Non-invasive assessment of knee condition using acoustic emission monitoring

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Khokhlova, Liudmila
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University College Cork
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Joint disorders, in particular osteoarthritis (OA), are widespread in the older population worldwide, with the global prevalence of knee OA estimated at 27.37% for people aged 55 and older, according to Global Disease Burden Report 2019. Chronic pain and disability caused by OA pose a significant public health problem, and while late-stage disease-modifying options are mostly limited to joint replacements, OA develops over decades, allowing clinical care to potentially alter its course when timely diagnosis and early therapy are available and lifestyle changes are implemented. Multiple diagnostic methods are utilised for orthopaedic evaluation of OA, but they are frequently confined to clinical settings and need expensive equipment such as magnetic resonance imaging (MRI) and radiography, or especially qualified clinical specialists to analyse the imaging data (e.g., ultrasound). With the growing interest in tele- and personalised medicine, the methods that are suitable for such applications are actively gaining the attention of researchers. One such method is joint acoustic emission (AE) monitoring, a non-invasive method based on recording of the elastic waves within the materials during friction or deformation. While the method's feasibility and potential for use in orthopaedics have been established, the scarcity of studies investigating its reliability, the wide variation in methodologies, and the lack of a clear consensus on recording techniques and cartilage damage biomarkers are evident. The presented work is a publication-based thesis that highlights, in a series of interlinked peer-reviewed manuscripts (Appendix A), the development and evaluation of a novel, robust, and reliable non-invasive method for knee status assessment using AE monitoring. The main contribution of this thesis to this method is the establishment of new monitoring techniques that are less sensitive to motion artefacts (Section 2.2) and provide significantly improved reliability (Section 2.3) over methods investigated to date with inter-day ICCs up to 0.901, 95% CI [0.681, 0.978] for the tested number of hits per repetition. The proposed method was validated using progressive cartilage damage in a cadaver specimen (Chapter 3) that simulated OA development and allowed for the investigation related changes in knee AEs. The study led to the initial confirmation of the cause-and-effect relationship between articular cartilage damage and AE in controlled settings, where a potential impact of anatomical variations is minimal. Further validation was achieved by considering the correlations between knee AEs parameters in the general population and alternative measures of knee condition, such as self-reported knee status and functional assessments (Chapter 4), with moderate correlations (Spearman’s ρ up to 0.475, 95%CI [0.202, 0.679], p=0.001) being discovered between AEs parameters and functional test (five times sit-to-stand) results. The highest correlation scores observed for the metrics that reflect the functional state of the knee indicate a unique insight of the method into the interaction between articular surfaces during knee movement rather than static imaging. This thesis contributes to the development of joint AE monitoring from an engineering and clinical perspective. By achieving improved repeatability, this work allows for a further investigation of the method, specifically for assessment of treatment progress or disease progression, where maintaining measurements’ reliability over time is crucial. The presented evaluation of the feasibility of assessing progressive cartilage damage and correlation analysis with alternative metrics of knee condition assessment contribute to the ongoing progress of joint AE monitoring validation, confirming it’s clinical relevance and bringing it closer to clinical practice. Additionally, a link discovered between knee AEs and joint functionality points to the potential for AE monitoring to complement existing imaging techniques with unique insights into the interaction between articular surfaces during joint function. Such an understanding could prove particularly valuable in evaluating the effectiveness of disease-modifying drugs, therapies, and rehabilitation progress in future.
Medical diagnosis , Acoustic emission , Orthopaedics , Knee health , Joint sound , Osteoarthritis , Repeatability
Khokhlova, L. 2023. Non-invasive assessment of knee condition using acoustic emission monitoring. PhD Thesis, University College Cork.