Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastrointest Endosc. Oct 28, 2020; 1(2): 33-43
Published online Oct 28, 2020. doi: 10.37126/aige.v1.i2.33
Understanding deep learning in capsule endoscopy: Can artificial intelligence enhance clinical practice?
Amporn Atsawarungruangkit, Yousef Elfanagely, Akwi W Asombang, Abbas Rupawala, Harlan G Rich
Amporn Atsawarungruangkit, Akwi W Asombang, Abbas Rupawala, Harlan G Rich, Division of Gastroenterology, Warren Alpert School of Medicine, Brown University, Providence, RI 02903, United States
Yousef Elfanagely, Department of Internal Medicine, Brown University, Providence, RI 02903, United States
Author contributions: Atsawarungruangkit A and Elfanagely Y contributed equally to this work including literature review, study selection, and manuscript writing; Asombang AW, Rupawala A, and Rich HG critically revised the manuscript and provided supervision.
Conflict-of-interest statement: The authors declare no conflict of interests.
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: Amporn Atsawarungruangkit, MD, Academic Fellow, Division of Gastroenterology, Warren Alpert School of Medicine, Brown University, 593 Eddy Street, POB 240, Providence, RI 02903, United States.
Received: September 21, 2020
Peer-review started: September 21, 2020
First decision: September 25, 2020
Revised: October 1, 2020
Accepted: October 13, 2020
Article in press: October 13, 2020
Published online: October 28, 2020

Wireless capsule endoscopy (WCE) enables physicians to examine the gastrointestinal tract by transmitting images wirelessly from a disposable capsule to a data recorder. Although WCE is the least invasive endoscopy technique for diagnosing gastrointestinal disorders, interpreting a WCE study requires significant time effort and training. Analysis of images by artificial intelligence, through advances such as machine or deep learning, has been increasingly applied to medical imaging. There has been substantial interest in using deep learning to detect various gastrointestinal disorders based on WCE images. This article discusses basic knowledge of deep learning, applications of deep learning in WCE, and the implementation of deep learning model in a clinical setting. We anticipate continued research investigating the use of deep learning in interpreting WCE studies to generate predictive algorithms and aid in the diagnosis of gastrointestinal disorders.

Keywords: Capsule endoscopy, Deep learning, Machine learning, Wireless capsule endoscopy, Small bowel capsule, Video capsule endoscopy

Core Tip: Wireless capsule endoscopy is the least invasive endoscopy technique for investigating the gastrointestinal tract. However, it takes a significant amount of time for interpreting the results. Deep learning has been increasingly applied to interpret capsule endoscopy images. We have summarized deep learning’s framework, various characteristics in published literature, and application in the clinical setting.