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.
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.
|Belghith, Akram; Bowd, Christopher; Medeiros, Felipe A et al. (2016) Does the Location of Bruch's Membrane Opening Change Over Time? Longitudinal Analysis Using San Diego Automated Layer Segmentation Algorithm (SALSA). Invest Ophthalmol Vis Sci 57:675-82|
|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|
|Silverman, Anna L; Hammel, Naama; Khachatryan, Naira et al. (2016) Diagnostic Accuracy of the Spectralis and Cirrus Reference Databases in Differentiating between Healthy and Early Glaucoma Eyes. Ophthalmology 123:408-14|
|Khachatryan, Naira; Medeiros, Felipe A; Sharpsten, Lucie et al. (2015) The African Descent and Glaucoma Evaluation Study (ADAGES): predictors of visual field damage in glaucoma suspects. Am J Ophthalmol 159:777-87|
|Yousefi, Siamak; Goldbaum, Michael H; Varnousfaderani, Ehsan S et al. (2015) Detecting glaucomatous change in visual fields: Analysis with an optimization framework. J Biomed Inform 58:96-103|
|Belghith, Akram; Bowd, Christopher; Medeiros, Felipe A et al. (2015) Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression. Artif Intell Med 64:105-15|
|Belghith, Akram; Bowd, Christopher; Weinreb, Robert N et al. (2014) A hierarchical framework for estimating neuroretinal rim area using 3D spectral domain optical coherence tomography (SD-OCT) optic nerve head (ONH) images of healthy and glaucoma eyes. Conf Proc IEEE Eng Med Biol Soc 2014:3869-72|
|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|
|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|
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