Publication Name	World Journal of Gastroenterology
Manuscript ID	41991
Country	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 Executive 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.
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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
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