Combining deep learning and mechanistic modelling to automate the interpretation of clinical retinal imaging

Increasing diabetes incidence and an aging population have placed an unprecedented burden on clinical ophthalmology. With an anticipated 60% increase in demand for services over the next twenty years, this will necessitate new ways of working. Equally, a range of new and improved retinal imaging technologies have been developed that have the potential to improve both patient monitoring and diagnosis, but which require in-depth expert interpretation. To address these challenges, this project will combine mechanistic, biophysical modelling of the retina with machine learning tools to develop a software platform to assist ophthalmologists with the interpretation of image data from wide-field, colour retinal photography and optical coherence tomography angiography (OCT-A). The platform will also be used for treatment planning and optimisation.