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Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. May 28, 2021; 27(20): 2545-2575
Published online May 28, 2021. doi: 10.3748/wjg.v27.i20.2545
State of machine and deep learning in histopathological applications in digestive diseases
Soma Kobayashi, Joel H Saltz, Vincent W Yang
Soma Kobayashi, Joel H Saltz, Department of Biomedical Informatics, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, United States
Vincent W Yang, Department of Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, United States
Vincent W Yang, Department of Physiology and Biophysics, Renaissance School of Medicine, Stony Brook University, Stony Brook , NY 11794, United States
Author contributions: Kobayashi S organized and drafted the manuscript; Saltz JH and Yang VW reviewed and performed critical revisions of the manuscript.
Supported by National Institutes of Health, No. GM008444 (to Kobayashi S), No. CA225021 (to Saltz JH), and No. DK052230 (to Yang VW).
Conflict-of-interest statement: The authors have no 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: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Vincent W Yang, MD, PhD, Chairman, Full Professor, Department of Medicine, Renaissance School of Medicine, Stony Brook University, HSC T-16, Rm 040, 101 Nicolls Road, Stony Brook, NY 11794, United States. vincent.yang@stonybrookmedicine.edu
Received: January 28, 2021
Peer-review started: January 28, 2021
First decision: February 24, 2021
Revised: March 27, 2021
Accepted: April 29, 2021
Article in press: April 29, 2021
Published online: May 28, 2021
Core Tip

Core Tip: Machine learning- and deep learning-based imaging approaches have been increasingly applied to histopathological slides and hold much potential in areas spanning diagnosis, disease grading and characterizations, academic research, and clinical decision support mechanisms. As these studies have entered into translational applications, tracking the current state of these methodologies and the clinical areas in which impact is most likely is of high importance. This review will thus provide a background of major concepts and terminologies while highlighting emerging literature regarding histopathological applications of these techniques and challenges and opportunities moving forward.