Improving the radiological diagnosis of hepatic artery thrombosis after liver transplantation: Current approaches and future challenges

Abstract

Hepatic artery thrombosis (HAT) is a devastating vascular complication following liver transplantation, requiring prompt diagnosis and rapid revascularization treatment to prevent graft loss. At present, imaging modalities such as ultrasound, computed tomography, and magnetic resonance play crucial roles in diagnosing HAT. Although imaging techniques have improved sensitivity and specificity for HAT diagnosis, they have limitations that hinder the timely diagnosis of this complication. In this sense, the emergence of artificial intelligence (AI) presents a transformative opportunity to address these diagnostic limitations. The development of machine learning algorithms and deep neural networks has demonstrated the potential to enhance the precision diagnosis of liver transplant complications, enabling quicker and more accurate detection of HAT. This article examines the current landscape of imaging diagnostic techniques for HAT and explores the emerging role of AI in addressing future challenges in the diagnosis of HAT after liver transplant.

Keywords: Liver transplantation, Postoperative complications, Hepatic artery, Thrombosis, Radiology, Artificial intelligence

Core Tip: Hepatic artery thrombosis (HAT) is a severe vascular complication after liver transplant requiring prompt diagnosis and intervention to prevent graft loss and patient death. However, current imaging methods have limitations. Artificial intelligence (AI), especially deep learning, holds promising potential to enhance precise and accurate HAT diagnosis. This article explores current HAT imaging techniques and highlights the potential role of AI-based methods, aiming to improve diagnostic performance and recipient survival.