Scoliosis is a complex three-dimensional structural deformity of the human spine, and causes S/C-shape spine in the coronal plane. Surgery is often required to correct severe deformities. There is no consensus in surgery planning as scoliosis is very patient-specific. To assist with the planning, biomechanical models are greatly helpful because they can provide predictive information concerning surgery outcome. However, previous models suffer from low level of accuracy to predict the surgery outcome, especially the instrumented spine shape the major concern of patients and surgeons. This thesis developed a patient-specific multibody model of scoliotic spine for prediction of surgical correction in the coronal plane. The developed model could estimate the instrumented spine shape more accurately than previous models. As there were eight different instrumentation configurations for our patient cohort, the accurate predictions can show that our model may be able to predict the surgical correction as a function of the instrumentation configurations. The model with such capability may allow surgeons to test different instrumentation configurations and identify a better configuration for a patient. This can mitigate surgical complication risks in the current management of such complex spinal deformity.
|Qualification||Doctor of Philosophy|
|Award date||8 Aug 2016|
|Place of Publication||Singapore|
|Publication status||Published - 2016|