Identifying and quantifying risk factors for macular disorders using automated approaches to phenotyping
Principal Investigator: Professor Iris Heid
Approved Research ID: 33999
Approval date: June 25th 2018
Therapeutic options for age-related macular degeneration (AMD) are limited and AMD is the leading cause of blindness in elderly. Disorders of the vitreoretinal interface require extensive surgical intervention. Both diseases are therefore linked to substantial individual and public health burden. We aim to contribute to improve the knowledge of the causes of these diseases. We approach this by using observational data to understand risk factors that increase the susceptibility for these diseases. These diseases can be diagnosed via fundus photography and optical coherence tomography (OCT). The UKBiobank data including data from these imaging techniques are ideal to investigate association between risk factors and diagnosis. One of the predominant challenges of these imaging data is the huge effort involved when manually analyzing these images. New automated approaches are available (deep learning). However, the performance of such automated approaches to yield high quality diagnosis compared to manual diagnosis is not known for these diseases. Specifically, we aim to apply deep learning algorithms for AMD and vitreoretinal boarder disorders that are already developed to UK Biobank data in order to evaluate the accuracy. These algorithms will be applied to the fundus photos and OCT images of UKBiobank. We will also manually grade images for a subgroup of persons comparing results with the automated diagnosis. We will analyze the association of genetic factors together with lifestyle and metabolic parameters with these retinal diseases. The expected Project Duration is one year. We expect our results to contribute to an improved understanding of the causes of retinal diseases, which will ultimately help to develop new and better therapeutic Options.