Publication Name	World Journal of Orthopedics
Manuscript ID	90357
Country	United States

Received	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
Revised	2023-12-22 02:49
Second Decision	2024-01-04 02:49
Accepted by Journal Editor-in-Chief
Accepted by Executive 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
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
CrossCheck Report	90357-CrossCheck report.pdf