Optimising treatment for scoliosis correction using computational modelling and data analysis


Scoliosis is a 3-dimensional deformity of the spine that develops in child/adolescent life and can deteriorate during skeletal growth and especially in periods of rapid skeletal development such as around puberty. The overall prevalence has been reported to be between 0.5-5%. Scoliosis can severely affect the quality of life of many patients; it may lead to multiple impairments and creates a health inequality, requiring treatment. Females are 8 times more likely to progress to a curve that requires treatment in comparison to males. There is no consensus among spinal surgeons on the use of correction techniques in the treatment of scoliosis. For example, some surgeons prefer approaches which are thought to provide good scoliosis correction though they may pose long-term risks in comparison to lower risk approaches that may provide comparable results (e.g. all pedicle screw correction compared to hybrid hook/screw instrumentation correction). Similarly there are many other questions related to treatment and correction techniques that remain debated and can be answered using computational modelling.


  1. Develop computational models using the imaging data (CT and MRI) to simulate the anatomy of an adolescent scoliotic spine across the different types of curves which represent the common patterns seen in clinical practice. 
  2. Undertake modelling studies to evaluate intra-operative corrective forces required in scoliosis correction of the spine to answer a range of clinical questions.
  3. Compare the computational results with patient reported outcomes using the Scoliosis Research Society (SRS-22) validated questionnaire for adolescent idiopathic scoliosis.
  4. Develop software to recommend an optimum correction approach for a given clinical scenario.

Learning outcomes for the student

The project will enable the PhD student to:

  1. Present and critique the state-of-art with respect to scoliosis and its surgical treatment approaches.
  2. Develop complex geometry of the scoliotic spines from 3D scan data.
  3. Describe the mathematical constitutive models of components involved in modelling. 
  4. Describe loadings experienced by the spine due to different correction approaches and instrumentations.
  5. Learn to generate, curate and analyse large datasets.
  6. Master techniques for data analysis obtained from computational modelling, imaging and patient reported outcomes.
  7. Work in a team and become self-reliant for acquiring resources required for research.
  8. Present research in international conferences and write journal papers.

Further Information: 

The selected candidate will be jointly supervised by engineers and clinicians and will be part of the Edinburgh Computational Biomechanics Group: https://ecbm.eng.ed.ac.uk/home

The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity

Closing Date: 

Monday, May 16, 2022

Principal Supervisor: 

Assistant Supervisor: 


The candidate should have an undergraduate or a master’s degree in Engineering or Physics. They should have had undertaken courses in solid mechanics and finite element modelling. Desirable skills include coding experience in programming languages such as Fortran, Python, Matlab, C/C++.

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.


Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.

Strong candidates may be considered for full funding (Tuition fees + stipend) - open to UK/EU candidates.

Further information and other funding options.

Informal Enquiries: