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

Table 7 Artificial intelligence applications in hepatology: Treatment

Ref.	Parameters employed	AI classifier	Sizes of the training/validation sets	Outcomes	Performance
Wübbolding et al[133]	Analyze soluble immune markers	Several	28/497 HBV patients	Prediction of early virological relapse	0.73-0.892,6, 0.59-0.672,7
Haga et al[134]	WGS of HCV	Several	86/87 HCV patients	Classification of HCV variants resistant to antiviral drugs	0.5-0.9372,5, 0.597-0.9542,6
Bedon et al[135]	DNA methylation profiling	RF-based	300/74 HCC specimens	6-mo progression-free survival	67.1-80.61,5, 64.8-80.21,7
Tsilimigras et al[137]	Laboratory results, clinicopathological parameters, tumor characteristics	CART	976 HCC patients	Determining factors of prognostic weigh preoperatively within the BCLC staging system	---
Tsilimigras et al[139]	Laboratory results, clinicopathological parameters, tumor characteristics	CART	1146 CCA patients	Determining factors of prognostic weigh preoperatively	---
Jeong et al[140]	Laboratory results, clinicopathological parameters	DNN	1421/2347	Intrahepatic CCA susceptible to adjuvant therapy following resection	0.842,5, 0.782,7
Shao et al[141]	Clinicopathological parameters	ANN	288 CCA patients (training:validation = 8:2)	Predict early occlusion following bilateral plastic stent placement	0.96482,5, 0.95442,6

¹Accuracy (%).

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

³Sensitivity (%).

⁴Specificity (%).

⁵Training.

⁶Internal validation.

⁷External validation/testing. ANN: Artificial neural network; BCLC: Barcelona clinic liver cancer; CART: Classification and regression tree; CCA: Cholangiocarcinoma; DNN: Deep neural network; HCC: Hepatocellular carcinoma; HCV: Hepatitis C; RF: Random forest; WGS: Whole-genome sequencing.