Designing Early Detection AI Diagnostic Tools for Age-related Macular Degeneration

Description

This studentship provides multidisciplinary training across retinal physiology, pathophysiology, retinal image interpretation, and application of artificial intelligence^{1,2,3,4,5,6,7} and would suit an individual with experience in programming, artificial Intelligence, and computer science, ideally with a biological background.  

Age-related macular degeneration (AMD) is the most common cause of blindness in industrialized countries and one of the leading causes of vision loss in the UK. AMD is a chronic disease that affects the retina at the back of the eye and is typically diagnosed in late stages using Optical Coherence Tomography (OCT), that provides high resolution cross-sectional images of retinal morphological changes, when treatment is often irreversible.

The retinal pigment epithelium (RPE) is thought to play a major role in the onset and progression of AMD. The RPE consists of typical simple polarized epithelial cells with cuboidal morphology, connected by tight junctions (TJ)^8,9,10. The RPE, along with the Bruch’s membrane, is positioned between photoreceptors and the vascular choroid, with which it forms a complex, and is central for many processes ranging from barrier function to the daily removal of spent photoreceptor outer segments through phagocytosis^11,12.

Using powerful 3D in vitro mimetic retinal tissue models, the interdisciplinary team are investigating the molecular mechanisms that control RPE morphology and function to identify molecular targets and develop treatments for early AMD. Novel early treatment methods will necessitate more sensitive diagnostic tools to identify and treat the disease early. Since changes in RPE morphology, polarity and/or adhesion during AMD affect the entire RPE-choroid complex the postgraduate researcher (PGR) will develop automated computer-aided technologies based on deep learning using OCT images from early AMD patients, to identify morphological changes to the RPE-choroid complex to help diagnose the disease early.

The PGR will receive training in retinal biology, and to develop methodology for the automated localization of relevant retinal landmarks in control and AMD patients, design of automated segmentation frameworks to segment the anatomic landmarks including the RPE-choroid areas in both patients and state-of-the art in vitro 3D retinal tissue mimetic models. In addition, the successful candidate will undertake a short placement at the University of Newcastle, supervised by Professor Linda Lako, to gain further experience in retinal mimetic models to further extend their interdisciplinary training. 

The project offers a unique opportunity to work with supervisors with distinct expertise (Zihni -Molecular Cell Biology, Stem Cells, 3D in vitro Retinal Disease Models; Zheng - Artificial Intelligence, Image Processing, Computer Vision, Machine Learning and Medical Image Analysis; Madhusudhan- Clinical Ophthalmology, Medical Imaging) to find solutions for one of the major global age-related diseases.

Benefits of being in the DiMeN DTP:

This project is part of the Discovery Medicine North Doctoral Training Partnership (DiMeN DTP), a diverse community of PhD students across the North of England researching the major health problems facing the world today. Our partner institutions (Universities of Leeds, Liverpool, Newcastle, York and Sheffield) are internationally recognised as centres of research excellence and can offer you access to state-of the-art facilities to deliver high impact research.

We are very proud of our student-centred ethos and committed to supporting you throughout your PhD. As part of the DTP, we offer bespoke training in key skills sought after in early career researchers, as well as opportunities to broaden your career horizons in a range of non-academic sectors.

Being funded by the MRC means you can access additional funding for research placements, international training opportunities or internships in science policy, science communication and beyond. See how our current DiMeN students have benefited from this funding here: https://www.dimen.org.uk/blog 

Further information on the programme and how to apply can be found on our website: https://www.dimen.org.uk/how-to-apply