Background
Glaucoma is the first cause of blindness worldwide, which is a sophisticated multi-variable disease that engenders structural and functional variations. Glaucoma in terms of genetic analysis has been hindered due to the complicated structure of the retina layers and the limitation of the computation resources. The retina is a deeply complex structure of the eye that is responsible for receiving incoming light and providing some pre-processing and sending that information to the brain. It comprises several tissue layers that are involved in the process of the input light. In a type of glaucoma disease, any layers of the retina can be affected which makes it important to have a precise understanding of each layer in the retina. Optical Coherence Tomography (OCT) is an innovative and non-invasive imaging technique that plays an instrumental role in retinal diagnostics. It operates based on the principles of interferometry to capture high-resolution cross-sectional images, often termed ’optical biopsies’. OCT imaging provides us with the ability to clearly differentiate between various stratified layers of the retina. It achieves this by measuring the echo time delay and magnitude of backscattered light, thereby creating a detailed map of the retinal structure with 5-10 micrometers accuracy that is sufficient to identify subtle morphological changes in the retina which could be indicative of early-stage ocular diseases. Consequently, OCT has emerged as an invaluable tool in both the clinical and research contexts, contributing significantly to our understanding of the retina and its associated pathologies. Due to the complexity of the OCT images and the limitation of computational resources, precise analysis of the OCT images was still impossible. However, artificial intelligence (AI) has recently empowered researchers for more complex analysis in all areas and in fact, it has become an inseparable part of ophthalmology nowadays. The AI models help us to extract the features behind the data with high dimensionality like 3D medical images. Also, thanks to recent computational resources like graphical processing units (GPUs), more dense datasets can be analyzed with a better understanding of the feature variations in complex organs like the retina. However, the use of AI algorithms has been limited by the data annotation by experts [3] while large-scale biobank studies with multi-modal data have been started (e.g., UK biobank [4]). This is useful for discovering hidden structural patterns of OCT-derived macular thickness layers using deep learning (DL) among subjects with high polygenic risk (PRS) for primary open-angle glaucoma (POAG). Subsequently, we use these endophenotypes to discover novel genomic loci.
What We Do
Our team proposes to uncover the hidden structural patterns of OCT-derived retina layers with two AI models. These patterns are useful for further genetic-wide analysis studies (GWAS). The proposed AI models then group semantically similar images. We also provide an unsupervised evaluation method that sets a ground truth for clustering evaluation and can be used for determining the number of clusters. We also investigate the derived endophenotypes for the longitudinal images to see if there is a correlation between the patterns and also the progress of glaucoma among the patients.
Selected Publications
- Zebardast, N., Fazli, M., Sekimitsu, S., Hashemabad, S.K., Elze, T., Segrè, A.V., Wang, M. and Wiggs, J.L., 2022. Deep unsupervised discovery of OCT phenotypes enables genome-wide analyses. Investigative Ophthalmology & Visual Science, 63(7), pp.1844-1844.