Publication Name	Artificial Intelligence in Gastroenterology
Manuscript ID	56295
Country	United States

Received	2020-04-23 19:11
Peer-Review Started	2020-04-23 19:12
To Make the First Decision
Return for Revision	2020-06-04 04:19
Revised	2020-06-15 17:35
Second Decision	2020-06-16 09:30
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2020-06-16 22:54
Articles in Press	2020-06-16 22:54
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2020-07-06 01:12
Publish the Manuscript Online	2020-07-27 09:30

ISSN	2644-3236 (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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Website	http://www.wjgnet.com

Category	Gastroenterology & Hepatology
Manuscript Type	Retrospective Study
Article Title	Machine learning better predicts colonoscopy duration
Manuscript Source	Unsolicited Manuscript
All Author List	Alexander Joseph Podboy and David Scheinker
ORCID	Author(s) ORCID Number Alexander Joseph Podboy http://orcid.org/0000-0001-9353-4965 David Scheinker http://orcid.org/0000-0001-5885-8024
Funding Agency and Grant Number
Corresponding Author	Alexander Joseph Podboy, MD, Academic Fellow, Doctor, Doctor, Division of Gastroenterology and Hepatology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94063, United States. alexander.podboy@gmail.com
Key Words	Machine Learning; Colonoscopy; Endoscopy; Artifical intelligence; Practice outcomes; Operations
Core Tip	Machine learning has been utilized to predict surgical procedure duration and enhance operating room proficiency, however its usefulness for predicting colonoscopy procedure duration has not been examined. In determination of procedure duration by machine learning outperformed historical practice.
Publish Date	2020-07-27 09:30
Citation	Podboy AJ, Scheinker D. Machine learning better predicts colonoscopy duration. Artif Intell Gastroenterol 2020; 1(1): 30-36
URL	https://www.wjgnet.com/2644-3236/full/v1/i1/30.htm
DOI	https://dx.doi.org/10.35712/aig.v1.i1.30

Full Article (PDF)	AIG-1-30.pdf
Full Article (Word)	AIG-1-30.docx
Manuscript File	56295-Review.docx
Answering Reviewers	56295-Answering reviewers.pdf
Audio Core Tip	56295-Audio core tip.m4a
Biostatistics Review Certificate	56295-Biostatistics statement.pdf
Conflict-of-Interest Disclosure Form	56295-Conflict-of-interest statement.pdf
Copyright License Agreement	56295-Copyright license agreement.pdf
Signed Informed Consent Form(s) or Document(s)	56295-Informed consent statement.pdf
Institutional Review Board Approval Form or Document	56295-Institutional review board statement.pdf
Peer-review Report	56295-Peer-review(s).pdf
Scientific Misconduct Check	56295-Scientific misconduct check.pdf
Scientific Editor Work List	56295-Scientific editor work list.pdf
CrossCheck Report	56295-CrossCheck report.pdf