Yoo BS, Houston KV, D'Souza SM, Elmahdi A, Davis I, Vilela A, Parekh PJ, Johnson DA. Advances and horizons for artificial intelligence of endoscopic screening and surveillance of gastric and esophageal disease. Artif Intell Med Imaging 2022; 3(3): 70-86 [DOI: 10.35711/aimi.v3.i3.70]
Corresponding Author of This Article
David A Johnson, MD, MACG, FASGE, MACP, Division of Gastroenterology, Department of Internal Medicine, Eastern Virginia Medical School, 886 Kempsville Road Suite 114, Norfolk, VA 23507, United States. dajevms@aol.com
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/
Artif Intell Med Imaging. Jun 28, 2022; 3(3): 70-86 Published online Jun 28, 2022. doi: 10.35711/aimi.v3.i3.70
Advances and horizons for artificial intelligence of endoscopic screening and surveillance of gastric and esophageal disease
Byung Soo Yoo, Kevin V Houston, Steve M D'Souza, Alsiddig Elmahdi, Isaac Davis, Ana Vilela, Parth J Parekh, David A Johnson
Byung Soo Yoo, Steve M D'Souza, Alsiddig Elmahdi, Isaac Davis, Ana Vilela, Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
Kevin V Houston, Department of Internal Medicine, Virginia Commonwealth University, Richmond, VA 23298, United States
Parth J Parekh, David A Johnson, Division of Gastroenterology, Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
Author contributions: Johnson DA, Parekh PJ, and Yoo BS all contributed to the construction of the project; all authors wrote and edited the manuscript.
Conflict-of-interest statement: All the authors declare that they have no conflict of interest.
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: David A Johnson, MD, MACG, FASGE, MACP, Division of Gastroenterology, Department of Internal Medicine, Eastern Virginia Medical School, 886 Kempsville Road Suite 114, Norfolk, VA 23507, United States. dajevms@aol.com
Received: January 28, 2022 Peer-review started: January 28, 2022 First decision: March 28, 2022 Revised: May 18, 2022 Accepted: June 20, 2022 Article in press: June 20, 2022 Published online: June 28, 2022 Processing time: 150 Days and 22.6 Hours
Abstract
The development of artificial intelligence in endoscopic assessment of the gastrointestinal tract has shown progressive enhancement in diagnostic acuity. This review discusses the expanding applications for gastric and esophageal diseases. The gastric section covers the utility of AI in detecting and characterizing gastric polyps and further explores prevention, detection, and classification of gastric cancer. The esophageal discussion highlights applications for use in screening and surveillance in Barrett's esophagus and in high-risk conditions for esophageal squamous cell carcinoma. Additionally, these discussions highlight applications for use in assessing eosinophilic esophagitis and future potential in assessing esophageal microbiome changes.
Core Tip: The application of artificial intelligence (AI) in gastroenterology has demonstrated broad utility in esophageal and gastric disease diagnosis and management. The current data shows that AI can be used for gastric polyp and cancer detection and characterization as well as screening and surveillance for esophageal cancer and its high-risk conditions such as Barrett’s esophagus. The AI systems can also apply in conditions such as achalasia, post-caustic esophageal injuries, and eosinophilic esophagitis.
