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World J Methodol. Dec 20, 2023; 13(5): 419-425
Published online Dec 20, 2023. doi: 10.5662/wjm.v13.i5.419
Machine learning and deep neural network-based learning in osteoarthritis knee
Harish V K Ratna, Madhan Jeyaraman, Naveen Jeyaraman, Arulkumar Nallakumarasamy, Shilpa Sharma, Manish Khanna, Ashim Gupta
Harish V K Ratna, Department of Orthopaedics, Rathimed Speciality Hospital, Chennai 600040, Tamil Nadu, India
Madhan Jeyaraman, Naveen Jeyaraman, Arulkumar Nallakumarasamy, Department of Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai 600077, Tamil Nadu, India
Madhan Jeyaraman, Ashim Gupta, Department of Orthopaedics, South Texas Orthopaedic Research Institute, Laredo, TX 78045, United States
Shilpa Sharma, Department of Paediatric Surgery, All India Institute of Medical Sciences, New Delhi 110029, India
Manish Khanna, Department of Orthopaedics, Autonomous State Medical College, Ayodhya 224133, Uttar Pradesh, India
Ashim Gupta, Department of Regenerative Medicine, Regenerative Orthopaedics, Noida 201301, Uttar Pradesh, India
Ashim Gupta, Department of Regenerative Medicine, Future Biologics, Lawrenceville, GA 30043, United States
Ashim Gupta, Department of Regenerative Medicine, BioIntegarte, Lawrenceville, GA 30043, United States
Author contributions: Jeyaraman M contributed to conceptualization; Ratna HVK, Jeyaraman N, and Nallakumarasamy A contributed to initial draft; Ratna HVK and Jeyaraman M contributed to final draft; Sharma S, Khanna M, and Gupta A contributed to supervision; All authors accepted to publish the current version of the manuscript.
Conflict-of-interest statement: Authors declare no conflicts of interest.
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: Madhan Jeyaraman, MS, PhD, Assistant Professor, Research Associate, Department of Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai 600077, Tamil Nadu, India. madhanjeyaraman@gmail.com
Received: July 16, 2023
Peer-review started: July 16, 2023
First decision: September 13, 2023
Revised: September 14, 2023
Accepted: September 28, 2023
Article in press: September 28, 2023
Published online: December 20, 2023
Abstract

Osteoarthritis (OA) of the knee joint is considered the commonest musculoskeletal condition leading to marked disability for patients residing in various regions around the globe. Application of machine learning (ML) in doing research regarding OA has brought about various clinical advances viz, OA being diagnosed at preliminary stages, prediction of chances of development of OA among the population, discovering various phenotypes of OA, calculating the severity in OA structure and also discovering people with slow and fast progression of disease pathology, etc. Various publications are available regarding machine learning methods for the early detection of osteoarthritis. The key features are detected by morphology, molecular architecture, and electrical and mechanical functions. In addition, this particular technique was utilized to assess non-interfering, non-ionizing, and in-vivo techniques using magnetic resonance imaging. ML is being utilized in OA, chiefly with the formulation of large cohorts viz, the OA Initiative, a cohort observational study, the Multi-centre Osteoarthritis Study, an observational, prospective longitudinal study and the Cohort Hip & Cohort Knee, an observational cohort prospective study of both hip and knee OA. Though ML has various contributions and enhancing applications, it remains an imminent field with high potential, also with its limitations. Many more studies are to be carried out to find more about the link between machine learning and knee osteoarthritis, which would help in the improvement of making decisions clinically, and expedite the necessary interventions.

Keywords: Osteoarthritis, Knee, Artificial intelligence, Machine learning, Deep neural network

Core Tip: Application of machine learning in research has various clinical advances viz, osteoarthritis (OA) knee being diagnosed at preliminary stages, prediction of development of OA, discovering various phenotypes. Large cohorts have been formulated viz, the OA Initiative, the Multi-centre Osteoarthritis Study and the Cohort Hip & Cohort Knee. Many studies are awaited to find about the link between ML and knee OA, which would improve making decisions clinically, and expedite the necessary interventions.