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

Publication ,  Journal Article
Yousefi, S; Goldbaum, MH; Varnousfaderani, ES; Belghith, A; Jung, T-P; Medeiros, FA; Zangwill, LM; Weinreb, RN; Liebmann, JM; Girkin, CA; Bowd, C
Published in: J Biomed Inform
December 2015

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.

Duke Scholars

Published In

J Biomed Inform

DOI

EISSN

1532-0480

Publication Date

December 2015

Volume

58

Start / End Page

96 / 103

Location

United States

Related Subject Headings

  • Visual Fields
  • Middle Aged
  • Medical Informatics
  • Male
  • Humans
  • Glaucoma
  • Female
  • Biomedical Engineering
  • Aged
  • 4601 Applied computing
 

Citation

APA
Chicago
ICMJE
MLA
NLM
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. J Biomed Inform, 58, 96–103. https://doi.org/10.1016/j.jbi.2015.09.019
Yousefi, Siamak, Michael H. Goldbaum, Ehsan S. Varnousfaderani, Akram Belghith, Tzyy-Ping Jung, Felipe A. Medeiros, Linda M. Zangwill, et al. “Detecting glaucomatous change in visual fields: Analysis with an optimization framework.J Biomed Inform 58 (December 2015): 96–103. https://doi.org/10.1016/j.jbi.2015.09.019.
Yousefi S, Goldbaum MH, Varnousfaderani ES, Belghith A, Jung T-P, Medeiros FA, et al. Detecting glaucomatous change in visual fields: Analysis with an optimization framework. J Biomed Inform. 2015 Dec;58:96–103.
Yousefi, Siamak, et al. “Detecting glaucomatous change in visual fields: Analysis with an optimization framework.J Biomed Inform, vol. 58, Dec. 2015, pp. 96–103. Pubmed, doi:10.1016/j.jbi.2015.09.019.
Yousefi S, Goldbaum MH, Varnousfaderani ES, Belghith A, Jung T-P, Medeiros FA, Zangwill LM, Weinreb RN, Liebmann JM, Girkin CA, Bowd C. Detecting glaucomatous change in visual fields: Analysis with an optimization framework. J Biomed Inform. 2015 Dec;58:96–103.
Journal cover image

Published In

J Biomed Inform

DOI

EISSN

1532-0480

Publication Date

December 2015

Volume

58

Start / End Page

96 / 103

Location

United States

Related Subject Headings

  • Visual Fields
  • Middle Aged
  • Medical Informatics
  • Male
  • Humans
  • Glaucoma
  • Female
  • Biomedical Engineering
  • Aged
  • 4601 Applied computing