Artificial Intelligence for Detecting Non-alcoholic Steatohepatitis (NASH) - Gifari, Muhammad Wildan; Ramadhani, Yogi; Kurniawan, Dhadhang Wahyu (UI Scholars Hub, 2025)
Non-alcoholic steatohepatitis (NASH), an inflammatory disease of the liver, has recently raised concern among healthcare professionals worldwide due to its asymptomatic features, making early diagnosis challenging. If left unnoticed, NASH often progresses to lethal diseases such as liver fibrosis or hepatocellular carcinoma. Recent developments in the field of artificial intelligence (AI) might facilitate the early diagnosis of NASH in a more efficient manner, forming a promising strategy to diagnose patients. In simple terms, AI is any machine that is capable of human-level intelligence, including visual perception, speech recognition, or decision making. A subclass of AI, which particularly deals with knowledge-based systems to find a relationship between different datasets, is called machine learning (ML). ML is based on the capability of a system to define or learn a relationship between the input and output data and then apply the learned relationship to any future datasets with a similar structure. The capability to maintain and analyze large datasets and aid in the prediction of outcomes makes ML particularly interesting for the application in NASH by, for instance, analyzing image data from patients, using biomarkers to predict clinical disease progression or by determining the efficacy of applied therapeutics. In this review, we will highlight the recent developments in the AI-based diagnosis and treatment of liver diseases. First, we provide a brief introduction to AI and ML before generalizing the use of AI in the diagnosis and treatment of different liver diseases. Then, we will specifically elaborate on the use of AI in the detection of NASH and its precursor, non-alcoholic fatty liver disease (NAFLD), focusing on the prediction and diagnosis of NASH and NAFLD as well as on the automation of imaging processes. Finally, we will highlight the clinical importance of AI in the detection of NASH before concluding with the future challenges for the application of AI in the field of NASH detection and treatment.