Discovery of acoustic emission based biomarker for quantitative assessment of knee joint ageing and degeneration

Based on the study of 34 healthy and 19 osteoarthritic knees in three different age groups (early, middle and late adulthood), this thesis reports the discovery of the potential of knee acoustic emission (AE) as a biomarker for quantitative assessment of joint ageing and degeneration. Signal processing and statistical analysis were conducted on the joint angle signals acquired using electronic goniometers attached to the lateral side of the legs during repeated sit- stand-sit movements. A four-phase movement model derived from joint angle measurement is proposed for statistical analysis, and it consists of the ascending- acceleration and ascending-deceleration phases in the sit-to- stand movement, followed by the descending-acceleration and descending-deceleration phases in the stand-to-sit movement. Through the quantitative assessment of joint angle signals based on the four-phase model established, statistical differences of different knee conditions related to age and degeneration were discovered based on cycle-by- cycle variations and movement symmetry. For AE burst signals acquired from piezo-electric sensors attached to the knee joints during repeated sit-stand-sit movements, the statistical analysis started from the quantity of AE events in the proposed four movement phases and extended to waveform features extracted from AE signals. While the quantity of AE events was found to follow certain statistical trends related to age and degeneration in each movement phase, detail statistical analysis of AE waveform features yielded the peak amplitude value and average signal level of each AE burst as two most significant features. An image based knee AE feature profile is presented based on 2D colour histograms formed by the peak amplitude value and average signal level in four movement phases. It provides not only a visual trend related to knee age and degeneration, but also enables visual assessment of th

Discovering Associations between Acoustic Emission and Magnetic Resonance Imaging Biomarkers from 10 Osteoarthritic Knees

Objective: Acoustic emission (AE) sensed from knee joints during weight-bearing movements greatly increases with joint deterioration, but the relationship between AE patterns and specific anatomical damage, as seen for example in magnetic resonance imaging (MRI), is unknown. This knowledge is essential to validate AE biomarkers for the evaluation of knee joints, and forms the objective of this exploratory work to associate knee AE and MRI. Methods: A novel processing framework is proposed to enable direct correlation between static 3D MRI of knees and their dynamic 1D AE during sit-stand-sit movements. It comprises a method to estimate articular cartilage thickness according to joint angle from knee MRI, and a method to derive statistically representative waveform features according to joint angle from movement and load-dependent knee AE. Results: In 10 subjects diagnosed with knee osteoarthritis, age 55~79 years and body mass index 25~35 kg/m2, a strong inverse relationship between knee AE and cartilage thickness in the medial tibiofemoral compartment around the fully standing position was observed. Knees with thinner articular cartilage generated more AE with higher amplitude, greater energy, longer duration, and higher frequencies, in agreement with the assumption of more intense articulation friction under full body weight. Conclusion: AE provides promising quantitative biomarkers in knee joint disease. Significance: These findings provide impetus for the further development of AE as a low-cost non-invasive biomarker modality to improve the management of knee joint disease

Assessment of Hip and Knee Joints and Implants Using Acoustic Emission Monitoring:A Scoping Review

Objectives: Population ageing and the subsequent increase of joint disorders prevalence requires the development of non-invasive and early diagnostic methods to enable timely medical assistance and promote healthy aging. Over the last decades, acoustic emission (AE) monitoring, a technique widely used in non-destructive testing, has also been introduced in orthopedics as a diagnostic tool. This review aims to synthesize the literature on the use of AE monitoring for the assessment of hip and knee joints or implants, highlighting the practical aspects and implementation considerations. Methods: this review was conducted as per the PRISMA statement for scoping reviews. All types of studies, with no limits on date of publication, were considered. Articles were assessed and study design parameters and technical characteristics were extracted from relevant studies. Results: conducted search identified 1379 articles and 64 were kept for charting. Seven additional articles were added at a later stage. Reviewed works were grouped into studies on joint condition assessment, implant assessment, and hardware or software development. Native knees and hip implants were most commonly assessed. The most researched conditions were osteoarthritis, implant loosening or squeaking in vivo and structural damage of implants in vitro. Conclusion: in recent years, AE monitoring showed potential of becoming a useful diagnostic tool for lower limb pathologies. However, further research is needed to refine the existing methods and assess their feasibility in early diagnostics. Significance: The current state of research on AE monitoring for hip and knee joint assessment is described and future research directions are identified

Assessment of hip and knee joints and implants using acoustic emission monitoring: A scoping review

Objectives: Population ageing and the subsequent increase of joint disorders prevalence requires the development of non-invasive and early diagnostic methods to enable timely medical assistance and promote healthy aging. Over the last decades, acoustic emission (AE) monitoring, a technique widely used in non-destructive testing, has also been introduced in orthopedics as a diagnostic tool. This review aims to synthesize the literature on the use of AE monitoring for the assessment of hip and knee joints or implants, highlighting the practical aspects and implementation considerations. Methods: this review was conducted as per the PRISMA statement for scoping reviews. All types of studies, with no limits on date of publication, were considered. Articles were assessed and study design parameters and technical characteristics were extracted from relevant studies. Results: conducted search identified 1379 articles and 64 were kept for charting. Seven additional articles were added at a later stage. Reviewed works were grouped into studies on joint condition assessment, implant assessment, and hardware or software development. Native knees and hip implants were most commonly assessed. The most researched conditions were osteoarthritis, implant loosening or squeaking in vivo and structural damage of implants in vitro. Conclusion: in recent years, AE monitoring showed potential of becoming a useful diagnostic tool for lower limb pathologies. However, further research is needed to refine the existing methods and assess their feasibility in early diagnostics. Significance: The current state of research on AE monitoring for hip and knee joint assessment is described and future research directions are identified

Acoustic analysis of the knee joint in the study of osteoarthritis detection during walking

This thesis investigates the potential of non-invasive detection of knee Osteoarthritis (OA) using the sounds emitted by the knee joint during walking and captured by a single microphone. This is a novel application since, until now, there are no other methods that considered this type of signals. Clinical detection of knee OA relies on imaging techniques such as X-radiology and Magnetic Resonance Imaging. Some of these methods are expensive and impractical while others pose health risks due to radiation. Knee sounds on the other hand may offer a quick, practical and cost-effective alternative for the detection of the disease. In this thesis, the knee sound signal structure is investigated using signal processing methods for information extraction from the time, frequency, cepstral and modulation domains. Feature representations are obtained and their discriminant properties are studied using statistical methods such as the Bhattacharyya distance and supervised learning techniques such as Support Vector Machine. From this work, a statistical feature parameterisation is proposed and its efficacy for the task of healthy vs OA knee condition classification is investigated using a comprehensive experimental framework proposed in this thesis. Feature-based representations that incorporate spatiotemporal information using gait pattern variables, were also investigated for classification. Using the waveform characteristics of the acoustic pulse events detected in the signal, such representations are proposed and evaluated. This approach utilised a novel stride detection and segmentation algorithm that is based on dynamic programming and is also proposed in the thesis. This algorithm opens up potential applications in other research fields such as gait analysis.Open Acces

Systems for Noninvasive Assessment of Biomechanical Load in the Lower Limb

Every move you make—and, yes, every step you take—is the result of action at a joint, and so proper joint function is pivotal to the way we explore and interact with the world around us. Unfortunately, joint function is often disrupted by injuries, chronic disorders, or neurological deficits, which can, in turn, disrupt quality of life. Many forms of joint dysfunction derive from adverse biomechanical loading conditions—that is, the forces and torques to which our limbs are subjected—and, thus, techniques for monitoring these loads during daily life may improve our understanding of how injuries and disorders arise and progress—and, most importantly, how best to treat them. The standard methods for assessing these loading conditions, however, are almost all benchtop-bound and confined to laboratories or clinics, so their utility in at-home or ambulatory settings—where they may be most impactful—is limited. In an attempt to address this void, in this work, we present three novel techniques for extracting information related to joint loading using a synthesis of noninvasive / wearable sensing and machine learning. First, we detail the development of an adjustable-stiffness ankle exoskeleton with multimodal sensing capabilities and use it to explore how humans interact with external elastic loading of the ankle during walking. Then, in an attempt to peer “under the skin,” we develop a novel form-factor for capturing joint sounds— the skin-surface vibrations produced by articulating structures internal to the joint—and demonstrate that these noninvasive measurements can be used to discriminate levels of axial loading at the knee. Finally, taking the concept of joint acoustics one step further, we introduce a new, active acoustics-based technique whereby the tensile loading of a particular tissue—the Achilles tendon—can be estimated by measuring the tissue’s mechanical response to a burst vibration on the skin surface. Using this approach, we are able to assess this loading state (and, by association, the net moment at the ankle) reliably across several activities of daily life, and, through a proof-of-concept study, we demonstrate how the technique can effectively translate to a fully wearable device. Collectively, the efforts reported in this thesis represent a novel, multi-path approach to assessing biomechanical loading states in the lower limb and the effects thereof. These tools and insights may serve as a basis for future development of wearable, accessible technologies for monitoring joint load during daily life, thereby reducing injury risk, tracking disease progress, assessing the efficacy of treatment, and accelerating recovery.Ph.D

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf

Musculoskeletal Diseases 2021-2024

This open access book focuses on imaging of the musculoskeletal diseases. Over the last few years, there have been considerable advances in this area, driven by clinical as well as technological developments. The authors are all internationally renowned experts in their field. They are also excellent teachers, and provide didactically outstanding chapters. The book is disease-oriented and covers all relevant imaging modalities, with particular emphasis on magnetic resonance imaging. Important aspects of pediatric imaging are also included. IDKD books are completely re-written every four years. As a result, they offer a comprehensive review of the state of the art in imaging. The book is clearly structured with learning objectives, abstracts, subheadings, tables and take-home points, supported by design elements to help readers easily navigate through the text. As an IDKD book, it is particularly valuable for general radiologists, radiology residents, and interventional radiologists who want to update their diagnostic knowledge, and for clinicians interested in imaging as it relates to their specialty