Publication Name	Artificial Intelligence in Gastroenterology
Manuscript ID	74646
Country	United States

Received	2021-12-31 01:26
Peer-Review Started	2021-12-31 01:27
To Make the First Decision
Return for Revision	2022-02-07 03:11
Revised	2022-02-18 18:19
Second Decision	2022-04-28 03:28
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2022-04-28 04:17
Articles in Press	2022-04-28 04:17
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2022-04-28 11:10
Publish the Manuscript Online	2022-04-29 11:12

ISSN	2644-3236 (online)
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: http://creativecommons.org/licenses/by-nc/4.0/
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Category	Gastroenterology & Hepatology
Manuscript Type	Minireviews
Article Title	Application of artificial intelligence in non-alcoholic fatty liver disease and viral hepatitis
Manuscript Source	Invited Manuscript
All Author List	Atchayaa Gunasekharan, Joanna Jiang, Ashley Nickerson, Sajid Jalil and Khalid Mumtaz
Funding Agency and Grant Number
Corresponding Author	Khalid Mumtaz, MBBS, MSc, Associate Professor, Medicine, Ohio State University, 395 W. 12th Avenue, Columbus, Ohio 43210, United States. khalid.mumtaz@osumc.edu
Key Words	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.
Publish Date	2022-04-29 11:12
Citation	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
URL	https://www.wjgnet.com/2644-3236/full/v3/i2/46.htm
DOI	https://dx.doi.org/10.35712/aig.v3.i2.46

Full Article (PDF)	AIG-3-46.pdf
Full Article (Word)	AIG-3-46.docx
Manuscript File	74646_Auto_Edited-LJH.docx
Answering Reviewers	74646-Answering reviewers.pdf
Audio Core Tip	74646-Audio core tip.mp3
Conflict-of-Interest Disclosure Form	74646-Conflict-of-interest statement.pdf
Copyright License Agreement	74646-Copyright license agreement.pdf
Peer-review Report	74646-Peer-review(s).pdf
Scientific Misconduct Check	74646-Bing-Liu-JH-2.jpg
Scientific Misconduct Check	74646-CrossCheck.jpg
Scientific Editor Work List	74646-Scientific editor work list.pdf