Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Over one third of the UK population live with a musculoskeletal condition affecting the joints or spine. Amongst them, Psoriatic arthritis (PsA) and axial Spondyloarthritis (axSpA) are progressive and destructive forms of arthritis that affect 320,000 people in the UK. In the current diagnostic pathway, radiographs (X-ray imaging) remain the mainstay of damage assessment as they are accessible, quick, require minimal radiation and are low cost compared to other imaging modalities such Magnetic Resonance Imaging.  Advances in Artificial Intelligence (AI) now open the opportunity to automate quantification of damage on radiographs. Machine Learning, and in particular Deep Learning (DL), has enabled artifact detection, grading medical imaging, and detection of pathological conditions.

The project will aim to investigate the state-of-the-art computer vision object detection methods that could be used for detection of new bone formation (osteophytes) in spine X-ray imaging.

The project may also involve modifying the existing object detection methods to implement a bespoke AI/DL model for osteophytes detection, and writing up the report with a future publication in mind.                                                      

duration

8-12 weeks, from 19 June onward.

Primary supervisor available during the project

Our team