Glaucoma is a complex neurodegenerative disease that results in degeneration of retinal ganglion cells and their axons. With older people making up the fastest growing part of the US population, glaucoma will become even more prevalent in the US in the coming decades. Due to the complex interaction of multiple factors in glaucoma, better structural and functional predictors are needed for its progression. The main impediments are massive health record data and sophisticated computational models. Our overall goal is to leverage the power of big data and rapidly evolving machine learning approaches. The NEI's ?Big Data to Knowledge (BD2K)? initiative and the American Academy of Ophthalmology Intelligent Research in Sight (IRIS) registry are all efforts to exploit the power of data and to better understand diseases and to provide improved prevention and treatment. In this multi-PI proposal, we offer to assemble over 1 million optical coherence tomography (OCT) and visual fields (VFs) from the glaucoma research network (GRN). We propose to develop a hybrid artificial intelligence (AI) algorithm that synthesizes Gaussian mixture model expectation maximization (GEM) and archetypal machine learning approach to identify glaucoma progression and its monitoring using VFs and retinal nerve fiber layer (RNFL) thickness measurements. We will make these tools openly available to the vision and ophthalmology research communities. Our proposed studies could offer substantial improvements in the prognosis of glaucoma as well as potentially providing OCT and joint VF/OCT surrogate endpoints to be used in glaucoma clinical trials.
Leveraging big data in eye care is challenging. This study uses big functional and structural glaucoma data and develops hybrid machine learning models to identify glaucoma progression and its monitoring. Results could offer substantial improvements in prognosis of glaucoma and may provide surrogate endpoints for use in glaucoma clinical trials.
