ArticlePeerJ2025
Clustering and classification of early knee osteoarthritis using machine-learning analysis of step-up and down test kinematics in recreational table tennis players.
Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Artificial intelligence in the biomechanics of knee osteoarthritis: biomechanical phenotyping, risk stratification, diagnosis, treatment and clinical translation-a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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. Methods: Forty-three recreational table tennis players (eighty-six legs: 42 with EOA and 44 without EOA) underwent SU and SD tests. Frontal plane kinematics was analyzed using two-dimensional video analysis with markers placed at five key anatomical landmarks. Horizontal displacement measurements were compared between groups using independent Results: Significant differences were observed between EOA and non-EOA groups in frontal plane kinematics during SU and SD tests ( Conclusions: Machine-learning analysis of frontal plane kinematics during SU and SD tests showed promising potential for EOA detection and classification, offering a cost-effective and accessible alternative to conventional imaging-based approaches.
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