Dataset Information

Separation and thickness measurements of superficial and deep slabs of the retinal nerve fiber layer in healthy and glaucomatous eyes.

ABSTRACT:

Purpose

Describe a new method to analyze retinal nerve fiber layer (RNFL) thickness maps.

Design

Cross-sectional study.

Subjects

RNFL thickness maps of healthy and glaucomatous eyes.

Methods

Optical coherence tomography (OCT) RNFL raster scans from 98 healthy and 50 glaucomatous eyes were analyzed. The RNFL thickness maps were separated into superficial (SNFL) and deep (DNFL) slabs through a partial thickness plane set at the modal thickness (mode). Association between mode and OCT signal strength (SS), age, axial length, and visual field mean deviation (VFMD) was tested (Pearson coefficient, r). Thicknesses of inferior and superior SNFL regions (i-,s-SNFL), and inferior, superior, nasal, and temporal DNFL regions (i-,s-,n-,t-DNFL) were calculated. The regions thicknesses were compared between healthy and glaucomatous eyes (t-test) and between glaucomatous eyes with early, moderate, and severe disease (ANOVA and linear regressions of thickness on VFMD). Diagnostic accuracy and correlation with VFMD of RNFL regions thicknesses were calculated as the area under the receiver operating characteristic curve (AUC) and Pearson r, respectively. P<0.05 was considered significant.

Main outcome

Thickness of regions in SNFL and DNFL slabs.

Results

The mode was not associated with SS, age, axial length, or VFMD, it circumscribed the thicker RNFL around the optic disc of healthy and glaucomatous eyes, and it was used to separate the SNFL and DNFL slabs of RNFL thickness maps. The thickness of the SNFL slab was less in glaucomatous eyes than in healthy eyes (P<0.001). S-SNFL and i-SNFL thicknesses (respectively) were 86.0±8.2?m and 87.3±9.6?m in healthy eyes vs. 66.1±9.1?m and 63.4±8.2?m in glaucomatous eyes (P<0.001 for both). The thickness of the DNFL slab was similar between groups (P=0.19). T-DNFL thickness was 37.0±5.3?m in healthy eyes vs. 33.9±5.0?m in glaucomatous eyes (P<0.001); thicknesses of all other DNFL regions were similar. The SNFL regions only thinned with progressively worse glaucoma severity, had excellent AUCs (AUC?0.95, P<0.001), and correlated strongly with VFMD (r?0.60, P<0.001).

Conclusions

Glaucomatous RNFL thinning is predominantly detected within a slab with thickness greater than the mode. SNFL thickness has great AUC and correlation with VFMD in glaucomatous eyes. The usefulness for diagnosis and monitoring of glaucoma needs further study.

SUBMITTER: Vazquez LE

PROVIDER: S-EPMC7337289 | biostudies-literature | 2020 Jan-Feb

REPOSITORIES: biostudies-literature

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Publications

Separation and thickness measurements of superficial and deep slabs of the retinal nerve fiber layer in healthy and glaucomatous eyes.

Vazquez Luis E LE Mwanza Jean-Claude JC Triolo Giacinto G Monsalve Pedro P Feuer William J WJ Parrish Richard K RK Anderson Douglas R DR Budenz Donald L DL

Ophthalmology. Glaucoma 20191120 1

<h4>Purpose</h4>Describe a new method to analyze retinal nerve fiber layer (RNFL) thickness maps.<h4>Design</h4>Cross-sectional study.<h4>Subjects</h4>RNFL thickness maps of healthy and glaucomatous eyes.<h4>Methods</h4>Optical coherence tomography (OCT) RNFL raster scans from 98 healthy and 50 glaucomatous eyes were analyzed. The RNFL thickness maps were separated into superficial (SNFL) and deep (DNFL) slabs through a partial thickness plane set at the modal thickness (mode). Association betwe ...[more]

PMID: 32632406

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Project description:PurposeTo compare prospectively detection of progressive retinal nerve fiber layer thickness (RNFL) atrophy identified using time-domain optical coherence tomography with visual field progression using standard automated perimetry in glaucoma suspect and preperimetric glaucoma patients or perimetric glaucoma patients.DesignProspective, longitudinal clinical trial.MethodsEligible eyes with 2 years or more of follow-up underwent time-domain optical coherence tomography and standard automated perimetry every 6 months. The occurrence of visual field progression was defined as the first follow-up visit reaching a significant (P < .05) negative visual field index slope over time. RNFL progression or improvement was defined as a significant negative or positive slope over time, respectively. Specificity was defined as the number of eyes with neither progression nor improvement, divided by the number of eyes without progression. Cox proportional hazard ratios were calculated using univariate and multivariate models with RNFL loss as a time-dependent covariate.ResultsThree hundred ten glaucoma suspect and preperimetric glaucoma eyes and 177 perimetric glaucoma eyes were included. Eighty-nine eyes showed visual field progression and 101 eyes showed RNFL progression. The average time to detect visual field progression in those 89 eyes was 35 ± 13 months, and the average time to detect RNFL progression in those 101 eyes was 36 ± 13 months. In multivariate Cox models, average and superior RNFL losses were associated with subsequent visual field index loss in the entire cohort (every 10-?m loss; hazard ratio, 1.38; P = .03; hazard ratio, 1.20; P = .01; respectively). Among the entire cohort of 487 eyes, 42 had significant visual field index improvement and 55 had significant RNFL improvement (specificity, 91.4% and 88.7%, respectively).ConclusionsStructural progression is associated with functional progression in glaucoma suspect and glaucomatous eyes. Average and superior RNFL thickness may predict subsequent standard automated perimetry loss.

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

Separation and thickness measurements of superficial and deep slabs of the retinal nerve fiber layer in healthy and glaucomatous eyes.

Purpose

Design

Subjects

Methods

Main outcome

Results

Conclusions

Publications

Separation and thickness measurements of superficial and deep slabs of the retinal nerve fiber layer in healthy and glaucomatous eyes.

Similar Datasets

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