Machine Learning Methods for Detecting Disease-related Functional and Structural Change in Glaucoma

University Of California, San Diego, La Jolla CA

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Abstract

PROJECT SUMMARY This project aims to apply novel machine learning techniques to recently developed optical imaging measurement to improve the accurate prediction and detection of glaucomatous progression. Complex functional and structural tests in daily use by eye care providers contain hidden information that is not fully used in current analyses, and advanced pattern recognition/machine learning-based analysis techniques can find and use that hidden information. We will use mathematically rigorous techniques to discover patterns of defects and to track their changes in longitudinal series of perimetric and optical imaging data from up to 1,800 patient and healthy eyes, available as the result of long-term NIH funding. We also will investigate deep learning and novel statistical techniques for this purpose. The required longitudinal measurements from several newly developed optical imaging techniques were not available to our previously funded NEI- supported work. The proposed work potentially can enhance significantly the medical and surgical treatment of glaucoma and reduce the cost of glaucoma care by informing clinical decision-making based on mathematically based, externally validated methods. Moreover, improved techniques for predicting and detecting glaucomatous progression can be used for refined subject recruitment and to define endpoints for clinical trials of intraocular pressure-lowering and neuroprotective drugs.