SEVERITY GRADING AND BIOMARKERS FOR RETINAL VASCULITIS

Retinal vasculitis is an important component of inflammatory eye disease or uveitis, as well as a serious adverse effect of new intravitreal treatments. It can cause visual loss through tissue swelling (macular oedema), ischaemia and neovascularisation.

This PhD is for a programme of research in retinal vasculitis using clinical research in ophthalmology, retinal image analysis and artificial intelligence (AI) to

1) validate a novel severity grading scheme

2) develop multimodal non-invasive imaging in retinal vasculitis

3) develop automated grading of retinal vasculitis using AI.

1) The PhD student will investigate and validate a novel grading of wide-field fluorescein angiograms in retinal vasculitis before and after treatment. To do this they will utilise in-house imaging data and data from a multicentre, randomised controlled trial in uveitis currently recruiting. They will investigate how the novel severity grading can map the responsiveness of retinal vasculitis to steroid and adalimumab treatment, validating its use in trials and clinical practice.

2) The PhD student will investigate the use of optical coherence tomography (OCT), OCT angiography and other retinal imaging modalities as non-invasive evaluation of retinal vasculitis. OCT enables peri-and intravascular inflammation to be visualised with high resolution but its use in retinal vasculitis has not previously been critically examined or optimised. OCT angiography demonstrates retinal vessels in the central macula in great detail but does not provide information on vessel integrity. The student will investigate whether OCT with or without other imaging modalities could replace invasive fluorescein angiography for assessing retinal vasculitis.

3)    Our research group has done initial development of an automated AI grading of retinal vasculitis with the ultimate aim of developing a continuous scale of retinal vasculitis including important spatial data, rather than an ordinal scale. The student will work on ground truth development and, depending on the candidate, algorithm development for grading severity of retinal vasculitis. There is a very strong AI image analysis in healthcare group in Eye and Vision Science with expertise in developing AI image analysis in eye care.

The PhD student will benefit from expert and experienced supervision, as well as the supportive environment of University of Liverpool Department of Eye and Vision Science. Personal development and research learning are priorities for PhD students. Research in the clinical environment is supported by the Clinical Eye Research Centre within St Paul’s Eye Unit, Royal Liverpool University Hospital.