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7/27/2020 9:30:51 AM | Browse: 25 | Download: 12
Publication Name Artificial Intelligence in Gastroenterology
Manuscript ID 56295
Country/Territory United States
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
2020-06-15 17:35
Second Decision
2020-06-16 09:30
Accepted by Journal Editor-in-Chief
Accepted by Company 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.
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 and 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
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, Division of Gastroenterology and Hepatology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94063, United States. alexander.podboy@gmail.com
Keywords 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