Application of Artificial Intelligence for Diagnosis and Risk Stratification in NAFLD and NASH: The State of the Art.

Journal: Hepatology (Baltimore, Md.)
Published Date:

Abstract

The diagnosis of nonalcoholic fatty liver disease and associated fibrosis is challenging given the lack of signs, symptoms and nonexistent diagnostic test. Furthermore, follow up and treatment decisions become complicated with a lack of a simple reproducible method to follow these patients longitudinally. Liver biopsy is the current standard to detect, risk stratify and monitor individuals with nonalcoholic fatty liver disease. However, this method is an unrealistic option in a population that affects about one in three to four individuals worldwide. There is an urgency to develop innovative methods to facilitate management at key points in an individual's journey with nonalcoholic fatty liver disease fibrosis. Artificial intelligence is an exciting field that has the potential to achieve this. In this review, we highlight applications of artificial intelligence by leveraging our current knowledge of nonalcoholic fatty liver disease to diagnose and risk stratify NASH phenotypes.

Authors

  • Amreen M Dinani
    Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, New York, NY.
  • Kris V Kowdley
    Liver Institute Northwest, Seattle, WA; Elson S. Floyd College of Medicine, Washington State University, WA.
  • Mazen Noureddin
    Karsh Division of Gastroenterology and Hepatology, Department of Medicine, and Comprehensive Transplant Center, Cedars-Sinai Medical Center, Los Angeles, CA.