Our vision in this project is to create a computational framework for the proximal femur that can allow clinicians to take limited clinical imaging data, e.g. single X-rays, and quickly generate bone strength analyses or custom implants suited to individual patients. The framework can become an integral part of the patient record simplifying monitoring and tailoring of treatments.
Musculoskeletal diseases such as osteoporosis, osteoarthritis, or bone metastases are a significant personal and socio-economic burden affecting approximately 20% of UK’s inhabitants and millions of patients in ageing societies world-wide. These diseases lead to decreased quality of life, lowered productivity, increased mortality and cause significant annual costs. For example, osteoporosis alone results in approximately 70,000 hip fractures amongst over 300,000 fragility fractures annually in the UK causing £2 billion associated costs and this burden is expected to quadruple over the next decades as UK’s society is rapidly ageing. While pharmacological osteoporosis treatments exist, they are insufficient in a significant number of patients. Moreover, no effective pharmacological treatment is available for osteoarthritis or bone metastases so that implants are often the only option. While life expectancy continues to rise, patient specific treatment solutions to optimally manage those patients are still not available.
One solution consists of tailored medication strategies and, at a later stage, tailored implant solutions. Tailoring treatment solutions based on personalised computational bone strength assessments or personalised implants could help to lower the socio-economic burden and sustain quality of life. However, they require design and analyses strategies that consider cells, vessels, nerves, and the support structure of the surrogate to match the specific organ. The necessary computational framework to do that does not currently exist and this proposal addresses this.
For the proximal femur, we aim to develop a computational framework that is described as four elements: (i) data from limited clinical imaging data, e.g. clinical X-rays, to generate (ii) finite element (FE) models that allow us to “zoom” through the structure based on (iii) non-linear material models that are combined with personalised, multiscale representations of bone from fibril to organ and which allow it to (iv) incorporate epidemiologic data and rates of pathologic change. We envision that this will form part of the patient record so that regular analyses for prospective changes as well as triggering care could be automated in addition to tailor treatments.
Computational models could be a cornerstone in digital healthcare to tackle the socioeconomic burden associated with bone-related diseases such as osteoporosis, osteoarthritis and bone cancer. This includes improved diagnoses, the manufacturing of custom implants and the monitoring of bone implant systems in personalised medicine. If successful, the project will have direct, positive impact on realising this vision.
All applicants must have or expect to have a 1st class MChem, MPhys, MSci, MEng or equivalent degree by Autumn 2020. Selection will be based on academic excellence and research potential, and all short-listed applicants will be interviewed (in person or by Skype). This scholarship is only open to UK/EU applicants who meet residency requirements set out by EPSRC.
All applications must be received by 28th February 2020. All successful candidates should usually expect to start in September/October 2020.
How to Apply
Apply Online - https://hwacuk.elluciancrmrecruit.com/Admissions/Pages/Login.aspx
When applying through the Heriot-Watt on-line system please ensure you provide the following information:
(a) in ‘Study Option’
You will need to select ‘Edinburgh’ and ‘Postgraduate Research’. ‘Programme’ presents you with a drop-down menu. Choose Mechanical engineering PhD, chemical engineering PhD or Bio-engineering and Bio-sciences PhD and select September 2020 for study option (this can be updated at a later date if required)
(b) in ‘Research Project Information’
You will be provided with a free text box for details of your research project. Enter Title and Reference number of the project for which you are applying and also enter the supervisor’s name.
This information will greatly assist us in tracking your application.
Please note that once you have submitted your application, it will not be considered until you have uploaded your CV and transcripts.