Literature DB >> 21986567

Linear discriminant functions to improve the glaucoma probability score analysis to detect glaucomatous optic nerve heads: a multicenter study.

Michele Iester1, Francesco Oddone, Mirko Prato, Marco Centofanti, Paolo Fogagnolo, Luca Rossetti, Valeria Vaccarezza, Gianluca Manni, Antonio Ferreras.   

Abstract

PURPOSE: The purpose of the study was to create a linear discriminant function (LDF) formula by using the new Glaucoma Probability Score (GPS) global and sectorial optic nerve head parameters measured by Heidelberg Retinal Tomograph 3 to improve the GPS diagnostic capacity to discriminate between healthy and glaucomatous eyes. PATIENTS AND METHODS: This is a multicenter cross-sectional study. To calculate the LDF formula, 137 normal individuals and 96 glaucomatous patients were selected. Another independent sample of 60 healthy and 69 glaucomatous eyes was used to evaluate the diagnostic accuracy of the LDF formulas. All patients underwent a full eye examination, standard achromatic perimetry by using Humphrey Field Analyzer, and imaging with Heidelberg Retinal Tomograph 3. Glaucoma was defined on the basis of SITA-24-2 visual field loss (pattern standard deviation P<5% and glaucoma Hemifield test outside normal limits). The area under the receiver operating characteristics curves and sensitivity and specificity for different LDFs were analyzed as measures of diagnostic accuracy. The analysis was repeated after stratification for optic disc size and glaucoma stage.
RESULTS: Two LDF formulas improved GPS algorithm results. At fixed specificities of 85% and 95%, the sensitivity values were 79.7% and 71%, respectively, for the GPS-only LDF, which used only the so-called GPS parameters, whereas the values were 85.5% and 79.7%, respectively, for the GPS-RA LDF, which used sectorial rim area parameters as well.
CONCLUSIONS: When the so-called GPS parameters were used in an LDF formula, its diagnostic capacity slightly improved; however, the diagnostic performances of the GPS LDF, which used sectorial rim area parameters as well, were better.

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Year:  2013        PMID: 21986567     DOI: 10.1097/IJG.0b013e31823298b3

Source DB:  PubMed          Journal:  J Glaucoma        ISSN: 1057-0829            Impact factor:   2.503


  2 in total

1.  A Data Mining Framework for Glaucoma Decision Support Based on Optic Nerve Image Analysis Using Machine Learning Methods.

Authors:  Syed S R Abidi; Patrice C Roy; Muhammad S Shah; Jin Yu; Sanjun Yan
Journal:  J Healthc Inform Res       Date:  2018-06-20

2.  Assessment of the optic disc morphology using spectral-domain optical coherence tomography and scanning laser ophthalmoscopy.

Authors:  Pilar Calvo; Antonio Ferreras; Beatriz Abadia; Mirian Ara; Michele Figus; Luis E Pablo; Paolo Frezzotti
Journal:  Biomed Res Int       Date:  2014-07-06       Impact factor: 3.411

  2 in total

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