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2/7/2024 2:35:41 AM | Browse: 83 | Download: 33
Publication Name World Journal of Orthopedics
Manuscript ID 90357
Country/Territory United States
2023-11-30 21:14
Peer-Review Started
2023-11-30 21:14
To Make the First Decision
Return for Revision
2023-12-07 07:58
2023-12-22 02:49
Second Decision
2024-01-04 02:49
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief
2024-01-04 06:54
Articles in Press
2024-01-04 06:54
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript
2024-02-02 04:00
Publish the Manuscript Online
2024-02-07 02:35
ISSN 2218-5836 (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: https://creativecommons.org/Licenses/by-nc/4.0/
Copyright ©The Author(s) 2024. 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 Mathematical & Computational Biology
Manuscript Type Editorial
Article Title Deep learning automation of radiographic patterns for hallux valgus diagnosis
Manuscript Source Invited Manuscript
All Author List Angela Hussain, Cadence Lee, Eric Hu and Farid Amirouche
Author(s) ORCID Number
Cadence Lee http://orcid.org/0000-0002-5437-4273
Farid Amirouche http://orcid.org/0000-0002-3002-4199
Funding Agency and Grant Number
Corresponding Author Farid Amirouche, PhD, Professor, Department of Orthopedics Surgery, University of Illinois at Chicago, 835 S. Wolcott Ave, Room E270, Chicago, IL 60612, United States. amirouch@uic.edu
Key Words Artificial intelligence; Hallux valgus; Deep learning; Automated radiography
Core Tip This editorial summarizes and outlines the original paper “Automated decision support for Hallux valgus treatment options using anteroposterior foot radiographs”. We summarize the scope of the deep learning process and compare it to existing artificial intelligence studies used in clinical diagnostic studies. We additionally describe its limitations and impact in the field of automated diagnostic tools.
Publish Date 2024-02-07 02:35
Citation Hussain A, Lee C, Hu E, Amirouche F. Deep learning automation of radiographic patterns for hallux valgus diagnosis. World J Orthop 2024; 15(2): 105-109
URL https://www.wjgnet.com/2218-5836/full/v15/i2/105.htm
DOI https://dx.doi.org/10.5312/wjo.v15.i2.105
Full Article (PDF) WJO-15-105-with-cover.pdf
Full Article (Word) WJO-15-105.docx
Manuscript File 90357_Auto_Edited-YJP.docx
Answering Reviewers 90357-Answering reviewers.pdf
Audio Core Tip 90357-Audio core tip.mp3
Conflict-of-Interest Disclosure Form 90357-Conflict-of-interest statement.pdf
Copyright License Agreement 90357-Copyright license agreement.pdf
Peer-review Report 90357-Peer-review(s).pdf
Scientific Misconduct Check 90357-Bing-Chen YL-2.png
Scientific Editor Work List 90357-Scientific editor work list.pdf