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6/17/2022 3:54:26 AM | Browse: 45 | Download: 26
Publication Name World Journal of Orthopedics
Manuscript ID 73676
Country/Territory India
Received
2021-11-30 12:14
Peer-Review Started
2021-11-30 12:17
To Make the First Decision
Return for Revision
2022-01-11 02:37
Revised
2022-01-20 10:56
Second Decision
2022-05-10 02:52
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief
2022-05-14 08:26
Articles in Press
2022-05-14 08:26
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript
2022-06-08 23:50
Publish the Manuscript Online
2022-06-17 03:54
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) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
Article Reprints For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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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 Orthopedics
Manuscript Type Retrospective Study
Article Title Evaluation of artificial intelligence models for osteoarthritis of the knee using deep learning algorithms for orthopedic radiographs
Manuscript Source Invited Manuscript
All Author List Anjali Tiwari, Murali Poduval and Vaibhav Bagaria
ORCID
Author(s) ORCID Number
Anjali Tiwari http://orcid.org/0000-0002-8683-6102
Murali Poduval http://orcid.org/0000-0002-6821-0640
Vaibhav Bagaria http://orcid.org/0000-0002-3009-3485
Funding Agency and Grant Number
Corresponding Author Vaibhav Bagaria, FCPS, MBBS, MS, Director, Department of Orthopedics, Sir H. N. Reliance Foundation Hospital and Research Centre, Raja Rammohan Roy Road, Prarthana Samaj, Mumbai 400004, India. bagariavaibhav@gmail.com
Key Words Osteoarthritis; Artificial intelligence; Knee; Computer vision; Machine leaning; Deep learning
Core Tip In this study, we evaluated different machine learning models to determine which model is best to suited classify the severity of osteoarthritis of the knee using the Kellgren Lawrence (KL) grading system. The image set was a radiograph of native knee in AP and lateral view. The radiographic images don’t include common visual disturbances such as implants, casts and other pathologies in medical images. All the radiographic images with clinical signs of osteoarthritis according to KL grades were selected. These radiographic exams were annotated by experts and tagged as per different classes according to KL grades. This study will pave the way for future development in the field with the development of more accurate models and tools which can improve medical image classification by Machines and will give valuable insight into orthopedic disease pathology.
Publish Date 2022-06-17 03:54
Citation Tiwari A, Poduval M, Bagaria V. Evaluation of artificial intelligence models for osteoarthritis of the knee using deep learning algorithms for orthopedic radiographs. World J Orthop 2022; 13(6): 603-614
URL https://www.wjgnet.com/2218-5836/full/v13/i6/603.htm
DOI https://dx.doi.org/10.5312/wjo.v13.i6.603
Full Article (PDF) WJO-13-603.pdf
Full Article (Word) WJO-13-603.docx
Manuscript File 73676_Auto_Edited-LS.docx
Answering Reviewers 73676-Answering reviewers.pdf
Audio Core Tip 73676-Audio core tip.aac
Biostatistics Review Certificate 73676-Biostatistics statement.pdf
Conflict-of-Interest Disclosure Form 73676-Conflict-of-interest statement.pdf
Copyright License Agreement 73676-Copyright license agreement.pdf
Signed Informed Consent Form(s) or Document(s) 73676-Informed consent statement.pdf
Institutional Review Board Approval Form or Document 73676-Institutional review board statement.pdf
Non-Native Speakers of English Editing Certificate 73676-Language certificate.pdf
Peer-review Report 73676-Peer-review(s).pdf
Scientific Editor Work List 73676-Scientific editor work list.pdf