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Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurements.

Publication ,  Journal Article
Bowd, C; Lee, I; Goldbaum, MH; Balasubramanian, M; Medeiros, FA; Zangwill, LM; Girkin, CA; Liebmann, JM; Weinreb, RN
Published in: Invest Ophthalmol Vis Sci
April 30, 2012

PURPOSE: The goal of this study was to determine if glaucomatous progression in suspect eyes can be predicted from baseline confocal scanning laser ophthalmoscope (CSLO) and standard automated perimetry (SAP) measurements analyzed with relevance vector machine (RVM) classifiers. METHODS: Two hundred sixty-four eyes of 193 participants were included. All eyes had normal SAP results at baseline with five or more SAP tests over time. Eyes were labeled progressed (n = 47) or stable (n = 217) during follow-up based on SAP Guided Progression Analysis or serial stereophotograph assessment. Baseline CSLO-measured topographic parameters (n = 117) and baseline total deviation values from the 24-2 SAP test-grid (n = 52) were selected from each eye. Ten-fold cross-validation was used to train and test RVMs using the CSLO and SAP features. Receiver operating characteristic (ROC) curve areas were calculated using full and optimized feature sets. ROC curve results from RVM analyses of CSLO, SAP, and CSLO and SAP combined were compared to CSLO and SAP global indices (Glaucoma Probability Score, mean deviation and pattern standard deviation). RESULTS: The areas under the ROC curves (AUROCs) for RVMs trained on optimized feature sets of CSLO parameters, SAP parameters, and CSLO and SAP parameters combined were 0.640, 0.762, and 0.805, respectively. AUROCs for CSLO Glaucoma Probability Score, SAP mean deviation (MD), and SAP pattern standard deviation (PSD) were 0.517, 0.513, and 0.620, respectively. No CSLO or SAP global indices discriminated between baseline measurements from progressed and stable eyes better than chance. CONCLUSIONS: In our sample, RVM analyses of baseline CSLO and SAP measurements could identify eyes that showed future glaucomatous progression with a higher accuracy than the CSLO and SAP global indices. (ClinicalTrials.gov numbers, NCT00221897, NCT00221923.).

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Published In

Invest Ophthalmol Vis Sci

DOI

EISSN

1552-5783

Publication Date

April 30, 2012

Volume

53

Issue

4

Start / End Page

2382 / 2389

Location

United States

Related Subject Headings

  • Visual Field Tests
  • Time Factors
  • Support Vector Machine
  • Sensitivity and Specificity
  • Prognosis
  • Ophthalmoscopy
  • Ophthalmology & Optometry
  • Middle Aged
  • Microscopy, Confocal
  • Male
 

Citation

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Bowd, C., Lee, I., Goldbaum, M. H., Balasubramanian, M., Medeiros, F. A., Zangwill, L. M., … Weinreb, R. N. (2012). Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurements. Invest Ophthalmol Vis Sci, 53(4), 2382–2389. https://doi.org/10.1167/iovs.11-7951
Bowd, Christopher, Intae Lee, Michael H. Goldbaum, Madhusudhanan Balasubramanian, Felipe A. Medeiros, Linda M. Zangwill, Christopher A. Girkin, Jeffrey M. Liebmann, and Robert N. Weinreb. “Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurements.Invest Ophthalmol Vis Sci 53, no. 4 (April 30, 2012): 2382–89. https://doi.org/10.1167/iovs.11-7951.
Bowd C, Lee I, Goldbaum MH, Balasubramanian M, Medeiros FA, Zangwill LM, et al. Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurements. Invest Ophthalmol Vis Sci. 2012 Apr 30;53(4):2382–9.
Bowd, Christopher, et al. “Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurements.Invest Ophthalmol Vis Sci, vol. 53, no. 4, Apr. 2012, pp. 2382–89. Pubmed, doi:10.1167/iovs.11-7951.
Bowd C, Lee I, Goldbaum MH, Balasubramanian M, Medeiros FA, Zangwill LM, Girkin CA, Liebmann JM, Weinreb RN. Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurements. Invest Ophthalmol Vis Sci. 2012 Apr 30;53(4):2382–2389.

Published In

Invest Ophthalmol Vis Sci

DOI

EISSN

1552-5783

Publication Date

April 30, 2012

Volume

53

Issue

4

Start / End Page

2382 / 2389

Location

United States

Related Subject Headings

  • Visual Field Tests
  • Time Factors
  • Support Vector Machine
  • Sensitivity and Specificity
  • Prognosis
  • Ophthalmoscopy
  • Ophthalmology & Optometry
  • Middle Aged
  • Microscopy, Confocal
  • Male