Date Approved

4-14-2022

Embargo Period

4-19-2022

Document Type

Thesis

Degree Name

M.S. Electrical and Computer Engineering

Department

Electrical and Computer Engineering

College

Henry M. Rowan College of Engineering

Advisor

Shreekanth Mandayam, Ph.D.

Committee Member 1

Erik Brewer, Ph.D.

Committee Member 2

Ying (Gina) Tang, Ph.D.

Committee Member 3

Amanda Almon, M.F.A.

Keywords

biomedical devices, digital image processing, machine learning

Subject(s)

Arthroplasty--Research

Disciplines

Biomedical Engineering and Bioengineering | Electrical and Computer Engineering

Abstract

Orthopedic implant procedures for hip implants are performed on 300,000 patients annually in the United States, with 22.3 million procedures worldwide. While most such operations are successfully performed to relieve pain and restore joint function for the duration of the patient's life, advances in medicine have enabled patients to outlive the life of their implant, increasing the likelihood of implant failure. There is significant advantage to the patient, the surgeon, and the medical community in early detection of implant failures.The research work presented in this thesis demonstrates a non-invasive digital image processing technique for the automated detection of specific arthroplasty failures before requiring revision surgery. This thesis studies hip implant loosening as the primary cause of failure. A combination of digital image segmentation, representation and numerical description is employed and validated on 2-D X-ray images of hip implant phantoms to detect 3-D rotations of the implant, with the support of radial basis function neural networks to accomplish this task. A successful clinical implementation of the methods developed in this thesis can eliminate the need for revision surgery and prolong the life of the orthopedic implant.

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