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Modelling and prediction of spinal injury in rugby

Department for Health

Dr Dario Cazzola Sunday, March 28, 2021 Funded PhD Project (European/UK Students Only)
Bath United Kingdom Biomedical Engineering Data Analysis Medical Physics Computer Science Sport & Exercise Science

About the Project

The University of Bath is inviting applications for the following funded PhD project supervised by Dr Dario Cazzola (Department for Health), Dr Neill Campbell (Department of Computer Science) and Prof Keith Stokes (Department for Health).

Funding is available to candidates who qualify for Home fee status. Following the UK’s departure from the European Union, the rules governing fee status have changed and, therefore, candidates from the EU/EEA are advised to check their eligibility before applying. Please see the Funding Eligibility section below for more information.

The successful student will be part of the Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA) which performs world-leading multi-disciplinary research in Intelligent Visual and Interactive Technology. Funded by the EPSRC and the University of Bath, CAMERA exists to accelerate the impact of fundamental research being undertaken at the University in the Departments of Computer Science, Health and Psychology.

Overview of the Research:

Spinal catastrophic injury in rugby activities (e.g. scrums and tackles) are not very common but can be very impairing (e.g. tetraplegia, or quadriplegia) and generate high societal costs. Governing bodies, such as the World of Rugby, have successfully minimised such injuries during scrummaging by changing the game rules. These changes were informed by biomechanical analysis carried out at University of Bath. Unfortunately, rugby tackling still remains a very risky part of the game and a cause-effect relationship between tackling technique and resulting injuries is still lacking. 

The aim of this projects is to unveil such relationship and causally link specific tackling situations to injury diagnoses. This will be achieved by using probabilistic, generative machine learning to represent the probabilistic relationship between qualitative and quantitative tackling features and injury diagnoses. This is a great opportunity to extend the state-of-the-art in machine learning whilst working directly with domain experts to provide real-world insights and benefits in the fields of bio-mechanics and sports injury prevention.

Qualitative data will include description of injurious events gathered via interviews with injured players, whilst quantitative data will be retrieved from the simulation of the same injuries in a computational framework using musculoskeletal models of the spine. The simulation framework will be firstly validated against imaging data (e.g. x-rays and MRIs) of real-world spinal injuries, and then used to create a set of ‘synthetic’ data of theoretical injurious scenarios. The ‘synthetic’ datasets will include quantitative information of vertebrae motion, and forces applied to the spine during injurious events, which is key to understand the injury mechanisms and compare the simulated injuries with real injury diagnoses and medical images. The final step will consist in the creation of the probabilistic model that will use as input the qualitative descriptors of tackling scenarios and quantitative simulation results, and will provide the most likely injury diagnosis. 

Candidate Requirements:

Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree (or the equivalent) in a relevant subject, such as Engineering, Physics, Maths, Computer Science or Sport Science. A master’s level qualification would also be advantageous. In addition, applicants should have a very good proficiency in Python or Matlab (required), C++ (desirable).

Non-UK applicants must meet our English language entry requirement.

Enquiries and Applications:

Informal enquiries are welcomed and should be directed to Dr Dario Cazzola ().

Formal applications should be made via the University of Bath’s online application form for a PhD in Health.

More information about applying for a PhD at Bath may be found on our website.

Funding Eligibility:

In order to be considered for the available studentship, you must qualify as a Home student.  In determining Home student status, we follow the UK government’s fee regulations and guidance from the UK Council for International Student Affairs (UKCISA). Further information may also be found within the university’s fee status guidance.

EU/EEA citizens who live outside the UK are unlikely to be eligible for Home fees and funding.

Funding Notes

A studentship includes Home tuition fees, a stipend (£15,609 per annum, 2021/22 rate) and research/training expenses (£1,000 per annum) for up to 3 years. Eligibility criteria apply – see Funding Eligibility section above.


• Cazzola D et al. Cervical Spine Injuries: A Whole-Body Musculoskeletal Model for the Analysis of Spinal Loading. PloS one. 2017;12(1):e0169329. doi: 10.1371/journal.pone.0169329. PubMed PMID: 28052130; PubMed Central PMCID: PMCPMC5214544.
• Cazzola, et al. Musculoskeletal modelling of the human cervical spine for the investigation of injury mechanisms during axial impacts. PloS one. 2019;14(5):e0216663. doi: 10.1371/journal.pone.0216663. PubMed PMID: 31071162.
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