Role of artificial intelligence in the detection, assessment and outcome of gastroesophageal varices

Abstract

Gastroesophageal varices (GEVs) are one of the first clinically relevant consequences of PH, developing in 60%-80% of patients with liver cirrhosis. They are directly associated with a higher risk of decompensation and death. Screening endoscopy is the most common screening strategy in patients with cirrhosis. However, there is a tendency to find non-invasive predictors of GEVs to avoid costly and potentially harmful procedures safely. Artificial intelligence (AI)-driven predictive models effectively integrate diverse clinical, imaging, and laboratory data to provide non-invasive and precise risk stratification, reducing the reliance on endoscopic evaluations. Deep learning applications, particularly convolutional neural networks (CNNs), have proved highly effective in analyzing endoscopic images, thereby enhancing diagnostic accuracy beyond traditional visual inspection. Additionally, radiomics-based AI models utilizing computed tomography (CT) and elastography have enabled non-invasive risk assessment, improving predictions of bleeding risk and estimations of the hepatic venous pressure gradient (HVPG). Ethical considerations, such as data privacy and algorithmic bias, also require careful management. Future research should focus on prospective validation, real-world application studies, and the development of standardized AI frameworks to ensure the clinical applicability of these methods. AI-driven precision medicine has the potential to revolutionize the management of GEVs, offering more efficient, accurate, and individualized patient care while optimizing healthcare resource utilization.

Keywords

Artificial intelligence / esophageal varices / gastric varices / gastroesophageal varices / machine learning / deep learning / radiomics

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