Study level

  • Vacation research experience scheme


Topic status

We're looking for students to study this topic.


Associate Professor Saulo Martelli
Associate Professor in Biomedical Engineering and Biomechanics
Division / Faculty
Faculty of Engineering
Professor Peter Pivonka
Professor and Chair of Biomedical Engineering and Spinal Disorders
Division / Faculty
Faculty of Engineering

External supervisors

  • Dr Ashish Gupta, Greenslopes Private Hospital


The challenge of conducting a clinical trial to test the performance of joint replacement prosthetics in-vivo results in an expensive and risky lead time for the development of new implant designs. Computational methods have the potential to significantly improve the workflow of implant design via in-silico clinical trials.

Anatomical variation in bone morphology across populations also presents a challenge, and new prosthetics will need to be offered in a variety of shapes and sizes to maximise their suitability for a diverse range of candidate patients.

Statistical shape modelling offers a tool for capturing population variation by generating a vast array of statistically valid bone shapes that can be analysed computationally using the finite element method.

Our research project aims to combine medical imaging, statistical shape modelling and FEA methods to investigate the performance of prosthetic implants used in shoulder joint replacement. By testing implant performance across a population, using in-silico computational methods, we expect that the lead time for developing new and novel implants can be reduced. This will result in greater outcomes for patients suffering from diseases such as osteoarthritis where joint replacement offers a significant improvement in quality of life.

Research activities

During this research project, you'll be involved in:

  • segmentation of humerus and scapula bones from CT scans
  • developing three-dimensional subject-specific computational bone models
  • FEA of intact human bone models
  • FEA analysis of novel implant designs under different biomechanical loads.


Upon conclusion of the research, we expect to have developed:

  • statistical shape and density models of the human shoulder that represent a population
  • a computational tool to test new and novel implant designs in-silico using FEA methods
  • a framework to assist clinicians in the implant selection process.

Skills and experience

To be considered for this research, you should have a background in one or more of the following:

  • biomedical engineering
  • mechanical engineering
  • segmentation and image processing.



Contact the supervisors for more information.