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10/28/2020 4:29:58 AM | Browse: 527 | Download: 720
Publication Name Artificial Intelligence in Gastrointestinal Endoscopy
Manuscript ID 59667
Country/Territory United States
Received
2020-09-21 20:06
Peer-Review Started
2020-09-21 20:06
To Make the First Decision
Return for Revision
2020-09-25 01:09
Revised
2020-10-01 02:35
Second Decision
2020-10-12 07:03
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief
2020-10-13 04:15
Articles in Press
2020-10-13 04:15
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript
2020-10-28 00:02
Publish the Manuscript Online
2020-10-28 04:29
ISSN 2689-7164 (online)
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/
Copyright © The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
Article Reprints For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
Permissions For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website http://www.wjgnet.com
Category Gastroenterology & Hepatology
Manuscript Type Minireviews
Article Title Understanding deep learning in capsule endoscopy: Can artificial intelligence enhance clinical practice?
Manuscript Source Unsolicited Manuscript
All Author List Amporn Atsawarungruangkit, Yousef Elfanagely, Akwi W Asombang, Abbas Rupawala and Harlan G Rich
ORCID
Author(s) ORCID Number
Amporn Atsawarungruangkit http://orcid.org/0000-0003-0622-6839
Yousef Elfanagely http://orcid.org/0000-0002-3056-3811
Akwi W Asombang http://orcid.org/0000-0002-8658-6772
Abbas Rupawala http://orcid.org/0000-0001-5669-0686
Harlan G Rich http://orcid.org/0000-0003-3538-7631
Funding Agency and Grant Number
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
Key Words 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.
Publish Date 2020-10-28 04:29
Citation 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
URL https://www.wjgnet.com/2689-7164/full/v1/i2/33.htm
DOI https://dx.doi.org/10.37126/aige.v1.i2.33
Full Article (PDF) AIGE-1-33.pdf
Full Article (Word) AIGE-1-33.docx
Manuscript File 59667_Auto_Edited.docx
Answering Reviewers 59667-Answering reviewers.pdf
Audio Core Tip 59667-Audio core tip.mp3
Conflict-of-Interest Disclosure Form 59667-Conflict-of-interest statement.pdf
Copyright License Agreement 59667-Copyright license agreement.pdf
Peer-review Report 59667-Peer-review(s).pdf
Scientific Misconduct Check 59667-Scientific misconduct check.pdf
Scientific Editor Work List 59667-Scientific editor work list.pdf