Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastrointest Endosc. Oct 28, 2020; 1(2): 28-32
Published online Oct 28, 2020. doi: 10.37126/aige.v1.i2.28
Artificial intelligence in Barrett’s esophagus: A renaissance but not a reformation
Karen Chang, Christian S Jackson, Kenneth J Vega
Karen Chang, Department of Internal Medicine, University of California, Riverside School of Medicine, Riverside, CA 92521, United States
Christian S Jackson, Gastroenterology Section, VA Loma Linda Healthcare Syst, Loma Linda, CA 92357, United States
Kenneth J Vega, Division of Gastroenterology and Hepatology, Department of Medicine, Augusta University-Medical College of Georgia, Augusta, GA 30912, United States
Author contributions: All authors planned the editorial and reviewed all data included; all authors wrote the editorial and revised it for intellectual content; and approved the final submitted version; Vega KJ is the editorial guarantor.
Conflict-of-interest statement: All authors have no conflict of interests to declare.
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: Kenneth J Vega, MD, Professor, Division of Gastroenterology and Hepatology, Department of Medicine, Augusta University-Medical College of Georgia, 1120 15th Street, AD 2226, Augusta, GA 30912, United States. kvega@augusta.edu
Received: October 10, 2020
Peer-review started: October 10, 2020
First decision: October 22, 2020
Revised: October 26, 2020
Accepted: October 27, 2020
Article in press: October 27, 2020
Published online: October 28, 2020

Esophageal cancer remains as one of the top ten causes of cancer-related death in the United States. The primary risk factor for esophageal adenocarcinoma is the presence of Barrett’s esophagus (BE). Currently, identification of early dysplasia in BE patients requires an experienced endoscopist performing a diagnostic endoscopy with random 4-quadrant biopsies taken every 1-2 cm using appropriate surveillance intervals. Currently, there is significant difficulty for endoscopists to distinguish different forms of dysplastic BE as well as early adenocarcinoma due to subtleties in mucosal texture and color. This obstacle makes taking multiple random biopsies necessary for appropriate surveillance and diagnosis. Recent advances in artificial intelligence (AI) can assist gastroenterologists in identifying areas of likely dysplasia within identified BE and perform targeted biopsies, thus decreasing procedure time, sedation time, and risk to the patient along with maximizing potential biopsy yield. Though using AI represents an exciting frontier in endoscopic medicine, recent studies are limited by selection bias, generalizability, and lack of robustness for universal use. Before AI can be reliably employed for BE in the future, these issues need to be fully addressed and tested in prospective, randomized trials. Only after that is achieved, will the benefit of AI in those with BE be fully realized.

Keywords: Barrett's esophagus, Artificial intelligence, Machine learning, Cognitive neural networks, Computer aided diagnosis, Endoscopy

Core Tip: Screening and surveillance in patients with Barrett’s esophagus (BE) remain problematic in regards to accuracy and adherence. This occurs in spite of recom-mendations and advances in endoscopic imaging. Artificial intelligence (AI) algorithms assist in endoscopic evaluation of BE by identifying potential targets for biopsy. This may occur by increasing endoscopic efficiency and diagnosing accuracy by decreasing procedure time. AI in BE has been developed by expert endoscopists and appear to perform similarly among them. At this point, the benefit of AI in BE may be for use by non-expert endoscopists and trainees to maximize BE endoscopic evaluation.