Mr Alex Swee
I was awarded a Bachelor of Engineering with Honours specialising in Biomedical Engineering by the University of Auckland in 2015. During my undergraduate study, I was involved in an ABI summer research project investigating how biomechanics may influence gout, supervised by Justin Fernandez. For my fourth year project, I modelled the sheep spine to be used as a tool for orthopaedic evaluation, supervised by Justin and Vickie Shim (my current supervisors) and also Dr Jacob Munro.
Research | Current
Bone integration in anterior lumbar inter-body fusion (ALIF) spinal implants
Spinal fusion implants are designed to integrate with bone and are typically tested in animal models such as sheep. However, there is a lack of understanding of how osseointegration is influenced by the range of anatomically based loads in vivo including from muscle forces and different tasks. The restricted movement also gives rise to bone degeneration in adjacent discs.
If the implant is too stiff this leads to stress shielding, bone loosening and revision surgery. Recent composite materials have lower stiffness to reduce the stress shielding problem, but lead to increased shear stress at the bone implant interface. A solution is to produce a mechanically graded implant with varying material properties informed by a more realistic loading environment.
Ossis are designing next-generation custom anterior lumbar inter-body fusion (ALIF) implants for the spine incorporating optimised trabecular scaffold architecture. In this project, we will develop an osteointegration modelling pipeline validated in a sheep model, to evaluate:
- the consequences of Ossis’ design decisions on the performance on the ALIF implants, and;
- whether Ossis’ custom ALIF implants improves osseointegration over existing fusion technology, and reduces bone resorption in adjacent intervertebral discs.
Our modelling pipeline will be evaluated using 12 sheep with CT, BMD and histology after scaffold harvest. We will then assess how this is translated to the human condition by using an existing population of human spine models.