Publication Name	Artificial Intelligence in Gastrointestinal Endoscopy
Manuscript ID	67312
Country	Brazil

Received	2021-04-21 02:05
Peer-Review Started	2021-04-21 02:08
To Make the First Decision
Return for Revision	2021-05-19 21:06
Revised	2021-06-05 04:02
Second Decision	2021-07-19 03:13
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2021-07-19 09:28
Articles in Press	2021-07-19 09:28
Publication Fee Transferred
Edit the Manuscript by Language Editor	2021-07-26 01:45
Typeset the Manuscript	2021-08-04 05:59
Publish the Manuscript Online	2021-09-06 10:32

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) 2021. 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	Minireviews
Article Title	Deep learning applied to the imaging diagnosis of hepatocellular carcinoma
Manuscript Source	Invited Manuscript
All Author List	Vinícius Remus Ballotin, Lucas Goldmann Bigarella, John Soldera and Jonathan Soldera
Funding Agency and Grant Number
Corresponding Author	Jonathan Soldera, MD, MSc, Associate Professor, Staff Physician, Clinical Gastroenterology, Universidade de Caxias do Sul, Rua Francisco Getúlio Vargas 1130, Caxias do Sul 95070-560, RS, Brazil. jonathansoldera@gmail.com
Key Words	Hepatocellular carcinoma; Cirrhosis; Machine learning; Artificial intelligence
Core Tip	Hepatocellular carcinoma is diagnosed using imaging techniques, such as computed tomography and magnetic resonance imaging. In order to improve outcomes and bypass obstacles, many companies and clinical centers have been trying to develop deep learning systems (DLS) that could be able to diagnose and classify liver nodules in the cirrhotic liver. Neural networks has become one of the most efficient approaches to accurately diagnose liver nodules using DLS. Therefore, with the improvement of these techniques in the long term, they could be applicable in daily practice, modifying outcomes.
Publish Date	2021-09-06 10:32
Citation	Ballotin VR, Bigarella LG, Soldera J, Soldera J. Deep learning applied to the imaging diagnosis of hepatocellular carcinoma. Artif Intell Gastrointest Endosc 2021; 2(4): 127-135
URL	https://www.wjgnet.com/2689-7164/full/v2/i4/127.htm
DOI	https://dx.doi.org/10.37126/aige.v2.i4.127

Full Article (PDF)	AIGE-2-127.pdf
Full Article (Word)	AIGE-2-127.docx
Manuscript File	67312-Review-FilipodiaCL.docx
Answering Reviewers	67312-Answering reviewers.pdf
Audio Core Tip	67312-Audio core tip.m4a
Conflict-of-Interest Disclosure Form	67312-Conflict-of-interest statement.pdf
Copyright License Agreement	67312-Copyright license agreement.pdf
Non-Native Speakers of English Editing Certificate	67312-Language certificate.pdf
Peer-review Report	67312-Peer-review(s).pdf
Scientific Misconduct Check	63712-Bing-Wang JL-1.jpg
Scientific Misconduct Check	67312-Bing-Fan JR-2.png
Scientific Misconduct Check	67312-Scientific misconduct check.pdf
Scientific Editor Work List	67312-Scientific editor work list.pdf