Publication Name	Artificial Intelligence in Gastrointestinal Endoscopy
Manuscript ID	56976
Country	United Kingdom

Received	2020-06-01 16:25
Peer-Review Started	2020-06-01 16:26
To Make the First Decision
Return for Revision	2020-06-18 02:41
Revised	2020-07-14 10:29
Second Decision	2020-07-16 10:19
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2020-07-16 23:35
Articles in Press	2020-07-16 23:35
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2020-07-23 00:25
Publish the Manuscript Online	2020-07-23 11:43

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.
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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	Application of convolutional neural networks for computer-aided detection and diagnosis in gastrointestinal pathology: A simplified exposition for an endoscopist
Manuscript Source	Invited Manuscript
All Author List	Yirupaiahgari KS Viswanath, Sagar Vaze and Richie Bird
ORCID	Author(s) ORCID Number Yirupaiahgari KS Viswanath http://orcid.org/0000-0003-3880-1172 Sagar Vaze http://orcid.org/0000-0003-2920-9345 Richie Bird http://orcid.org/0000-0002-4560-708X
Funding Agency and Grant Number
Corresponding Author	Yirupaiahgari KS Viswanath, CCST, FRCS, FRCS (Gen Surg), MBBS, Professor, Upper GI Laparoscopic and Endoscopic Unit, James Cook University Hospital, Marton Road, Middlesbrough, Cleveland TS43BW, United Kingdom. keyhole1234@gmail.com
Key Words	Convolutional neural network; Gastrointestinal endoscopy; Artificial intelligence; Deep learning; Machine learning;
Core Tip	The convolutional neural network (CNN), a deep learning model, has gained immense success in endoscopy image analysis, with its application to diagnose and detect gastrointestinal (GI) pathology at endoscopy. This article shares a basic framework of the utilisation of CNNs in GI endoscopy, along with a concise review of a few published AI-based endoscopy articles in the last 4 years.
Publish Date	2020-07-23 11:43
Citation	Viswanath YKS, Vaze S, Bird R. Application of convolutional neural networks for computer-aided detection and diagnosis in gastrointestinal pathology: A simplified exposition for an endoscopist. Artif Intell Gastrointest Endosc 2020; 1(1): 1-5
URL	https://www.wjgnet.com/2689-7164/full/v1/i1/1.htm
DOI	https://dx.doi.org/10.37126/aige.v1.i1.1

Full Article (PDF)	AIGE-1-1.pdf
Full Article (Word)	AIGE-1-1.docx
Manuscript File	56976-Review.docx
Answering Reviewers	56976-Answering reviewers.pdf
Audio Core Tip	56976-Audio core tip.mp3
Conflict-of-Interest Disclosure Form	56976-Conflict-of-interest statement.pdf
Copyright License Agreement	56976-Copyright license agreement.pdf
Peer-review Report	56976-Peer-review(s).pdf
Scientific Misconduct Check	56976-Scientific misconduct check.pdf
Scientific Editor Work List	56976-Scientific editor work list.pdf
CrossCheck Report	56976-CrossCheck report.pdf