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Phd Proposal by Daniel Whittingslow

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Daniel Whittingslow

PhD Proposal Presentation

Date: February 13, 2018
Time: 4 PM
Location: Tech Square Research Building (TSRB) Room 509, Georgia Tech


Committee Members:
Omer Inan, PhD (Georgia Institute of Technology, Electrical and Computer Engineering) (Advisor)

Robert Butera, PhD (Georgia Institute of Technology, Biomedical Engineering)

Young-Hui Chang, PhD (Georgia Institute of Technology, Biomedical Engineering)
Sampath Prahalad, MD (Emory University School of Medicine, Department of Rheumatology)

Shelly Abramowicz, MD (Emory University School of Medicine, Division of Oral and Maxillofacial Surgery)


Title:  Acoustic Emissions Generated by Joint Articulation as a Non-invasive Biomarker for Characterizing, Screening, and Monitoring Rehabilitation of Musculoskeletal Injury and Disease.


Abstract: Musculoskeletal (MSK) injuries and diseases of the joints, such as arthritis, affect the biomechanics of joint articulation. These disorders present high burdens to patients due to the persistence of pain, difficulty with ambulation, and reduced quality of life. Diagnosis and monitoring of MSK injury and joint disease often rely on imaging and subjective clinical evaluations. Once identified, these conditions are often treated with surgery or long-term medications. After the initial diagnosis and treatment, patients’ long-term rehabilitation plans often fall short in adequately returning them to pre-injury status. This is due in no small part to the lack of quantitative metrics for assessing rehabilitation. Imaging studies such as plain field radiography, computed tomography, or magnetic resonance imaging provide the most clinically relevant information; however, repeat imaging during rehabilitation is infeasible due to the high cost, time, and restriction to a clinical setting. Therefore, there exists a need for a novel, quantitative biomarker for long-term monitoring of MSK and joint status both in and outside the clinic.

Acoustic emissions (AEs) are sounds produced by a joint during articulation. They are thought to be caused by internal joint friction that propagates to the surface of the skin causing vibrations and are heavily reliant on the biomechanical relationships of the joint. A recent study recorded the AEs from collegiate athletes’ knees before, immediately after, and post-rehabilitation of knee injuries. In this study, the AEs from the athletes’ injured knees were significantly different than their healthy contralateral knees. Furthermore, after effective intervention and rehabilitation, the AEs from the injured knee closely resembled the initial uninjured state. This finding indicates that characteristics of these AEs may be indicative of mechanical/inflammatory changes occurring within a joint, and as such could form the basis for an easily measurable, quantitative biomarker of joint health.

 

Therefore, this proposal aims to explore the use of AEs produced during joint articulation as a biomarker for overall joint health. Our lab has designed a joint AE recording hardware setup that leverages the sensitivity of commercially available uniaxial accelerometers. I plan to use this system to investigate the clinical utility of joint sounds via three aims: (1) I will investigate joint sounds in a cadaver knee model to better understand their origin and confounding factors. (2) I will explore the ability of knee joint sounds to classify the severity of arthritis and as a therapeutic efficacy monitoring tool. (3) I will analyze joint sounds produced by the temporomandibular joint (TMJ) to assess their applicability in joints other than the knees and to better understand the diagnostic/screening potential of this sensing modality. This technology has broad applications in the realms of MSK diagnosis, screening, and monitoring. This work will provide key clinical support to this nascent technology and allow for future exploration into affordable and wearable at-home monitoring of MSK and joint health.

Status

  • Workflow Status:Published
  • Created By:Tatianna Richardson
  • Created:01/25/2019
  • Modified By:Tatianna Richardson
  • Modified:01/25/2019

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