Smart patches for early detection of Osteoarthritis

Arthritis Research UK states that 8.75 million people in the UK have sought treatment for osteoarthritis (OA). An early and reliable detection system for OA has clear potential to save large amounts of money on expensive diagnostic tools, such as x-ray, and greatly improve the lifestyle and health of patients.

When human joints develop OA, they can make audible grating or clicking noises during regular movement, indicating friction between bone and cartilage (Crepitus). This generally occurs during the later stages of the disease, when it is too late to intervene with treatment to arrest further disease development. Interestingly, joints and bones with earlier onset of the disease can also emit noise; however, this is confined to higher, non-audible frequencies. In OA patients, being able to detect this noise, for example in joints with small cartilage lesions or instabilities, would allow for a much earlier stage detection (for example, in a GP surgery or even in self monitoring linked to a mobile or tablet app), without expensive imaging costs and expertise. This would allow earlier intervention when the inflammatory process and the cascade degenerative process have not fully developed.

The technology to detect these signals is established in materials testing under the name of “Acoustic Emission” (AE). Biomedical research applications exist, but OA studies are limited. The PhD candidate will be exploring different approaches to the use and type of sensors and data processing techniques with the aim to develop and validate a thin, low profile “smart patch”, which will be assessed on healthy and then OA patients, alongside biomechanics analysis, dynamic fluoroscopy (a live x-ray imaging technique) and MRI as gold standard laboratory and imaging techniques. The project will explore the link between AE and the phenomena happening within the joint with the view to developing a new diagnostic device.

Candidates should hold or expect to gain a first class degree or a good 2.1 and/or an appropriate Master’s level qualification (or their equivalent).

Applicants whose first language is not English will be required to demonstrate proficiency in the English language (IELTS 6.5 or equivalent)