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:
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.
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

Machine learning (ML)- and deep learning (DL)-based imaging modalities have exhibited the capacity to handle extremely high dimensional data for a number of computer vision tasks. While these approaches have been applied to numerous data types, this capacity can be especially leveraged by application on histopathological images, which capture cellular and structural features with their high-resolution, microscopic perspectives. Already, these methodologies have demonstrated promising performance in a variety of applications like disease classification, cancer grading, structure and cellular localizations, and prognostic predictions. A wide range of pathologies requiring histopathological evaluation exist in gastroenterology and hepatology, indicating these as disciplines highly targetable for integration of these technologies. Gastroenterologists have also already been primed to consider the impact of these algorithms, as development of real-time endoscopic video analysis software has been an active and popular field of research. This heightened clinical awareness will likely be important for future integration of these methods and to drive interdisciplinary collaborations on emerging studies. To provide an overview on the application of these methodologies for gastrointestinal and hepatological histopathological slides, this review will discuss general ML and DL concepts, introduce recent and emerging literature using these methods, and cover challenges moving forward to further advance the field.

Keywords: Artificial intelligence, Machine learning, Deep learning, Gastroenterology, Hepatology, Histopathology

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.