Introduction
Knee osteoarthritis (OA) is a prevalent degenerative joint disease that affects millions of people worldwide, causing pain, stiffness, and a reduced quality of life ( Whittaker et al., 2021 ). According to recent World Health Organization data, osteoarthritis affects over 520 million people globally, making it the most common joint disorder worldwide and one of the leading causes of disability ( WHO, 2022 ). The prevalence is expected to increase dramatically due to aging populations, rising obesity rates, and increased participation in high-impact sports, creating an urgent need for effective early detection and intervention strategies ( Migliore et al., 2023 ). In addition, early detection and intervention in the initial stages of OA, known as early OA (EOA), have gained significant recognition for their potential to improve long-term outcomes and slow disease progression ( Migliore et al., 2023 ). However, current diagnostic methods often fail to identify OA until significant joint damage occurs, highlighting the need for more sensitive and specific tools to detect EOA ( Chu et al., 2012 ; Ryd et al., 2015 ).

Individuals who engage in sports characterized by frequent pivoting and repeated impact movements face an elevated risk of developing knee osteoarthritis. Table tennis demands players to perform quick directional shifts, constant lateral movements, and continuous weight transfers during matches ( Biz et al., 2022 ; Vannini et al., 2016 ). While table tennis is generally associated with higher levels of shoulder injuries compared to other racket sports, the knee joint still accounts for approximately 5% of acute injuries experienced by table tennis players ( Kondrič et al., 2011 ). Research comparing former elite table tennis players with non-athletic controls found that 68.2% of ex-elite table tennis players reported symptoms of knee pain compared with only 27.3% of the control group ( p = 0.02) ( Rajabi et al., 2012 ). This significantly higher prevalence of knee symptoms appears to be primarily attributed to the distinctive movement patterns inherent to the sport, specifically the continuous pivoting and rapid changes in direction ( Lam et al., 2019 ; Shao et al., 2020 ). Given these findings, there is growing concern about the potential long-term impact on recreational players who regularly participate in table tennis. This population may be particularly valuable for investigating early detection strategies for osteoarthritis, as they could be susceptible to premature development of the condition.

While age, sex, body mass index (BMI), and genetic factors play significant roles in OA development, recent research has shifted the focus from these static factors to dynamic biomechanical alterations as crucial elements in OA development and progression ( Zengini et al., 2018 ). Abnormal movement patterns can lead to altered joint loading, potentially contributing to cartilage degradation and OA onset ( Dell’Isola et al., 2017 ). These changes in joint kinematics can be subtle during the early stages of the disease, making them difficult to detect using conventional clinical assessments ( Chu et al., 2012 ; Ryd et al., 2015 ). An increased knee adduction moment during gait is associated with a higher risk of OA progression ( Miyazaki et al., 2002 ). Furthermore, altered knee flexion angles and moments during walking have been observed in individuals with early-stage knee OA ( Favre, Erhart-Hledik & Andriacchi, 2014 ; Mündermann, Dyrby & Andriacchi, 2005 ).

Functional performance tests, such as step-up (SU) and step-down (SD) tests, have emerged as valuable tools for assessing lower limb function in patients with knee OA ( Almeida et al., 2021 ). The SU and SD tests are reliable and valid for assessing functional performance in individuals with knee OA ( Almeida et al., 2021 ). Moreover, these tests demonstrated good responsiveness to changes following the interventions in patients with OA ( Almeida et al., 2021 ). Several studies have used SU and SD kinematics to investigate knee OA. Pain during stair climbing is an early indicator of knee OA ( Hensor et al., 2015 ). Altered knee kinematics during stair ascent and descent have been reported in individuals with knee OA ( Kaufman et al., 2001 ). Differences in the knee and hip angles during stair descent were observed between patients with OA and healthy controls ( Igawa & Katsuhira, 2014 ). While these studies primarily focused on sagittal plane kinematics, our study aimed to analyze frontal plane kinematics during SU and SD. This approach is crucial because frontal plane movements, such as knee adduction, may have a more significant impact on joint stress than sagittal plane movements ( Mündermann, Dyrby & Andriacchi, 2005 ; Sharma et al., 2001 ). By examining the frontal plane kinematics, the present study can potentially gain more insight into the biomechanical factors that contribute to EOA development and progression.

Current diagnostic methods often fail to identify OA until significant joint damage occurs, highlighting the need for more sensitive and specific tools to detect EOA ( Chu et al., 2012 ; Ryd et al., 2015 ). Conventional diagnostic approaches such as radiography, magnetic resonance imaging (MRI), and arthroscopy have notable limitations that restrict their effectiveness for early detection and large-scale screening. These limitations include high cost and limited accessibility, particularly for MRI and arthroscopy; time-intensive procedures requiring specialized facilities; inconsistent interpretation with significant inter-observer variability; and the need for highly trained specialists for result interpretation. Furthermore, standard radiography often fails to detect early cartilage changes ( Guermazi et al., 2011 ; Hayashi, Guermazi & Kwoh, 2014 ), while more sensitive imaging methods may identify incidental findings that do not correlate with clinical symptoms ( Culvenor et al., 2019 ; Horga et al., 2020 ). These limitations underscore the need for more accessible, objective, and cost-effective methods to detect EOA before irreversible joint damage occurs.

The application of machine-learning techniques to biomechanical data offers a promising approach for the accurate and early detection of OA. Although recent studies have demonstrated the potential of machine learning (ML) in OA research using magnetic resonance imaging (MRI) or radiography data, these methods may have limitations in detecting EOA ( Pedoia et al., 2019 ; Tiulpin et al., 2019 ). Although some studies have used inertial measurement unit sensors for kinematic analysis in OA prediction, the clinical applicability of these methods may be limited ( Clermont et al., 2019 ). Our study aimed to address these limitations by utilizing a two dimensional (2D) video analysis of SU and SD test kinematics combined with machine-learning techniques. This approach offers a more practical and clinically feasible method for EOA detection while potentially maintaining high prediction and classification accuracy ( Kobsar et al., 2017 ; Osis et al., 2016 ).

Building on this foundation, this research aimed to: (1) use unsupervised learning algorithms to identify distinct groups based on frontal plane kinematic patterns during SU and SD tests; (2) develop supervised learning models to classify between EOA and non-EOA status (binary classification); and (3) identify the most influential kinematic variables associated with EOA by focusing on SU and SD tests and employing both clustering and classification approaches. The present study hypothesized that: (1) individuals with EOA would demonstrate significantly different frontal plane kinematic patterns during SU and SD tests compared to those without EOA; (2) these kinematic patterns could be classified into distinct subgroups using unsupervised ML; and (3) supervised ML algorithms could accurately classify EOA status using these kinematic variables as predictive features.