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.
The proposed project will improve machine learning techniques for predicting and detecting glaucomatous change in patient eyes tested longitudinally by visual field and optical imaging instruments and will make use of a very large amount of data, obtained using previously awarded NIH funds, to do so. This proposal addresses the current NEI Glaucoma and Optic Neuropathies Program objectives of developing improved diagnostic measures to characterize and detect optic nerve disease onset and characterize glaucomatous neuro- degeneration within the visual pathways at structural and functional levels. The development of a clinically useful novel, empirical system for predicting and detecting glaucomatous progression can have a significant impact on the future of clinical care and on the future of clinical trials designed to investigate IOP lowering and neuroprotective drugs.
|Christopher, Mark; Belghith, Akram; Bowd, Christopher et al. (2018) Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs. Sci Rep 8:16685|
|Bowd, Christopher; Zangwill, Linda M; Weinreb, Robert N et al. (2018) Racial Differences in Rate of Change of Spectral-Domain Optical Coherence Tomography-Measured Minimum Rim Width and Retinal Nerve Fiber Layer Thickness. Am J Ophthalmol 196:154-164|
|Ghahari, Elham; Bowd, Christopher; Zangwill, Linda M et al. (2018) Macular Vessel Density in Glaucomatous Eyes With Focal Lamina Cribrosa Defects. J Glaucoma 27:342-349|