Application of artificial intelligence in non-alcoholic fatty liver disease and viral hepatitis

Abstract

Non-alcoholic fatty liver disease (NAFLD) and chronic viral hepatitis are among the most significant causes of liver-related mortality worldwide. It is critical to develop reliable methods of predicting progression to fibrosis, cirrhosis, and decompensated liver disease. Current screening methods such as biopsy and transient elastography are limited by invasiveness and observer variation in analysis of data. Artificial intelligence (AI) provides a unique opportunity to more accurately diagnose NAFLD and viral hepatitis, and to identify patients at high risk for disease progression. We conducted a literature review of existing evidence for AI in NAFLD and viral hepatitis. Thirteen articles on AI in NAFLD and 14 on viral hepatitis were included in our analysis. We found that machine learning algorithms were comparable in accuracy to current methods for diagnosis and fibrosis prediction (MELD-Na score, liver biopsy, FIB-4 score, and biomarkers). They also reliably predicted hepatitis C treatment failure and hepatic encephalopathy, for which there are currently no established prediction tools. These studies show that AI could be a helpful adjunct to existing techniques for diagnosing, monitoring, and treating both NAFLD and viral hepatitis.

Keywords: Non-alcoholic fatty liver disease; Non-alcoholic steatohepatitis; Fatty liver; Artificial intelligences; Steatosis; Fibrosis; Machine learning

Core Tip: Non-alcoholic fatty liver disease (NAFLD) exists on a spectrum from simple hepatocyte steatosis to non-alcoholic steatohepatitis (NASH) with ballooning and fibrosis. Given the lack of efficient screening methods and high rate of asymptomatic disease, it is challenging to identify patients with NAFLD in its various stages. Although liver biopsy remains the gold standard for diagnosing NASH, it is an invasive, costly, and painful procedure. Conventional imaging modalities including ultrasound, computed tomography, magnetic resonance imaging and transient elastography are limited by inter- and intra-observer variability depending on the stage of fibrosis. Similarly, despite recent progress in the prevention and treatment of viral hepatitis, predicting sustained virological response and disease progression remains challenging. Artificial intelligence (AI) is an exciting and increasingly pertinent field in medicine as clinicians incorporate augmenting technology into their daily practice. This review summarizes recent literature on the application of AI in NAFLD and viral hepatitis. Specifically, the review will assess the performance of AI as a non-invasive method for the diagnosis and staging of liver fibrosis and steatosis, as well as for the detection and treatment of chronic viral hepatitis. It will also aim to highlight the potential for AI based methods on their ability to develop therapeutic targets.