Spinal fixation is an intricate area of spinal surgery, with positive patient outcomes dependent on careful surgical planning. Appropriate pre-operative choices, such as the selection of implants utilised, must be balanced by factors such as patient comorbidities, which can complicate decision making and lead to unfavourable outcomes such as failed procedures.
This research aims to serve as the bedrock for the development of a novel pre-operative planning tool for spinal fixation surgeries. Our vision is to provide clinicians with the ability to quantify the biomechanical effects of applying different surgical interventions to anatomically accurate digital models of patients’ spines, thus allowing them to better personalise treatment based on the characteristics of the patient. The aim of such a pre-operative tool would be to better inform surgical planning and optimize the parameters of surgical interventions chosen by clinicians, ideally leading to an improvement in patient outcomes.
This project will establish the foundation for our vision by utilising Finite Element Analysis (FEA) to generate anatomically accurate computational models of patients’ spines. The broad aims of this project are twofold. Firstly, to refine and optimise the modelling process of the spine and secondly, to validate the use of these models as a pre-clinical tool. The latter aim will be achieved by retrospectively analysing a series of patients with failed spinal fixation procedures, and using our pre-operative models to determine if alternative surgical parameters, such as the use of different pedicle screw lengths, would have resulted in better patient outcomes. The key outcomes of this project will then directly inform the viability of using such modelling on a larger cohort, and the potential implementation of artificial intelligence (AI) techniques to refine our modelling system thereafter.