Literature DB >> 16865006

Improving glaucoma diagnosis by the combination of perimetry and HRT measurements.

Christian Y Mardin1, Andrea Peters, Folkert Horn, Anselm G Jünemann, Berthold Lausen.   

Abstract

PURPOSE: The aim of this study was to determine, whether the combination of morphologic data of the optic nerve head and visual field (VF) data would improve diagnosis of glaucoma, on the basis of the measurements alone. PATIENTS AND METHODS: Eighty-eight perimetric glaucomatous and 88 normal optic discs from the Erlangen Glaucoma Registry were matched for age. All normals and patients were examined in a standardized manner (Slitlamp biomicroscopy, gonioscopy, 24 h-applanation tonometry, automated VF testing, 15-degree optic disc stereographs, and Heidelberg Retina Tomograph (HRT)-scanning of the optic disc). The HRT variables were calculated in 4 optic disc sectors. All variables were calculated with the software's standard reference plane. To gain the same allocation of sectors as provided by the HRT software, the VF responses were averaged within 4 sectors. Classification results of these VF responses were compared with the summarized results within 4 sectors. Six different combinations of morphologic and VF data were used to assess their suitability to diagnose the disease. HRT measurements, and the standard output of the Octopus (HRT/PERI1), HRT measurements and the summarized sectors and their standard deviations (HRT/PERI2), HRT measurements, standard output of the octopus and the summarized sectors and their standard deviations (HRT/PERI1/PERI2), standard output of the Octopus (PERI1), summarized sectors of the Octopus and their standard deviations (PERI2) and HRT measurements. To assess the diagnostic value of the different data sets machine learning classifiers, stabilized linear discriminant analysis, classification trees, bagging, and double-bagging were applied.
RESULTS: Combination of morphologic and VF data improved the automated classification rules. The accuracy to diagnose glaucoma just by VF and HRT indices was maximized for double-bagging using both diagnostic tools. An estimated misclassification probability of less than 0.07 could be achieved for the primary open angle glaucoma patients combining HRT and VF sectors by double bagging. So highest sensitivity was 95% and specificity 91%, achieved by double-bagging and combination of HRT, PERI1, and PERI2.
CONCLUSIONS: The combination of optic disc measurements and VF data could not only improve glaucoma diagnosis in future, but could also help to find an objective way to diagnose glaucomatous optic atrophy. The limitation of the topographic relationship between structure and function is the individual variability of the optic disc morphology and the subjective variability of VF testing.

Entities:  

Mesh:

Year:  2006        PMID: 16865006     DOI: 10.1097/01.ijg.0000212232.03664.ee

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


  11 in total

1.  Visual evoked potential and psychophysical contrast thresholds in glaucoma.

Authors:  Siti Nurliyana Abdullah; Gordon F Sanderson; Andrew C James; Ted Maddess
Journal:  Doc Ophthalmol       Date:  2014-03-11       Impact factor: 2.379

2.  Improving glaucoma detection using spatially correspondent clusters of damage and by combining standard automated perimetry and optical coherence tomography.

Authors:  Ali S Raza; Xian Zhang; Carlos G V De Moraes; Charles A Reisman; Jeffrey M Liebmann; Robert Ritch; Donald C Hood
Journal:  Invest Ophthalmol Vis Sci       Date:  2014-01-29       Impact factor: 4.799

3.  Heidelberg Retina Tomograph 3 machine learning classifiers for glaucoma detection.

Authors:  K A Townsend; G Wollstein; D Danks; K R Sung; H Ishikawa; L Kagemann; M L Gabriele; J S Schuman
Journal:  Br J Ophthalmol       Date:  2008-06       Impact factor: 4.638

Review 4.  Diagnostic tools for glaucoma detection and management.

Authors:  Pooja Sharma; Pamela A Sample; Linda M Zangwill; Joel S Schuman
Journal:  Surv Ophthalmol       Date:  2008-11       Impact factor: 6.048

5.  The East London glaucoma prediction score: web-based validation of glaucoma risk screening tool.

Authors:  Cook Stephen; Longo-Mbenza Benjamin
Journal:  Int J Ophthalmol       Date:  2013-02-18       Impact factor: 1.779

6.  Predicting progression in glaucoma suspects with longitudinal estimates of retinal ganglion cell counts.

Authors:  Daniel Meira-Freitas; Renato Lisboa; Andrew Tatham; Linda M Zangwill; Robert N Weinreb; Christopher A Girkin; Jeffrey M Liebmann; Felipe A Medeiros
Journal:  Invest Ophthalmol Vis Sci       Date:  2013-06-19       Impact factor: 4.799

7.  Combining functional and structural tests improves the diagnostic accuracy of relevance vector machine classifiers.

Authors:  Lyne Racette; Christine Y Chiou; Jiucang Hao; Christopher Bowd; Michael H Goldbaum; Linda M Zangwill; Te-Won Lee; Robert N Weinreb; Pamela A Sample
Journal:  J Glaucoma       Date:  2010-03       Impact factor: 2.503

8.  Evaluation of a combined index of optic nerve structure and function for glaucoma diagnosis.

Authors:  Michael V Boland; Harry A Quigley
Journal:  BMC Ophthalmol       Date:  2011-02-11       Impact factor: 2.209

9.  Automated algorithms combining structure and function outperform general ophthalmologists in diagnosing glaucoma.

Authors:  Leonardo Seidi Shigueoka; José Paulo Cabral de Vasconcellos; Rui Barroso Schimiti; Alexandre Soares Castro Reis; Gabriel Ozeas de Oliveira; Edson Satoshi Gomi; Jayme Augusto Rocha Vianna; Renato Dichetti Dos Reis Lisboa; Felipe Andrade Medeiros; Vital Paulino Costa
Journal:  PLoS One       Date:  2018-12-05       Impact factor: 3.240

10.  Glaucoma Diagnostic Accuracy of Machine Learning Classifiers Using Retinal Nerve Fiber Layer and Optic Nerve Data from SD-OCT.

Authors:  Kleyton Arlindo Barella; Vital Paulino Costa; Vanessa Gonçalves Vidotti; Fabrício Reis Silva; Marcelo Dias; Edson Satoshi Gomi
Journal:  J Ophthalmol       Date:  2013-11-28       Impact factor: 1.909

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