[[Development and evaluation of an innovative system for assessing and monitoring of inflammation in the joints of patients with rheumatoid arthritis]]
Rheumatoid arthritis (RA) affects ~1% of the global population. Disease Activity Score (DAS) 28 is an internationally recognised indicator of disease status and requires physical examination of 28 joints by a doctor, which is highly subjective and time-consuming. Ultrasonography provides a more accurate measurement of synovial inflammation, but they are expensive and require trained operators, restricting their use in the patient’s home and GP surgery. Frequent assessments are also important for monitoring disease progression and therapeutic response, but there is a practical limit to the number of times that the patients can be assessed in clinic due to time constraints. As a result clinicians can only get a brief 'snapshot' of their disease activity.
There is therefore a clinical need to develop alternative techniques that are convenient to use in the patient’s home or GP surgery, enabling patients to better self-manage their condition and reduce their need to attend hospital clinics. The PhD project with Prof Dingchang Zheng at the Medical Device and Technology Research Group and Dr Mark Lazarus (Consultant Rheumatologist), aims to develop and evaluate a cost-effective system that provides an objective assessment of disease status that correlates with current clinical indices (DAS28 and ultrasound synovial thickness). The system will also provide ongoing monitoring of disease activity to identify RA ‘flares’.
We are looking for a highly motivated candidate who has a strong interest in developing innovative healthcare technologies and has a broad understanding of physiological measurements. The PhD project requires working across multidisciplinary with clinicians, biomedical engineers, and medical device industry at different stages along the development pathway, and will lead to a patentable medical technique and high quality publications. This project will involve clinical data collection and analysis from patients, and a variety of computing work (including physiological signal processing, AI algorithms development, and statistical analysis).
How to apply
To apply, you’ll need: A first class bachelor’s degree or a 2:1 bachelor’s degree and a masters at merit level or above. Equivalent awards will be considered. Qualifications must be relevant for the particular studentship you are applying for.