Clustering and classification of early knee osteoarthritis using machine-learning analysis of step-up and down test kinematics in recreational table tennis players.

Journal: PeerJ
Published Date: May 27, 2025

Abstract

OBJECTIVE: Early detection of knee osteoarthritis is crucial for improving patient outcomes. While conventional imaging methods often fail to detect early changes and require specialized expertise for interpretation, this study aimed to investigate the use of frontal plane kinematic data during step-up (SU) and step-down (SD) tests to classify and predict early osteoarthritis (EOA) using machine-learning techniques.

Authors

  • Ui-Jae Hwang
    Laboratory of KEMA AI Research (KAIR), Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea.
  • Kyu Sung Chung
    Hanyang University Guri Hospital, Department of Orthopaedic Surgery, Hanyang University, Guri-si, Gyeonggi-do, Republic of South Korea.
  • Sung-Min Ha
    Department of Physical Therapy, Sang Ji University, Wonjusi, Gangwondo, Republic of South Korea.

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