Learning anatomy of the spine using Foundation Models (SpineFM)

Project summary

Over one-third of the UK population lives with a musculoskeletal condition affecting the joints or spine. Among them, axial Spondyloarthritis (axSpA) is a progressive and destructive form of arthritis that affects 1 in 200 of the adult population 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 as Magnetic Resonance Imaging. Advances in Artificial Intelligence (AI), including foundation models such as Segment Anything or its medical variants, now open the opportunity to learn the anatomy of the spine (and thus any damages to it) using radiographs. The project will aim to investigate the state-of-the-art foundation models that could be used for the detection of individual vertebrae and then to identify those which appear to be normal or abnormal from spine X-ray imaging.

The project may also involve modifying the existing foundation models to implement a bespoke foundation model specialized in spinal imaging (SpineFM), and writing up the report with a future publication in mind.

Timescale

Up to 12 weeks.

Day-to-day supervision

Dr Bartek Papiez.

Suitability

The ideal candidate will have:

- Strong programming skills (preferable Python)

- Experience or interest in machine learning (Deep Learning) and medical image analysis (in particular radiological imaging)

- Experience or enthusiasm to work with clinicians.