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Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements.

Publication ,  Journal Article
Yousefi, S; Goldbaum, MH; Balasubramanian, M; Medeiros, FA; Zangwill, LM; Liebmann, JM; Girkin, CA; Weinreb, RN; Bowd, C
Published in: IEEE Trans Biomed Eng
July 2014

A hierarchical approach to learn from visual field data was adopted to identify glaucomatous visual field defect patterns and to detect glaucomatous progression. The analysis pipeline included three stages, namely, clustering, glaucoma boundary limit detection, and glaucoma progression detection testing. First, cross-sectional visual field tests collected from each subject were clustered using a mixture of Gaussians and model parameters were estimated using expectation maximization. The visual field clusters were further estimated to recognize glaucomatous visual field defect patterns by decomposing each cluster into several axes. The glaucoma visual field defect patterns along each axis then were identified. To derive a definition of progression, the longitudinal visual fields of stable glaucoma eyes on the abnormal cluster axes were projected and the slope was approximated using linear regression (LR) to determine the confidence limit of each axis. For glaucoma progression detection, the longitudinal visual fields of each eye on the abnormal cluster axes were projected and the slope was approximated by LR. Progression was assigned if the progression rate was greater than the boundary limit of the stable eyes; otherwise, stability was assumed. The proposed method was compared to a recently developed progression detection method and to clinically available glaucoma progression detection software. The clinical accuracy of the proposed pipeline was as good as or better than the currently available methods.

Duke Scholars

Published In

IEEE Trans Biomed Eng

DOI

EISSN

1558-2531

Publication Date

July 2014

Volume

61

Issue

7

Start / End Page

2112 / 2124

Location

United States

Related Subject Headings

  • Visual Fields
  • Visual Field Tests
  • Pattern Recognition, Automated
  • Middle Aged
  • Male
  • Linear Models
  • Humans
  • Glaucoma
  • Female
  • Disease Progression
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Yousefi, S., Goldbaum, M. H., Balasubramanian, M., Medeiros, F. A., Zangwill, L. M., Liebmann, J. M., … Bowd, C. (2014). Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements. IEEE Trans Biomed Eng, 61(7), 2112–2124. https://doi.org/10.1109/TBME.2014.2314714
Yousefi, Siamak, Michael H. Goldbaum, Madhusudhanan Balasubramanian, Felipe A. Medeiros, Linda M. Zangwill, Jeffrey M. Liebmann, Christopher A. Girkin, Robert N. Weinreb, and Christopher Bowd. “Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements.IEEE Trans Biomed Eng 61, no. 7 (July 2014): 2112–24. https://doi.org/10.1109/TBME.2014.2314714.
Yousefi S, Goldbaum MH, Balasubramanian M, Medeiros FA, Zangwill LM, Liebmann JM, et al. Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements. IEEE Trans Biomed Eng. 2014 Jul;61(7):2112–24.
Yousefi, Siamak, et al. “Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements.IEEE Trans Biomed Eng, vol. 61, no. 7, July 2014, pp. 2112–24. Pubmed, doi:10.1109/TBME.2014.2314714.
Yousefi S, Goldbaum MH, Balasubramanian M, Medeiros FA, Zangwill LM, Liebmann JM, Girkin CA, Weinreb RN, Bowd C. Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements. IEEE Trans Biomed Eng. 2014 Jul;61(7):2112–2124.

Published In

IEEE Trans Biomed Eng

DOI

EISSN

1558-2531

Publication Date

July 2014

Volume

61

Issue

7

Start / End Page

2112 / 2124

Location

United States

Related Subject Headings

  • Visual Fields
  • Visual Field Tests
  • Pattern Recognition, Automated
  • Middle Aged
  • Male
  • Linear Models
  • Humans
  • Glaucoma
  • Female
  • Disease Progression