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.