Gunasekharan A, Jiang J, Nickerson A, Jalil S, Mumtaz K. Application of artificial intelligence in non-alcoholic fatty liver disease and viral hepatitis. Artif Intell Gastroenterol 2022; 3(2): 46-53 [DOI: 10.35712/aig.v3.i2.46]
Corresponding Author of This Article
Khalid Mumtaz, MBBS, MSc, Associate Professor, Department of Medicine, Ohio State University, 395 W. 12th Avenue, Columbus, OH 43210, United States. khalid.mumtaz@osumc.edu
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Atchayaa Gunasekharan, Department of Medicine, Jewish Hospital, Cincinatti, OH 43201, United States
Joanna Jiang, Ashley Nickerson, Sajid Jalil, Khalid Mumtaz, Department of Medicine, Ohio State University, Columbus, OH 43210, United States
Author contributions: Gunasekharan A analyzed articles, wrote and reviewed manuscript; Jiang J analyzed articles relating to viral hepatitis, wrote and reviewed those portions; Nickerson A reviewed manuscript; Jalil S reviewed manuscript; Mumtaz K helped with layout of manuscript, analyzed articles, wrote and revised manuscript.
Conflict-of-interest statement: None of the authors have any conflicts of interest to report.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Khalid Mumtaz, MBBS, MSc, Associate Professor, Department of Medicine, Ohio State University, 395 W. 12th Avenue, Columbus, OH 43210, United States. khalid.mumtaz@osumc.edu
Received: December 31, 2021 Peer-review started: December 31, 2021 First decision: February 7, 2022 Revised: February 18, 2022 Accepted: April 28, 2022 Article in press: April 28, 2022 Published online: April 28, 2022 Processing time: 119 Days and 9.8 Hours
Core Tip
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.
