Editorial
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Mar 21, 2024; 30(11): 1494-1496
Published online Mar 21, 2024. doi: 10.3748/wjg.v30.i11.1494
Advancements in Barrett's esophagus detection: The role of artificial intelligence and its implications
Sara Massironi
Sara Massironi, Division of Gastroenterology and Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, Fondazione IRCCS San Gerardo dei Tintori, University of Milano-Bicocca, Monza 20900, Italy
Author contributions: Massironi S authored the commentary article, contributing her expertise and insights to analyze the research findings, contextualize their significance in the field of gastroenterology, and articulate the implications of AI-assisted detection in Barrett's esophagus for the broader medical community.
Conflict-of-interest statement: Massironi S declares that there are no conflicts of interest regarding the publication of this article, with no financial, consultative, institutional, or other relationships that might lead to bias or a 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: Sara Massironi, MD, PhD, Adjunct Professor, Division of Gastroenterology and Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, Fondazione IRCCS San Gerardo dei Tintori, University of Milano-Bicocca, Via Pergolesi 33, Monza 20900, Italy. sara.massironi@libero.it
Received: January 7, 2024
Peer-review started: January 7, 2024
First decision: January 23, 2024
Revised: January 27, 2024
Accepted: February 27, 2024
Article in press: February 27, 2024
Published online: March 21, 2024
Abstract

Artificial intelligence (AI) is making significant strides in revolutionizing the detection of Barrett's esophagus (BE), a precursor to esophageal adenocarcinoma. In the research article by Tsai et al, researchers utilized endoscopic images to train an AI model, challenging the traditional distinction between endoscopic and histological BE. This approach yielded remarkable results, with the AI system achieving an accuracy of 94.37%, sensitivity of 94.29%, and specificity of 94.44%. The study's extensive dataset enhances the AI model's practicality, offering valuable support to endoscopists by minimizing unnecessary biopsies. However, questions about the applicability to different endoscopic systems remain. The study underscores the potential of AI in BE detection while highlighting the need for further research to assess its adaptability to diverse clinical settings.

Keywords: Barrett's esophagus, Artificial intelligence, Endoscopic images, Artificial intelligence model, Early cancer detection, Endoscopy

Core Tip: The use of artificial intelligence (AI) to detect Barrett's esophagus (BE) is a groundbreaking advancement in the field of gastroenterology. This innovative approach, which employs endoscopic images for training AI models, challenges the conventional distinction between endoscopic and histological BE. The results show good promise, with the AI system achieving high accuracy, sensitivity, and specificity in BE detection. This development has the potential to reduce unnecessary biopsies and streamline the diagnostic process. However, the adaptability of AI to different endoscopic systems remains a critical consideration, warranting further research for widespread clinical implementation.