Publication Name	World Journal of Gastroenterology
Manuscript ID	65091
Country	United Kingdom

Received	2021-02-27 21:26
Peer-Review Started	2021-02-27 21:30
To Make the First Decision
Return for Revision	2021-04-18 02:29
Revised	2021-04-29 21:20
Second Decision	2021-08-24 03:23
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2021-08-24 08:41
Articles in Press	2021-08-24 08:41
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2021-09-13 01:01
Publish the Manuscript Online	2021-09-15 10:33

ISSN	1007-9327 (print) and 2219-2840 (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/
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Category	Gastroenterology & Hepatology
Manuscript Type	Minireviews
Article Title	Optical diagnosis of colorectal polyps using convolutional neural networks
Manuscript Source	Invited Manuscript
All Author List	Rawen Kader, Andreas V Hadjinicolaou, Fanourios Georgiades, Danail Stoyanov and Laurence B Lovat
Funding Agency and Grant Number
Corresponding Author	Rawen Kader, BMed, MBBS, MRCP, Research Fellow, Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, Charles Bell House, 43-45 Foley Street, Fitzrovia, London W1W 7TY, United Kingdom. r.kader@nhs.net
Key Words	Artificial intelligence; Deep learning; Convolutional neural networks; Computer aided diagnosis; Optical diagnosis; Colorectal polyps
Core Tip	A convolutional neural network (CNN) is a specific type of artificial intelligence deep learning. These networks may play an important role in the coming years in assisting endoscopists to optically diagnose colorectal polyps. CNNs can mitigate the inter-operator variability amongst endoscopists, potentially enabling a “resect and discard” or “leave in” strategy to be adopted. This would improve the efficiency of colonoscopy, reduce healthcare costs and reduce adverse events for patients by avoiding unnecessary resections of non-neoplastic polyps. In this article, we expand on the most relevant studies in this field and discuss limitations and future directions that will determine fulfilment of the potential of CNN in the optical diagnosis of colorectal polyps.
Publish Date	2021-09-15 10:33
Citation	Kader R, Hadjinicolaou AV, Georgiades F, Stoyanov D, Lovat LB. Optical diagnosis of colorectal polyps using convolutional neural networks. World J Gastroenterol 2021; 27(35): 5908-5918
URL	https://www.wjgnet.com/1007-9327/full/v27/i35/5908.htm
DOI	https://dx.doi.org/10.3748/wjg.v27.i35.5908

Full Article (PDF)	WJG-27-5908.pdf
Full Article (Word)	WJG-27-5908.docx
Manuscript File	65091_Auto_Edited-JLW.docx
Answering Reviewers	65091-Answering reviewers.pdf
Audio Core Tip	65091-Audio core tip.mp4
Conflict-of-Interest Disclosure Form	65091-Conflict-of-interest statement.pdf
Copyright License Agreement	65091-Copyright license agreement.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	65091-Grant application form(s).pdf
Supplementary Material	65091-Supplementary material.pdf
Peer-review Report	65091-Peer-review(s).pdf
Scientific Misconduct Check	65091-Scientific misconduct check.pdf
Scientific Editor Work List	65091-Scientific editor work list.pdf