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.