Letter to the Editor
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Apr 28, 2022; 28(16): 1722-1724
Published online Apr 28, 2022. doi: 10.3748/wjg.v28.i16.1722
Comment on “Artificial intelligence in gastroenterology: A state-of-the-art review”
Thomas Bjørsum-Meyer, Anastasios Koulaouzidis, Gunnar Baatrup
Thomas Bjørsum-Meyer, Gunnar Baatrup, Department of Surgery, Odense University Hospital, Svendborg 5700, Denmark
Thomas Bjørsum-Meyer, Anastasios Koulaouzidis, Gunnar Baatrup, Department of Clinical Research, University of Southern Denmark, Faculty of Health Science, Odense 5230, Denmark
Author contributions: Bjørsum-Meyer T conceived of the idea for the manuscript and contributed to the manuscript drafting and critical revision; Koulaouzidis A and Baatrup G contributed to the manuscript drafting and critical revision.
Conflict-of-interest statement: AK is consultant for Jinshan. He is director of iCERV Ltd and cofounder of AJM Medicaps Ltd. He has received a GivenImaging Ltd-ESGE grant, and material support for clinical research from SynMed/Intromedic. In the last ten years, he has received honoraria & lecture fees from Jinshan, Dr FalkPharma UK and Ferring. He has also received educational travel support from Aquilant, Jinshan, Dr FalkPharma, Almirall, Ferring, and has participated in advisory board meetings for Tillots, Ankon, and Dr FalkPharmaUK.
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: Thomas Bjørsum-Meyer, MD, PhD, Associate Professor, Surgeon, Department of Surgery, Odense University Hospital, Baagøes Alle 15, Svendborg 5700, Denmark. thomas.bjoersum-meyer@rsyd.dk
Received: December 1, 2021
Peer-review started: December 1, 2021
First decision: December 26, 2021
Revised: January 7, 2022
Accepted: March 16, 2022
Article in press: March 16, 2022
Published online: April 28, 2022
Processing time: 143 Days and 23.3 Hours
Abstract

Colon capsule endoscopy (CCE) was introduced nearly two decades ago. Initially, it was limited by poor image quality and short battery time, but due to technical improvements, it has become an equal diagnostic alternative to optical colonoscopy (OC). Hastened by the coronavirus disease 2019 pandemic, CCE has been introduced in clinical practice to relieve overburdened endoscopy units and move investigations to out-patient clinics. A wider adoption of CCE would be bolstered by positive patient experience, as it offers a diagnostic investigation that is not inferior to other modalities. The shortcomings of CCE include its inability to differentiate adenomatous polyps from hyperplastic polyps. Solving this issue would improve the stratification of patients for polyp removal. Artificial intelligence (AI) has shown promising results in polyp detection and characterization to minimize incomplete CCEs and avoid needless examinations. Onboard AI appears to be a needed application to enable near-real-time decision-making in order to diminish patient waiting times and avoid superfluous subsequent OCs. With this letter, we discuss the potential and role of AI in CCE as a diagnostic tool for the large bowel.

Keywords: Video capsule endoscopy; Wireless capsule endoscopy; Artificial intelligence; Colonic polyps; Endoscopic surgical procedures; Colon neoplasm

Core Tip: Colon capsule endoscopy (CCE) generates a vast amount of image material-currently, this material must be assessed manually. Artificial intelligence (AI) as an adjunct to CCE has been reported as having high accuracy for detecting colonic lesions. Future studies need to evaluate AI algorithms for estimating the likelihood of neoplasia and predicting which patients are most likely to benefit from CCE. Onboard capsule intelligence has the potential to generate result reports immediately after completed examinations.