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

Table 1 Artificial intelligence applications in gastroenterology: Prevention

Ref.	Parameters employed	AI classifier	Sizes of the training/validation sets	Outcomes	Performance
Leung et al[27]	Laboratory results, clinicopathological parameters	Several	64238/25330 patients	Risk of gastric cancer development following H.pylori eradication	0.53-0.972,6, 59.3-98.13,6, 51.5-93.64,6
Nakahira et al[28]	Laboratory results, clinicopathological parameters, endoscopic images	CNN	7826/454 patients	Stratify risk of gastric cancer development	---
Taninaga et al[29]	Laboratory results, clinicopathological parameters, endoscopic images	CART	1144/287	Prediction of future gastric cancer	63.4-94.81,6, 0.736-0.8742,6
Goshen et al[31]	Laboratory results, clinicopathological parameters	DT, RF, GB	688 flagged patients	High risk of CRC development	----

¹Accuracy (%).

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

³Sensitivity (%).

⁴Specificity (%).

⁵Training.

⁶Internal validation.

⁷External validation/testing. CART: Classification and regression tree; CNN: Convolutional neural network; CRC: Colorectal cancer; DT: Decision tree; GB: Gradient boosting; RF: Random forest.