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)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-ETTN-E (92))
Program Officer
Chin, Hemin R
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of California San Diego
Schools of Medicine
La Jolla
United States
Zip Code
Yousefi, Siamak; Goldbaum, Michael H; Balasubramanian, Madhusudhanan et al. (2014) Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points. IEEE Trans Biomed Eng 61:1143-54
Balasubramanian, Madhusudhanan; Arias-Castro, Ery; Medeiros, Felipe A et al. (2014) Detecting glaucoma progression from localized rates of retinal changes in parametric and nonparametric statistical framework with type I error control. Invest Ophthalmol Vis Sci 55:1684-95
Miki, Atsuya; Medeiros, Felipe A; Weinreb, Robert N et al. (2014) Rates of retinal nerve fiber layer thinning in glaucoma suspect eyes. Ophthalmology 121:1350-8
Yousefi, Siamak; Goldbaum, Michael H; Balasubramanian, Madhusudhanan et al. (2014) Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements. IEEE Trans Biomed Eng 61:2112-24
Belghith, Akram; Balasubramanian, Madhusudhanan; Bowd, Christopher et al. (2014) A unified framework for glaucoma progression detection using Heidelberg Retina Tomograph images. Comput Med Imaging Graph 38:411-20
Bowd, Christopher; Weinreb, Robert N; Balasubramanian, Madhusudhanan et al. (2014) Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiers. PLoS One 9:e85941
Balasubramanian, Madhusudhanan; Kriegman, David J; Bowd, Christopher et al. (2012) Localized glaucomatous change detection within the proper orthogonal decomposition framework. Invest Ophthalmol Vis Sci 53:3615-28