Detecting glaucomatous change in visual fields: Analysis with an optimization framework

Siamak Yousefi, Michael H. Goldbaum, Ehsan S. Varnousfaderani, Akram Belghith, Tzyy Ping Jung, Felipe A. Medeiros, Linda M. Zangwill, Robert N. Weinreb, Jeffrey M. Liebmann, Christopher A. Girkin, Christopher Bowd

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)96-103
Number of pages8
JournalJournal of Biomedical Informatics
Volume58
DOIs
StatePublished - Dec 1 2015

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Visual Fields
Glaucoma
Patient monitoring
Convex optimization
Linear regression
Learning systems
Visual Field Tests
Physiologic Monitoring
Population
Linear Models
Research

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics

Cite this

Yousefi, S., Goldbaum, M. H., Varnousfaderani, E. S., Belghith, A., Jung, T. P., Medeiros, F. A., ... Bowd, C. (2015). Detecting glaucomatous change in visual fields: Analysis with an optimization framework. Journal of Biomedical Informatics, 58, 96-103. https://doi.org/10.1016/j.jbi.2015.09.019

Detecting glaucomatous change in visual fields : Analysis with an optimization framework. / Yousefi, Siamak; Goldbaum, Michael H.; Varnousfaderani, Ehsan S.; Belghith, Akram; Jung, Tzyy Ping; Medeiros, Felipe A.; Zangwill, Linda M.; Weinreb, Robert N.; Liebmann, Jeffrey M.; Girkin, Christopher A.; Bowd, Christopher.

In: Journal of Biomedical Informatics, Vol. 58, 01.12.2015, p. 96-103.

Research output: Contribution to journalArticle

Yousefi, S, Goldbaum, MH, Varnousfaderani, ES, Belghith, A, Jung, TP, Medeiros, FA, Zangwill, LM, Weinreb, RN, Liebmann, JM, Girkin, CA & Bowd, C 2015, 'Detecting glaucomatous change in visual fields: Analysis with an optimization framework', Journal of Biomedical Informatics, vol. 58, pp. 96-103. https://doi.org/10.1016/j.jbi.2015.09.019
Yousefi, Siamak ; Goldbaum, Michael H. ; Varnousfaderani, Ehsan S. ; Belghith, Akram ; Jung, Tzyy Ping ; Medeiros, Felipe A. ; Zangwill, Linda M. ; Weinreb, Robert N. ; Liebmann, Jeffrey M. ; Girkin, Christopher A. ; Bowd, Christopher. / Detecting glaucomatous change in visual fields : Analysis with an optimization framework. In: Journal of Biomedical Informatics. 2015 ; Vol. 58. pp. 96-103.
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