Automated Detection of Non‐Alcoholic Fatty Liver Disease Using Histopathological Images: A Systematic Review

Abstract

The global rise in fatty liver diseases is alarming. Traditional diagnostic methods include ultrasound, CT scans, MRI, and liver biopsies, the latter being the gold standard for diagnosis and treatment. Recent advancements in artificial intelligence (AI) have enhanced liver biopsy accuracy, improving treatment outcomes. This study investigates how various AI techniques aid histopathologists, gastroenterologists, and liver specialists in diagnosing and assessing liver damage due to abnormal fat accumulation. We conducted a systematic review of AI applications in evaluating fatty liver diseases, particularly through histopathological image analysis. Our search encompassed five scientific databases: PubMed Central, ACM Digital Library, IEEE Xplore, Scopus, and Google Scholar. We focused on peer‐reviewed articles, conference papers, theses, and book chapters, adhering to specific terminology. The data synthesis followed the PRISMA guidelines, comparing literature based on four key indices and their annual distribution. We evaluated 37 studies utilizing histopathological imaging for the diagnosis of non‐alcoholic fatty liver disease and non‐alcoholic steatohepatitis, including related conditions, metabolic dysfunction‐associated fatty liver disease and metabolic dysfunction‐associated steatohepatitis. The review summarized the performance of various algorithms and explored the distribution of machine learning efforts. Given the complexity of histopathological images, AI algorithms can effectively stratify liver samples affected by fat. Our findings indicate that AI's diagnostic performance closely matches traditional pathological interpretations, offering reliable results for clinical applications.This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Technologies > Artificial Intelligence