The effect of cataract on early stage glaucoma detection using spatial and temporal contrast sensitivity tests

Background: To investigate the effect of cataract on the ability of spatial and temporal contrast sensitivity tests used to detect early glaucoma. Methods: Twenty-seven glaucoma subjects with early cataract (mean age 60 ±10.2 years) which constituted the test group were recruited together with twenty-seven controls (cataract only) matched for age and cataract type from a primary eye care setting. Contrast sensitivity to flickering gratings at 20 Hz and stationary gratings with and without glare, were measured for 0.5, 1.5 and 3 cycles per degree (cpd) in central vision. Perimetry and structural measurements with the Heidelberg Retinal Tomograph (HRT) were also performed. Results: After considering the effect of cataract, contrast sensitivity to stationary gratings was reduced in the test group compared with controls with a statistically significant mean difference of 0.2 log units independent of spatial frequency. The flicker test showed a significant difference between test and control group at 1.5 and 3 cpd (p = 0.019 and p = 0.011 respectively). The percentage of glaucoma patients who could not see the temporal modulation was much higher compared with their cataract only counterparts. A significant correlation was found between the reduction of contrast sensitivity caused by glare and the Glaucoma Probability Score (GPS) as measured with the HRT (p<0.005). Conclusions: These findings indicate that both spatial and temporal contrast sensitivity tests are suitable for distinguishing between vision loss as a consequence of glaucoma and vision loss caused by cataract only. The correlation between glare factor and GPS suggests that there may be an increase in intraocular stray light in glaucoma

Improving Glaucoma Diagnosis by the Combination of Perimetry and HRT Measurements

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

New Glaucoma Classification Method based on Standard Heidelberg Retina Tomograph Parameters by Bagging Classification Trees

Purpose: In this article we propose and evaluate nonparametric tree classifiers that can handle non-normal data and a large number of possible predictors using the full set of standard Heidelberg Retina Tomograph measurements for classifying glaucoma. Methods: The classifiers were trained and tested using standard Heidelberg Retina Tomograph parameters from examinations of 98 subjects with glaucoma and 98 normal subjects of the Erlangen Glaucoma Registry. All patients and control subjects were evaluated by 15�-optic disc stereographs, Heidelberg Retina Tomograph measurements, standard computerized white-in-white perimetry, and 24-hour-intraocular pressure profiles. The subjects were matched by age and sex. Standard classification trees as well as bagged classification trees were used. The classification outcome of the trees was compared with the classification by two published linear discriminant functions based on Heidelberg Retina Tomograph variables with respect to their cross-validated misclassification error. Results: The bagged classification tree had the lowest misclassification error estimate of 14.8% with a sensitivity of 81.6% at a specificity of 88.8%. The cross-validated error rates of the two linear discriminant function procedures were 20.4% (sensitivity 82.6%, specificity 76.7%) and 20.6% (sensitivity 81.4%, specificity 77.3%) for our set of observations. Bagged classification trees were able to reduce the misclassification error of glaucoma classification. Conclusions: Bagged classification trees promise to be a new and efficient approach for glaucoma classification using morphometric 2- and 3-dimensional data derived from the Heidelberg Retina Tomograph, taking into account all given variables. Early treatment of glaucoma requires methods for diagnosing glaucoma before visual field defects are measurable. Various authors have proposed methods for classifying glaucoma based on Heidelberg Retina Tomograph (HRT) examinations. 1-7 The most commonly used classifiers were linear discriminant functions. This method is based on the assumption of multivariate normality and fails if the number of variables is large, whereas the number of observations is limited. Therefore, most authors select a small number of variables, which are incorporated into a linear discriminant analysis. However, this set of variables may not lead to the best linear classifier for other studies as shown recently. 8 In this article we propose and evaluate nonparametric tree classifiers, which can handle non-normal data and a large number of possible predictors without pre-selecting variables but using the full set of standard HRT measurements