James Battye
Research Topic: Predicting knee replacement using temporal MRI data from the Open Arthritis Initiative - a 45000 MRI scan dataset
Supervisors: Samuel Relton, Nishant Ravikumar, Asra Aslam
About James: James graduated with a master's degree in physics from the University of Bristol where his research focused on measuring the mass of distant galaxy clusters. Following this, he worked as a data analytics consultant at KPMG developing analytics solutions for a range of clients. He then joined Leeds Institute for Data Analytics (LIDA) at the University of Leeds as a data scientist. At LIDA, he conducted collaborative research alongside external partners centred on agent-based modelling, digital twins and geospatial analysis.
Project Description: Osteoarthritis (OA) is the most common form of arthritis, with incidence doubling in the past 20 years. In the UK, one-third of women and one-quarter of men aged 45-60 seek treatment for OA, rising to nearly half in those 75 and older. OA costs the NHS £10 billion annually, excluding wider economic impacts. These costs are expected to rise with an aging population. Knee OA is the most common form of OA in the UK.
Joint pain is the main clinical issue in OA. There are few effective treatments with significant side effects which often leads to knee replacement surgery. Over 100,000 such surgeries are performed annually in the UK, and this number is growing. Early risk assessment using MRI scans could allow for introduction of preventative measures to reduce symptoms and long-term risk of surgery.