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  Clinical assessment and prediction of outcome post shoulder joint replacement using a smart device


   School of Physics, Engineering and Technology

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

More than 8000 shoulder replacements are performed annually in the UK alone, with numbers continuing to grow. While they are effective at improving shoulder pain and function caused by joint arthritis, routinely collected patient-reported outcome measures (PROMs) suggest some patients may not benefit from the procedure, and the risk of serious adverse events following shoulder replacement may be higher than previously thought.

Project vision

This research will develop a tool capable of collecting patient outcome data and providing clinicians with a risk estimate for reoperation following shoulder replacement. Currently no technology can achieve this aim. A smart phone app will be developed to collect PROMs (Oxford Shoulder Score, EQ-5D) and functional outcome measures. This will make use of the devices inbuilt sensors to quantitatively measure the patients functional shoulder movement during standardised activities. The technology will be developed and validated using marker based motion capture in the schools biomechanics lab. The resulting data collection app and predictive model will be embedded into an end user interface to construct a ‘point-of-care’ forecasting calculator. Importantly this will designed to integrate into the existing IT infrastructure and patient treatment pathway. Function of the system will then be validated through pre-clinical testing.

Timeliness

The global incidence of shoulder replacements is rising at a rapid rate with some countries reporting up to a 17- fold increase over the last 10 years. While shoulder replacements are generally shown to be effective at improving shoulder pain and function caused by joint arthritis, patient-reported outcome measures (PROMs) suggest some patients may not benefit from the procedure, and the risk of serious adverse events following shoulder replacement may be higher than previously thought.

Experimental Approach

Effect size of risk factors associated with adverse events will be identified for different treatment paths assessed. This analysis will be based on pre-existing patient level data obtained from the National Joint Registry. The key outcome from this work will be a regression model indicating effect sizes of different risk factors.

Supervisors

Dr Peter Ellison, Lecturer in Clinical Engineering, School of PET, University of York

Prof A. Rangan, HYMS & Academic Centre for Surgery, University of York

Collaborators

This project will work with clinic partners from Academic Centre for Surgery, HYMS, South Tees Academic Centre for Surgery, University of Oxford, University of Sheffield and University of Cambridge.

About Movement Science and Engineering at York

Movement Science and Engineering is a subgroup of Healthcare Engineering at UoY. We currently consist of 10 academics focused on research into movement science and restoration of movement to patients. Research topics cover a diverse range of skills, from development of MEMS sensors for study of proteins, to AI based signal analysis for automated assessment of neuro and MSK degenerative conditions, and human machine interactions with assistive robotics.

About UoY

The University of York takes immense pride in its placement in the top ten UK universities in the REF, affirming our commitment to research excellence with social impact. As a University for the Public Good, we strive to establish strong partnerships and share knowledge to create local and global benefits. The overarching ambition of this project and its potential impact on Healthcare Engineering align perfectly with our principles of inclusion, internationalism, and collaboration.

How to Apply:

Applicants should apply via the University’s online application system at https://www.york.ac.uk/study/postgraduate-research/apply/. Please read the application guidance first so that you understand the various steps in the application process.

Candidates should have (or expect to obtain) a minimum of a UK upper second-class honours degree (2.1) or equivalent in Mechanical or Electrical Engineering. A candidate with a master’s degree (or expected to graduate) is also desired for this studentship, particularly with merit grades in Mechanical Eng, Electronics, and related disciplines. Experience in biomechanics or computer science will also be considered, however you must show an understanding of fundamental principles of experimental robotics mechanics, dynamics and control, and signal processing.


Engineering (12)

Funding Notes

This is a self-funded project and you will need to have sufficient funds in place (eg from scholarships, personal funds and/or other sources) to cover the tuition fees and living expenses for the duration of the research degree programme. Please check the School of Physics, Engineering and Technology website View Website for details about funding opportunities at York.

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