Dataset Information

Ability of cirrus HD-OCT optic nerve head parameters to discriminate normal from glaucomatous eyes.

ABSTRACT:

Purpose

To determine the ability of optic nerve head (ONH) parameters measured with spectral domain Cirrus HD-OCT (Carl Zeiss Meditec, Inc., Dublin, CA) to discriminate between normal and glaucomatous eyes and to compare them with the discriminating ability of peripapillary retinal nerve fiber layer (RNFL) thickness measurements performed with Cirrus HD-OCT.

Design

Evaluation of diagnostic test or technology.

Participants

Seventy-three subjects with glaucoma and 146 age-matched normal subjects.

Methods

Peripapillary ONH parameters and RNFL thickness were measured in 1 randomly selected eye of each participant within a 200 × 200 pixel A-scan acquired with Cirrus HD-OCT centered on the ONH.

Main outcome measures

Optic nerve head topographic parameters, peripapillary RNFL thickness, and area under receiver operating characteristic curves (AUCs).

Results

To distinguish normal from glaucomatous eyes, regardless of disease stage, the 6 best parameters (expressed as AUC) were vertical rim thickness (VRT, 0.963), rim area (0.962), RNFL thickness at clock-hour 7 (0.957), RNFL thickness of the inferior quadrant (0.953), vertical cup-to-disc ratio (VCDR, 0.951), and average RNFL thickness (0.950). The AUC for distinguishing between normal eyes and eyes with mild glaucoma was greatest for RNFL thickness of clock-hour 7 (0.918), VRT (0.914), rim area (0.912), RNFL thickness of inferior quadrant (0.895), average RNFL thickness (0.893), and VCDR (0.890). There were no statistically significant differences between AUCs for the best ONH parameters and RNFL thickness measurements (P > 0.05).

Conclusions

Cirrus HD-OCT ONH parameters are able to discriminate between normal eyes and eyes with glaucoma or even mild glaucoma. There is no difference in the ability of ONH parameters and RNFL thickness measurement, as measured with Cirrus OCT, to distinguish between normal and glaucomatous eyes.

SUBMITTER: Mwanza JC

PROVIDER: S-EPMC3017237 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

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Publications

Ability of cirrus HD-OCT optic nerve head parameters to discriminate normal from glaucomatous eyes.

Mwanza Jean-Claude JC Oakley Jonathan D JD Budenz Donald L DL Anderson Douglas R DR

Ophthalmology 20101028 2

<h4>Purpose</h4>To determine the ability of optic nerve head (ONH) parameters measured with spectral domain Cirrus HD-OCT (Carl Zeiss Meditec, Inc., Dublin, CA) to discriminate between normal and glaucomatous eyes and to compare them with the discriminating ability of peripapillary retinal nerve fiber layer (RNFL) thickness measurements performed with Cirrus HD-OCT.<h4>Design</h4>Evaluation of diagnostic test or technology.<h4>Participants</h4>Seventy-three subjects with glaucoma and 146 age-mat ...[more]

PMID: 20920824

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Project description:PURPOSE:Ellipsoid zone (EZ) and outer retinal integrity are strongly linked to visual prognosis, but quantitative normative data is lacking. This study evaluates the EZ, outer retina, and inner retina in eyes without macular disease across a wide age spectrum. METHODS:An IRB-approved study was performed for eyes without macular pathology undergoing Spectral Domain Optical Coherence Tomography (SD-OCT) scans on the Cirrus HD-OCT system (Carl Zeiss Meditec, Oberkochen, Germany). Scans were analyzed using a previously described automated EZ mapping tool with line-by-line manual verification. Segmentation included internal limiting membrane (ILM), outer nuclear layer/Henle fiber layer complex (ONL/HFL), EZ, and the retinal pigment epithelium (RPE). The output included metrics for the inner retina (ILM-OPL/HFL), outer retina (ONL/HFL-RPE), EZ-RPE area and volume, and en face EZ mapping. EZ-RPE attenuation on en face mapping was defined as EZ-RPE thickness < 20 um, and total attenuation was 0 um. Imaging parameters were assessed for the group and compared to age, sex, visual acuity and spherical equivalent. RESULTS:167 eyes from 167 subjects were included. Mean age was 49.7 years (range 10-84 years). The mean foveal retinal thickness was 200.58 ± 19.22 um. Mean inner retinal thickness was 21.47 ± 13.60 um. Mean outer retinal thickness was 179.11 ± 18.52 um. Mean EZ-RPE thickness was 50.58 ± 6.01um. The mean EZ-RPE volume was 1.20 ± 0.10 mm3. Mean EZ attenuation percentage per macular map area was 0.87% ± 1.13% and mean percentage total attenuation was 0.12% ± 0.14%. Total and inner retinal thickness metrics decreased with age. Mean outer retinal thickness increased with age. EZ-RPE parameters were unchanged with age. However, EZ attenuation was negatively correlated with age. CONCLUSION:This study provides important information for inner and outer retinal parameters. Future research on quantitative EZ integrity can utilize this data for comparison.

Project description:PurposeTo develop and evaluate a deep learning system for differentiating between eyes with and without glaucomatous visual field damage (GVFD) and predicting the severity of GFVD from spectral domain OCT (SD OCT) optic nerve head images.DesignEvaluation of a diagnostic technology.ParticipantsA total of 9765 visual field (VF) SD OCT pairs collected from 1194 participants with and without GVFD (1909 eyes).MethodsDeep learning models were trained to use SD OCT retinal nerve fiber layer (RNFL) thickness maps, RNFL en face images, and confocal scanning laser ophthalmoscopy (CSLO) images to identify eyes with GVFD and predict quantitative VF mean deviation (MD), pattern standard deviation (PSD), and mean VF sectoral pattern deviation (PD) from SD OCT data.Main outcome measuresDeep learning models were compared with mean RNFL thickness for identifying GVFD using area under the curve (AUC), sensitivity, and specificity. For predicting MD, PSD, and mean sectoral PD, models were evaluated using R2 and mean absolute error (MAE).ResultsIn the independent test dataset, the deep learning models based on RNFL en face images achieved an AUC of 0.88 for identifying eyes with GVFD and 0.82 for detecting mild GVFD significantly (P < 0.001) better than using mean RNFL thickness measurements (AUC = 0.82 and 0.73, respectively). Deep learning models outperformed standard RNFL thickness measurements in predicting all quantitative VF metrics. In predicting MD, deep learning models based on RNFL en face images achieved an R2 of 0.70 and MAE of 2.5 decibels (dB) compared with 0.45 and 3.7 dB for RNFL thickness measurements. In predicting mean VF sectoral PD, deep learning models achieved high accuracy in the inferior nasal (R2 = 0.60) and superior nasal (R2 = 0.67) sectors, moderate accuracy in inferior (R2 = 0.26) and superior (R2 = 0.35) sectors, and lower accuracy in the central (R2 = 0.15) and temporal (R2 = 0.12) sectors.ConclusionsDeep learning models had high accuracy in identifying eyes with GFVD and predicting the severity of functional loss from SD OCT images. Accurately predicting the severity of GFVD from SD OCT imaging can help clinicians more effectively individualize the frequency of VF testing to the individual patient.

Dataset Information

Ability of cirrus HD-OCT optic nerve head parameters to discriminate normal from glaucomatous eyes.

Purpose

Design

Participants

Methods

Main outcome measures

Results

Conclusions

Publications

Ability of cirrus HD-OCT optic nerve head parameters to discriminate normal from glaucomatous eyes.

Similar Datasets

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