Atsawarungruangkit A, Elfanagely Y, Asombang AW, Rupawala A, Rich HG. Understanding deep learning in capsule endoscopy: Can artificial intelligence enhance clinical practice? Artif Intell Gastrointest Endosc 2020; 1(2): 33-43 [DOI: 10.37126/aige.v1.i2.33]
Corresponding Author of This Article
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. amporn_atsawarungruangkit@brown.edu
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 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: http://creativecommons.org/Licenses/by-nc/4.0/
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. amporn_atsawarungruangkit@brown.edu
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 Processing time: 36 Days and 8.4 Hours
Abstract
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.
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.
