Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

Table 5 Artificial intelligence applications in hepatology: Prevention

Ref.	Parameters employed	AI classifier	Sizes of the training/validation sets	Outcomes	Performance
Goldman et al[108]	National database of routine annual health check-ups	DT-based	12019 patients	Risk of NAFLD and cirrhosis	84.50-85.731,6, 0.7740-0.84862,6
Yip et al[109]	Laboratory results, clinicopathological parameters	Several	500/422	Identify patients with NAFLD	0.87-0.902,5, 0.78-0.882,6, 55.48-94.523,5, 51.69-92.373,6, 58.47-91.534,5, 50.99-90.464,6
Ma et al[110]	Laboratory results, clinicopathological parameters	Several	10508 patients (training:validation = 9:1)	Identify patients with NAFLD	49.47-82.921,6, 20.2-68.03,6, 54.4-94.64,6
Sowa et al[111]	Laboratory results, clinicopathological parameters	Several	126 morbidly obese patients (training:validation = 9:1)	Fibrosis in NAFLD patients	791,6, 30.8-60.03,6, 77.0-92.24,6
Canbay et al[112]	Laboratory results, clinicopathological parameters	EFS	164/122 obese patients	Classification of NAFLD and NASH	0.73392,5, 0.70282,6
Fialoke et al[113]	National database of routine annual health check-ups	Several	108139 patients (training:validation = 4:1)	Classification among healthy, NAFLD, and NASH	77.2-79.71,6, 0.842-0.8762,6, 74.5-77.43,6
Sowa et al[114]	Data from biochemical and enzyme-linked immunosorbent assays	Several	133 patients (training:validation = 9:1)	Classification of NAFLD and ALD	DT: 89.02-95.11,6, 74.19-94.123,6, 96.08-98.044,6, RF: 0.8932-0.98462,6, SVM: 0.9058-0.91182,6
Wei et al[115]	Laboratory results, clinicopathological parameters	GB	576 HBV patients, (training:validation = 7:3), 3687 HCV patients	Classification of fibrosis/cirrhosis in HBV patients	0.904-0.9742,5, 0.871-0.9182,6, 79-883,5, 78-843,6, 86-924,5, 854,6
				Classification of fibrosis/cirrhosis in HCV patients	0.797-0.8492,7
Wang et al[116]	Laboratory results, clinicopathological parameters	ANN	226/1136/1167 HBV patients	Classification of significant fibrosis	0.8832,5, 0.8842,6, 0.9202,7
Raoufy et al[117]	Laboratory results, clinicopathological parameters	ANN	86/58 HBV patients	Classification of liver cirrhosis	91.381,6, 0.8982,6, 87.53,6, 924,6
Piscaglia et al[118]	Laboratory results, clinicopathological parameters	ANN	414/96 HCV patients	Classification of significant fibrosis	45.8-86.51,6, 0.872,5, 0.932,6, 30.4-1003,6, 30.1-98.64,6
Hashem et al[119]	Laboratory results, clinicopathological parameters	Several	22690/16877 HCV patients	Classification of significant fibrosis	66.3-84.41,6, 0.73-0.762,6
Ioannou et al[120]	Clinical/laboratory data extracted directly from electronic health records	DNN	48151 patients with HCV-related cirrhosis (training:validation = 9:1)	HCC development in HCV cirrhosis	0.759-0.8062,6
Emu et al[121]	Laboratory results, clinicopathological parameters	Several	1385 patients HCV (training:validation = 4:1)	Stage of liver cirrhosis	97.228-97.8311,6

¹Accuracy (%).

²Area under the receiver operating curve or c-index.

³Sensitivity (%).

⁴Specificity (%).

⁵Training.

⁶Internal validation.

⁷External validation/testing. ALD: Alcoholic liver disease; ANN: Artificial neural network; DNN: Deep neural network; DT: Decision tree; EFS: Ensemble feature selection; GB: Gradient boosting; HBV: Hepatitis B; HCC: Hepatocellular carcinoma; HCV: Hepatitis C; NAFLD: Non-alcoholic fatty liver disease; NASH: Non-alcoholic steatohepatitis RF: Random forest; SVM: Support vector machine.