Quantitative wear particle analysis for osteoarthritis assessment

Author:

Guo Meizhai1,Lord Megan S2,Peng Zhongxiao1

Affiliation:

1. School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, Australia

2. Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, Australia

Abstract

Osteoarthritis is a degenerative joint disease that affects millions of people worldwide. The aims of this study were (1) to quantitatively characterise the boundary and surface features of wear particles present in the synovial fluid of patients, (2) to select key numerical parameters that describe distinctive particle features and enable osteoarthritis assessment and (3) to develop a model to assess osteoarthritis conditions using comprehensive wear debris information. Discriminant analysis was used to statistically group particles based on differences in their numerical parameters. The analysis methods agreed with the clinical osteoarthritis grades in 63%, 50% and 61% of particles for no osteoarthritis, mild osteoarthritis and severe osteoarthritis, respectively. This study has revealed particle features specific to different osteoarthritis grades and provided further understanding of the cartilage degradation process through wear particle analysis – the technique that has the potential to be developed as an objective and minimally invasive method for osteoarthritis diagnosis.

Publisher

SAGE Publications

Subject

Mechanical Engineering,General Medicine

Reference22 articles.

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2. The Problem of Chondromalacia Patellae

3. Bevill SL. Regional variations in knee joint articular cartilage mechanobiology: a consideration in the initiation of osteoarthritis. Ann Arbor, MI: Proquest, 2009.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Numerical analysis of a poroelastic cartilage model: Investigating the influence of changing material properties in osteoarthritis;Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering;2024-04-24

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