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12/7/2018 3:57:11 AM | Browse: 894 | Download: 1228
Publication Name World Journal of Gastroenterology
Manuscript ID 41991
Country/Territory Norway
Received
2018-09-07 01:44
Peer-Review Started
2018-09-07 05:47
To Make the First Decision
2018-10-08 03:07
Return for Revision
2018-10-08 09:09
Revised
2018-10-25 18:56
Second Decision
2018-11-02 09:10
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief
2018-11-02 16:57
Articles in Press
2018-11-02 16:57
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript
2018-12-06 03:21
Publish the Manuscript Online
2018-12-07 03:57
ISSN 1007-9327 (print) and 2219-2840 (online)
Open Access This 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 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/
Copyright © The Author(s) 2018. 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 Editorial
Article Title Methodology to develop machine learning algorithms to improve performance in gastrointestinal endoscopy
Manuscript Source Invited Manuscript
All Author List Thomas de Lange, Pål Halvorsen and Michael Riegler
ORCID
Author(s) ORCID Number
Thomas de Lange http://orcid.org/0000-0003-3989-7487
Pål Halvorsen http://orcid.org/0000-0003-2073-7029
Michael Riegler http://orcid.org/0000-0002-3153-2064
Funding Agency and Grant Number
Funding Agency Grant Number
Norwegian Research Council 282315
Corresponding Author Thomas de Lange, MD, PhD, Associate Professor, Department of transplantation, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Postboks 4950 Nydalen, Oslo 0424, Norway. t.d.lange@medisin.uio.no
Key Words Endoscopy; Artificial intelligence; Deep learning; Computer assisted diagnosis; Gastrointestinal
Core Tip Assisted diagnosis using artificial intelligence and recent developments in computer hardware have enabled the narrower area of machine learning to equip the endoscopists with potentially powerful tools for computer assisted diagnosis systems. The success depends on various factors; optimizing algorithms, image database quality and size and comparison with existing systems.
Publish Date 2018-12-07 03:57
Citation de Lange T, Halvorsen P, Riegler M. Methodology to develop machine learning algorithms to improve performance in gastrointestinal endoscopy. World J Gastroenterol 2018; 24(45): 5057-5062
URL http://www.wjgnet.com/1007-9327/full/v24/i45/5057.htm
DOI http://dx.doi.org/10.3748/wjg.v24.i45.5057
Full Article (PDF) WJG-24-5057.pdf
Full Article (Word) WJG-24-5057.doc
Manuscript File 41991-Review.docx
Answering Reviewers 41991-Answering reviewers.pdf
Audio Core Tip 41991-Audio core tip.mp3
Conflict-of-Interest Disclosure Form 41991-Conflict-of-interest statement.pdf
Copyright License Agreement 41991-Copyright license agreement.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s) 41991-Grant application form(s).pdf
Non-Native Speakers of English Editing Certificate 41991-Language certificate.pdf
Peer-review Report 41991-Peer-review(s).pdf
Scientific Misconduct Check 41991-Scientific misconduct check.pdf
Scientific Editor Work List 41991-Scientific editor work list.pdf