic pancreatitis: 12	B-mode EUS	ANN	Sensitivity: 93%
	Pancreatic cancer: 22
				Specificity: 92%
	Normal patient: 22
				AUC: 0.93
Zhu et al[82]	Chronic pancreatitis: 126	B-mode EUS	SVM	Sensitivity: 96.25%
				Specificity: 93.38%
				Accuracy: 94.2%
	Pancreatic cancer: 262
Zhang et al[83]	Pancreatic cancer: 153	B-mode EUS	SVM	Sensitivity: 94.32%
				Specificity: 99.45%
	Normal patient: 63
				Accuracy: 97.98%
Ozkan et al[84]	Pancreatic cancer: 202	B-mode EUS	ANN	Sensitivity: 83.3%
				Specificity: 93.3%
	Normal patient: 130			Accuracy: 87.5%
Tonozuka et al[85]	Chronic pancreatitis: 34	B-mode EUS	CNN	Sensitivity: 90.2%
	Pancreatic cancer: 76
	Normal patient: 29
				Specificity: 74.9%
Săftoiu et al[88]	Chronic pancreatitis: 47	EUS elastography	ANN	Sensitivity: 87.59%
				Specificity: 82.94%
	Pancreatic cancer: 211
Săftoiu et al[90]	Chronic pancreatitis: 55	Contrast-enhanced EUS	ANN	Sensitivity: 94.64%
	Pancreatic cancer: 122			Specificity: 94.44%
Kuwahara et al[94]	IPMN: 50	B-mode EUS	CNN	Sensitivity: 95.7%
				Specificity: 92.6%
				Accuracy: 94.0%
Zhang et al[95]	Training: 291	B-mode EUS	CNN	Accuracy: 90.0%
	Testing: 181
Chen et al[101]	Rectal cancer: 127	Endorectal ultrasound	ANN	Sensitivity: 72.7%
				Specificity: 75.9%
		Shear-wave elastography
				AUC: 0.743

AI: Artificial intelligence; ANN: Artificial neural network; CNN: Convolutional neural network; GISTs: Gastrointestinal stromal tumors; IPMN: Intraductal papillary mucinous neoplasm; EUS: Endoscopic ultrasonography; SVM: Support vector machine.