This project aims to improve glaucoma management by applying novel pattern recognition techniques to improve the accurate prediction and detection of glaucomatous progression. The premise is that complex functional and structural tests in daily use by eye care providers contain hidden information that is not fully used in current analyses, and that advanced pattern recognition techniques can find and use that hidden information. The primary goals involve the use of 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 1800 glaucomatous and healthy eyes, available as the result of long-term NIH funding. With the interdisciplinary team of glaucoma and pattern recognition experts we have assembled, with our extensive NIH-supported database of eyes, and with the knowledge we have acquired in the optimal use of pattern recognition methods from previous NIH support, we believe the proposed work can enhance significantly the medical and surgical treatment of glaucoma and reduce the cost of glaucoma care. 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.

Public Health Relevance

The proposed project will develop and demonstrate the usefulness of pattern recognition techniques for predicting and detecting patterns of glaucomatous change in patient eyes tested longitudinally by visual field and optical imaging instruments. 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 neurodegeneration within the visual pathways at structural and functional levels. The development/use of novel, empirical techniques for predicting and detecting glaucomatous progression can have a significant impact on the future of clinical care and the future of clinical trials designed to investigate IOP lowering and neuroprotective drugs.

National Institute of Health (NIH)
National Eye Institute (NEI)
Research Project (R01)
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Study Section
Special Emphasis Panel (ZRG1-ETTN-E (92))
Program Officer
Chin, Hemin R
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University of California San Diego
Schools of Medicine
La Jolla
United States
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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
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Hammel, Na'ama; Belghith, Akram; Bowd, Christopher et al. (2016) Rate and Pattern of Rim Area Loss in Healthy and Progressing Glaucoma Eyes. Ophthalmology 123:760-70
Yousefi, Siamak; Balasubramanian, Madhusudhanan; Goldbaum, Michael H et al. (2016) Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields. Transl Vis Sci Technol 5:2
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