Development and implementation of an artificial intelligence system for assessing corrosion damage at stem taper of hip replacement implants: A retrieval study

Author: Roohollah Milimonfared

Milimonfared, Roohollah, 2019 Development and implementation of an artificial intelligence system for assessing corrosion damage at stem taper of hip replacement implants: A retrieval study, Flinders University, College of Science and Engineering

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Despite the clinical benefits of modularity in total hip replacement (THR) implants, modular

interfaces such as head-neck taper junction are susceptible to fretting wear and corrosion due to

relative micro-motions at the interface and also the presence of corrosive body fluid. This fact

induces a chain of host body responses that may ultimately result in revision surgery to retrieve the

failed implant. Through large-scale implant retrieval studies, the damage sustained by the implants

is assessed, and possible associations between several implant/patients factors and the

extent/location of the damage are investigated.

This PhD study aims to conduct the first large-scale retrieval study in Australia through exploring a

database of 2100 operation records available at Royal Adelaide Hospital and a retrieval library of

implants with approximately the same number of implants that had been retrieved since 1980s. The

database was filtered at multiple occasions to identify implants suitable for this study.

Visual scoring of damage at taper junctions is the sole method to quantify corrosion in large-scale

retrieval studies. In this work, an intelligent image analysis-based method was developed and

implemented that can objectively assess corrosion at the stem taper of retrieved hip implants,

according to the popular Goldberg’s scoring method. A Support Vector Machine classifier was used

that takes in vectors of global and local textural features and assigns scores to the corresponding

images. Bayesian optimisation fine-tuned the hyperparameters of six binary learners of this

classifier to minimise the cross-validation error and increase the accuracy level to 85%.

Moreover, the spatial distribution and the severity of corrosion damage onto the surface of the

metallic stem tapers were objectively explored. An ordinal logistic regression model was developed

to find the odds of receiving a higher score at eight distinct zones of stem tapers. A method to find

the order of damage severity across the eight zones was introduced based on an overall test of

statistical significance. The findings showed that corrosion at the stem tapers occurred more

commonly in the distal region in comparison with the proximal region. Also, the medial distal zone

was found to possess the most severe corrosion damage among all the studied eight zones.

In the last phase of the project, several multivariate analyses of patient and implant factors were

carried out to identify the challenges regarding the causal-explanatory statistical modelling

techniques that are currently used in the literature of retrieval studies. It was elaborated why this

group of techniques are not suitable for looking at multiple confounding variables. Predictive

analytics was recommended to be utilised in conjunction with the existing methods to enable

clinicians to predict the likelihood of implants failure for prospective recipients.

Keywords: Total hip arthroplasty, Metallic implants, Machine learning, Digital image processing, Texture analysis, Regression

Subject: Engineering thesis

Thesis type: Doctor of Philosophy
Completed: 2019
School: College of Science and Engineering
Supervisor: Reza Hashemi Oskouei