title: Detecting glaucomatous change in visual fields: Analysis with an optimization framework.
abstract: Detecting glaucomatous progression is an important aspect of glaucoma management. The assessment of longitudinal series of visual fields, measured using Standard Automated Perimetry (SAP), is considered the reference standard for this effort. We seek efficient techniques for determining progression from longitudinal visual fields by formulating the problem as an optimization framework, learned from a population of glaucoma data. The longitudinal data from each patient's eye were used in a convex optimization framework to find a vector that is representative of the progression direction of the sample population, as a whole. Post-hoc analysis of longitudinal visual fields across the derived vector led to optimal progression (change) detection. The proposed method was compared to recently described progression detection methods and to linear regression of instrument-defined global indices, and showed slightly higher sensitivities at the highest specificities than other methods (a clinically desirable result). The proposed approach is simpler, faster, and more efficient for detecting glaucomatous changes, compared to our previously proposed machine learning-based methods, although it provides somewhat less information. This approach has potential application in glaucoma clinics for patient monitoring and in research centers for classification of study participants.
authors.affiliation: Hamilton Glaucoma Center and the Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA.
authors.forename: Siamak
authors.initials: S
authors.lastname: Yousefi
authors.affiliation: Hamilton Glaucoma Center and the Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA.
authors.forename: Michael H
authors.initials: MH
authors.lastname: Goldbaum
authors.affiliation: Hamilton Glaucoma Center and the Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA.
authors.forename: Ehsan S
authors.initials: ES
authors.lastname: Varnousfaderani
authors.affiliation: Hamilton Glaucoma Center and the Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA.
authors.forename: Akram
authors.initials: A
authors.lastname: Belghith
authors.affiliation: Institute for Neural Computation and Institute of Engineering in Medicine, University of California San Diego, La Jolla, CA, USA.
authors.forename: Tzyy-Ping
authors.initials: TP
authors.lastname: Jung
authors.affiliation: Hamilton Glaucoma Center and the Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA.
authors.forename: Felipe A
authors.initials: FA
authors.lastname: Medeiros
authors.affiliation: Hamilton Glaucoma Center and the Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA.
authors.forename: Linda M
authors.initials: LM
authors.lastname: Zangwill
authors.affiliation: Hamilton Glaucoma Center and the Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA.
authors.forename: Robert N
authors.initials: RN
authors.lastname: Weinreb
authors.affiliation: Department of Ophthalmology, Columbia University, New York, NY, USA.
authors.forename: Jeffrey M
authors.initials: JM
authors.lastname: Liebmann
authors.affiliation: Department of Ophthalmology, University of Alabama, Birmingham, AL, USA.
authors.forename: Christopher A
authors.initials: CA
authors.lastname: Girkin
authors.affiliation: Hamilton Glaucoma Center and the Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA. Electronic address: cbowd@ucsd.edu.
authors.forename: Christopher
authors.initials: C
authors.lastname: Bowd
mesh_terms: D000368:Aged; D005260:Female; D005901:Glaucoma / Q000503:physiopathology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D014794:Visual Fields
keywords: Change detection; Computational modeling; Data mining; Glaucoma; Progression; Standard automated perimetry; Visual field