M
Madhusudhanan Balasubramanian
Researcher at University of Memphis
Publications - 43
Citations - 825
Madhusudhanan Balasubramanian is an academic researcher from University of Memphis. The author has contributed to research in topics: Glaucoma & Optic disk. The author has an hindex of 14, co-authored 43 publications receiving 677 citations. Previous affiliations of Madhusudhanan Balasubramanian include University of California, San Diego & Louisiana State University.
Papers
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Journal ArticleDOI
Glaucoma Progression Detection Using Structural Retinal Nerve Fiber Layer Measurements and Functional Visual Field Points
Siamak Yousefi,Michael H. Goldbaum,Madhusudhanan Balasubramanian,Tzyy-Ping Jung,Robert N. Weinreb,Felipe A. Medeiros,Linda M. Zangwill,Jeffrey M. Liebmann,Christopher A. Girkin,Christopher Bowd +9 more
TL;DR: This study was performed using several machine learning classifiers including Bayesian, Lazy, Meta, and Tree, composing different families to detect glaucomatous progression using longitudinal series of structural data extracted from retinal nerve fiber layer thickness measurements and visual functional data recorded from standard automated perimetry tests.
Journal ArticleDOI
Effect of image quality on tissue thickness measurements obtained with spectral domain-optical coherence tomography
Madhusudhanan Balasubramanian,Christopher Bowd,Gianmarco Vizzeri,Robert N. Weinreb,Linda M. Zangwill +4 more
TL;DR: Results indicate that when image quality is within the range specified as acceptable by SD-OCT manufacturers, RNFL and retinal thickness measurements are comparable.
Journal ArticleDOI
Spectral domain-optical coherence tomography to detect localized retinal nerve fiber layer defects in glaucomatous eyes.
Gianmarco Vizzeri,Madhusudhanan Balasubramanian,Christopher Bowd,Robert N. Weinreb,Felipe A. Medeiros,Linda M. Zangwill +5 more
TL;DR: SD-OCT is a promising technology for glaucoma detection as it may assist clinicians identify the presence of localizedglaucomatous structural damage seen on stereophotographs, and is confirmed by this study.
Journal ArticleDOI
Learning From Data: Recognizing Glaucomatous Defect Patterns and Detecting Progression From Visual Field Measurements
Siamak Yousefi,Michael H. Goldbaum,Madhusudhanan Balasubramanian,Felipe A. Medeiros,Linda M. Zangwill,Jeffrey M. Liebmann,Christopher A. Girkin,Robert N. Weinreb,Christopher Bowd +8 more
TL;DR: The proposed method was compared to a recently developed progression detection method and to clinically available glaucoma progression detection software and the clinical accuracy of the proposed pipeline was as good as or better than the currently available methods.
Journal ArticleDOI
Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields
Siamak Yousefi,Madhusudhanan Balasubramanian,Michael H. Goldbaum,Felipe A. Medeiros,Linda M. Zangwill,Robert N. Weinreb,Jeffrey M. Liebmann,Christopher A. Girkin,Christopher Bowd +8 more
TL;DR: GEM-POP was significantly more sensitive to PGON than PoPLR and linear regression of MD and VFI in the authors' sample, while providing localized progression information.