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©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Orthop. Jun 18, 2022; 13(6): 603-614
Published online Jun 18, 2022. doi: 10.5312/wjo.v13.i6.603
Published online Jun 18, 2022. doi: 10.5312/wjo.v13.i6.603
Evaluation of artificial intelligence models for osteoarthritis of the knee using deep learning algorithms for orthopedic radiographs
Anjali Tiwari, Vaibhav Bagaria, Department ofOrthopedics, Sir H. N. Reliance Foundation Hospital and Research Centre, Mumbai 400004, India
Murali Poduval, Lifesciences Engineering, Tata Consultancy Services, Mumbai 400096, India
Vaibhav Bagaria, Department ofOrthopedics, Columbia Asia Hospital, Mumbai 400004, India
Author contributions: Tiwari A analyzed and evaluated the feasibility and accuracy of the artificial intelligence models with the interpretation of data; Poduval M was involved in critically revising the draft; Bagaria V designed and conceptualized the study and revised the manuscript for important intellectual content; All authors read and approved the final manuscript.
Institutional review board statement: The study was reviewed and approved by the Scientific Advisory Committee and Institutional Ethics Committee of Sir H. N. Reliance Foundation Hospital and Research Centre, Mumbai, India (Approval No. HNH/IEC/2021/OCS/ORTH/56).
Informed consent statement: Informed consent was waived since the data were retrospectively collated anonymously from routine clinical practice.
Conflict-of-interest statement: The authors of this manuscript have no conflicts of interest to disclose.
Data sharing statement: No additional data are available.
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/
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
Received: November 30, 2021
Peer-review started: November 30, 2021
First decision: January 11, 2022
Revised: January 20, 2022
Accepted: May 13, 2022
Article in press: May 13, 2022
Published online: June 18, 2022
Processing time: 198 Days and 15.7 Hours
Peer-review started: November 30, 2021
First decision: January 11, 2022
Revised: January 20, 2022
Accepted: May 13, 2022
Article in press: May 13, 2022
Published online: June 18, 2022
Processing time: 198 Days and 15.7 Hours
Core Tip
Core Tip: In this study, we evaluated different machine learning models to determine which model is best to classify the severity of knee osteoarthritis using the Kellgren-Lawrence grading system. The image set was composed of radiographs of native knees, in anteroposterior and lateral views. The radiographic exams were annotated by experts and tagged according to Kellgren-Lawrence grades. The findings of this study will pave the way for future development in the field, with the development of more accurate models and tools that can improve medical image classification by machine learning and will give valuable insight into orthopedic disease pathology.