The Role of Artificial Intelligence, Including Endoscopic Diagnosis, in the Prediction of Presence, Bleeding, and Mortality of Esophageal Varices.

Abstract

Esophagogastric varices (EGVs) are a disease that occurs as a complication of the progression of liver cirrhosis, and since bleeding can be fatal, regular endoscopy is necessary. With the development of artificial intelligence (AI) in recent years, it is beginning to be applied to predicting the presence of EGVs, predicting bleeding, and making a diagnosis and prognosis. Based on previous reports, application methods of AI can be classified into the following four categories: (1) noninvasive prediction using clinical data obtained from clinical records such as laboratory data, past history, and present illness, (2) invasive detection and prediction using endoscopy and computed tomography (CT), (3) invasive prediction using multimodal AI (clinical data and endoscopy), (4) invasive virtual measurement on the image of endoscopy and CT. These methods currently allow for the use of AI in the following ways: (1) prediction of EGVs existence, variceal grade, bleeding risk, and survival rate, (2) detection and diagnosis of esophageal varices (EVs), (3) prediction of bleeding within 1 year, (4) prediction of variceal diameter and portal pressure gradient. This review explores current studies on AI applications in assessing EGVs, highlighting their benefits, limitations, and future directions.