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10/12/2021 7:00:09 AM | Browse: 60 | Download: 68
Publication Name World Journal of Gastroenterology
Manuscript ID 65087
Country/Territory Chile
2021-02-28 21:29
Peer-Review Started
2021-02-28 21:33
To Make the First Decision
Return for Revision
2021-03-27 21:39
2021-04-26 21:39
Second Decision
2021-09-13 02:28
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief
2021-09-16 23:53
Articles in Press
2021-09-16 23:53
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript
2021-10-11 00:10
Publish the Manuscript Online
2021-10-12 06:53
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/
Copyright © The Author(s) 2021. 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 Engineering, Biomedical
Manuscript Type Minireviews
Article Title Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions
Manuscript Source Invited Manuscript
All Author List Michelle Viscaino, Javier Torres Bustos, Pablo Muñoz, Cecilia Auat Cheein and Fernando Auat Cheein
Funding Agency and Grant Number
Funding Agency Grant Number
Chilean National Agency for Research and Development (ANID) FB0008
CONICYT-PCHA/Doctorado Nacional 2018-21181420
Corresponding Author Fernando Auat Cheein, PhD, Associate Professor, Department of Electronic Engineering, Universidad Técnica Federico Santa María, Av. España 1680, Valparaiso 2340000, Chile. fernando.auat@usm.cl
Key Words Artificial intelligence; Machine learning; Deep learning; Medical images; Colorectal cancer; Colorectal polyps
Core Tip Artificial intelligence-based (AI) methods have demonstrated high performance in classification, object detection, and segmentation tasks. Through multidisciplinary and collaborative work between clinicians and technicians, the advantages of AI have been successfully applied in automatic polyp detection and classification. The new AI-based systems present a better polyp detection rate and contribute to better clinical decision-making for preventing colorectal cancer (CRC). This article provides an overview of recent research focusing on AI and its applications in the early detection of CRC and adenomatous polyps.
Publish Date 2021-10-12 06:53
Citation Viscaino M, Torres Bustos J, Muñoz P, Auat Cheein C, Cheein FA. Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions. World J Gastroenterol 2021; 27(38): 6399-6414
URL https://www.wjgnet.com/1007-9327/full/v27/i38/6399.htm
DOI https://dx.doi.org/10.3748/wjg.v27.i38.6399
Full Article (PDF) WJG-27-6399.pdf
Full Article (Word) WJG-27-6399.docx
Manuscript File 65087_Auto_Edited.docx
Answering Reviewers 65087-Answering reviewers.pdf
Audio Core Tip 65087-Audio core tip.wav
Conflict-of-Interest Disclosure Form 65087-Conflict-of-interest statement.pdf
Copyright License Agreement 65087-Copyright license agreement.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s) 65087-Grant application form(s).pdf
Non-Native Speakers of English Editing Certificate 65087-Language certificate.pdf
Peer-review Report 65087-Peer-review(s).pdf
Scientific Misconduct Check 65087-Bing Liu M-1.png
Scientific Misconduct Check 65087-CrossCheck.png
Scientific Misconduct Check 65087-Bing-Gong ZM-2.png
Scientific Editor Work List 65087-Scientific editor work list.pdf