Ezzat WM. Machine learning as an artificial intelligence application in management of chronic hepatitis B virus infection. World J Gastroenterol 2025; 31(35): 109776 [PMID: 41024767 DOI: 10.3748/wjg.v31.i35.109776]
Corresponding Author of This Article
Wafaa Mohamed Ezzat, MD, Professor, Department of Internal Medicine, Medical Research and Clinical Studies Institute, National Research Center, El Buhoth Street, Cairo, Giza 12311, Egypt. wafaa_3t@yahoo.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Letter to the Editor
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/
World J Gastroenterol. Sep 21, 2025; 31(35): 109776 Published online Sep 21, 2025. doi: 10.3748/wjg.v31.i35.109776
Machine learning as an artificial intelligence application in management of chronic hepatitis B virus infection
Wafaa Mohamed Ezzat
Wafaa Mohamed Ezzat, Department of Internal Medicine, Medical Research and Clinical Studies Institute, National Research Center, Giza 12311, Egypt
Author contributions: Ezzat WM designed all activities in this manuscript, and approved the final manuscript publication.
Conflict-of-interest statement: The author reported no relevant conflicts of interest for this article.
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: Wafaa Mohamed Ezzat, MD, Professor, Department of Internal Medicine, Medical Research and Clinical Studies Institute, National Research Center, El Buhoth Street, Cairo, Giza 12311, Egypt. wafaa_3t@yahoo.com
Received: May 21, 2025 Revised: June 19, 2025 Accepted: September 1, 2025 Published online: September 21, 2025 Processing time: 120 Days and 13.2 Hours
Core Tip
Core Tip: There is substantial evidence indicating that stratification based on the gut microbiome could facilitate personalized interventions aimed at enhancing human health. It became essential to characterize the microbial ecosystems, resulting in a surge of various types of molecular profiling data, including metagenomics, metatranscriptomics, and metabolomics. In the analysis of such data, machine learning algorithms have proven to be effective in identifying crucial molecular signatures, uncovering potential patient stratifications, and especially in creating models that can reliably predict phenotypes. Machine learning may be supervised, unsupervised, semi-supervised or reinforcement type. Using a method for explaining individual classifier decisions for complex microbiota analysis may help in developing personalized treatment.
