Comparison of Retinal Nerve Fiber Layer Thickness and Optic Disk Algorithms with Optical Coherence Tomography to Detect Glaucoma

doi:10.1016/j.ajo.2005.08.023

American Journal of Ophthalmology

Volume 141, Issue 1, January 2006, Pages 105-115.e1

Presented as a poster at the annual meeting of ARVO, May 1–5, 2005, Fort Lauderdale, Florida.

https://doi.org/10.1016/j.ajo.2005.08.023 Get rights and content

Purpose

To compare the performance of the retinal nerve fiber layer (RNFL) thickness and optic disk algorithms as determined by optical coherence tomography to detect glaucoma.

Design

Observational cross-sectional study.

Methods

setting: Academic tertiary-care center. study population: One eye from 42 control subjects and 65 patients with open-angle glaucoma with visual acuity of ≥20/40, and no other ocular pathologic condition. observation procedures: Two optical coherence tomography algorithms were used: “fast RNFL thickness” and “fast optic disk.” main outcome measures: Area under the receiver operating characteristic curves and sensitivities at fixed specificities were used. Discriminating ability of the average RNFL thickness and RNFL thickness in clock-hour sectors and quadrants was compared with the parameters that were derived from the fast optic disk algorithm. Classification and regression trees were used to determine the best combination of parameters for the detection of glaucoma.

Results

The average visual field mean deviation (±SD) was 0.0 ± 1.3 and −5.3 ± 5.0 dB in the control and glaucoma groups, respectively. The RNFL thickness at the 7 o’clock sector, inferior quadrant, and the vertical C/D ratio had the highest area under the receiver operating characteristic curves (0.93 ± 0.02, 0.92 ± 0.03, and 0.90 ± 0.03, respectively). At 90% specificity, the best sensitivities (±SE) from each algorithm were 86% ± 3% for RNFL thickness at the 7 o’clock sector and 79% ± 4% for horizontal integrated rim width (estimated rim area). The combination of inferior quadrant RNFL thickness and vertical C/D ratio achieved the best classification (misclassification rate, 6.2%).

Conclusion

The fast optic disk algorithm performs as well as the fast RNFL thickness algorithm for discrimination of glaucoma from normal eyes. A combination of the two algorithms may provide enhanced diagnostic performance.

Section snippets

Material and methods

This observational cross-sectional study was conducted at the Glaucoma Division, Jules Stein Eye Institute, University of California at Los Angeles. The University’s institutional review board approved this investigation, and all participants gave their approval and signed an informed consent form to participate in the study. Eligible participants were recruited from a clinical database or were enrolled prospectively. Patients with glaucoma and the control group met the following criteria: age

Results

One eye each from 42 normal control subjects and 65 patients with glaucoma were recruited. The demographic characteristics of the study sample are shown in Table 1. The mean age of the control group was not significantly different from that of the glaucoma group (P = .098 and 0.092 for the entire glaucoma and early glaucoma groups, respectively). Average mean deviation (±SD) was 0.0 ± 1.3 dB for normal eyes, −5.3 ± 5.0 dB for all patients with glaucoma, and −2.5 ± 1.6 dB for early glaucomatous

Discussion

The current investigation was carried out to evaluate and compare the performance of the fast RNFL thickness and fast optic disk algorithms of the OCT. The RNFL thickness in the 7 o’clock sector and in the inferior quadrant had the highest AUC for discrimination between normal eyes and eyes with glaucoma. The RNFL thickness in the 7 o’clock sector was also the most sensitive (86% at 90% specificity) among the RNFL thickness parameters. The vertical C/D ratio, HIRW, and VIRA from the automated

Anita Manassakorn, MD, is Visiting Assistant Professor, Glaucoma Division, at Jules Stein Eye Institute, UCLA, Los Angeles, CA and Instructor in Ophthalmology, at Chiang Mai University, Chiang Mai, Thailand. Her research interests include imaging in glaucoma, visual field progression, and glaucoma management.

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Proper estimation of the cup-to-disc ratio (C/D ratio) plays a significant role in ophthalmic examinations, and it is urgent to improve the efficiency of C/D ratio automatic measurement. Therefore, we propose a new method for measuring the C/D ratio of OCTs in normal subjects. Firstly, the end-to-end deep convolution network is used to segment and detect the inner limiting membrane (ILM) and the two Bruch's membrane opening (BMO) terminations. Then, we introduce an ellipse fitting technique to post-process the edge of the optic disc. Finally, the proposed method is evaluated on 41 normal subjects using the optic-disc-area scanning mode of three machines: BV1000, Topcon 3D OCT-1, and Nidek ARK-1. In addition, pairwise correlation analyses are carried out to compare the C/D ratio measurement method of BV1000 to existing commercial OCT machines as well as other state-of-the-art methods. The correlation coefficient between the C/D ratio calculated by BV1000 and the C/D ratio calculated by manual annotation is 0.84, which indicates that the proposed method has a strong correlation with the results of manual annotation by ophthalmologists. Moreover, in comparison between BV1000, Topcon and Nidek in practical screening among normal subjects, the proportion of the C/D ratio less than 0.6 calculated by BV1000 accounts for 96.34%, which is the closest to the clinical statistics among the three OCT machines. The above experimental results and analysis show that the proposed method performs well in cup and disc detection and C/D ratio measurement, and compared with the existing commercial OCT equipment, the C/D ratio measurement results are relatively close to reality, which has certain clinical application value.
Performances of Machine Learning in Detecting Glaucoma Using Fundus and Retinal Optical Coherence Tomography Images: A Meta-Analysis
2022, American Journal of Ophthalmology
Citation Excerpt :
The Supplemental Table summarizes the relevant characteristics of included studies examining ML classifier's performance on detecting glaucoma using automated image analysis. Of all included studies, 73 (69.5%) used fundus photographs,18-90 30 (28.6%) used retinal OCT images,17,91-119 and 2 (1.9%) tested ML using both types of images.120,121 Fifty-three studies (50.5%) performed at least 1 session of independent validation, and 24 (22.9%) performed at least 1 session of external validation.
To evaluate the performance of machine learning (ML) in detecting glaucoma using fundus and retinal optical coherence tomography (OCT) images.
Meta-analysis.
PubMed and EMBASE were searched on August 11, 2021. A bivariate random-effects model was used to pool ML's diagnostic sensitivity, specificity, and area under the curve (AUC). Subgroup analyses were performed based on ML classifier categories and dataset types.
One hundred and five studies (3.3%) were retrieved. Seventy-three (69.5%), 30 (28.6%), and 2 (1.9%) studies tested ML using fundus, OCT, and both image types, respectively. Total testing data numbers were 197,174 for fundus and 16,039 for OCT. Overall, ML showed excellent performances for both fundus (pooled sensitivity = 0.92 [95% CI, 0.91-0.93]; specificity = 0.93 [95% CI, 0.91-0.94]; and AUC = 0.97 [95% CI, 0.95-0.98]) and OCT (pooled sensitivity = 0.90 [95% CI, 0.86-0.92]; specificity = 0.91 [95% CI, 0.89-0.92]; and AUC = 0.96 [95% CI, 0.93-0.97]). ML performed similarly using all data and external data for fundus and the external test result of OCT was less robust (AUC = 0.87). When comparing different classifier categories, although support vector machine showed the highest performance (pooled sensitivity, specificity, and AUC ranges, 0.92-0.96, 0.95-0.97, and 0.96-0.99, respectively), results by neural network and others were still good (pooled sensitivity, specificity, and AUC ranges, 0.88-0.93, 0.90-0.93, 0.95-0.97, respectively). When analyzed based on dataset types, ML demonstrated consistent performances on clinical datasets (fundus AUC = 0.98 [95% CI, 0.97-0.99] and OCT AUC = 0.95 [95% 0.93-0.97]).
Performance of ML in detecting glaucoma compares favorably to that of experts and is promising for clinical application. Future prospective studies are needed to better evaluate its real-world utility.
Study of correlation between stereopsis and retinal nerve fiber layer thickness in cases of glaucoma
2021, Medical Journal Armed Forces India
Glaucoma is an important and common optic neuropathy characterized by progressive loss of retinal ganglion cells and associated morphological changes to the optic nerve and retinal nerve fiber layer (RNFL). The most common assessment of visual function in glaucoma uses perimetric measurements of visual sensitivity. Only few studies have evaluated the binocular function in patients with glaucoma. This study was taken up to establish the correlation of RNFL thickness, in glaucoma, with near and distance stereopsis.
This pilot, cross-sectional observational study included 100 diagnosed cases of glaucoma and 100 normal participants as controls, studied over a period of one year. The records of all the participants were checked, and only established cases of glaucoma after fulfilling the inclusion and exclusion criteria were included. Analysis of the RNFL using spectral-domain optical coherence tomography was carried out. All the participants were thereafter evaluated for stereoacuity by near (at 40 inches) and distance (at 3 meter) Randot stereoacuity charts.
There was a negative correlation between the RNFL thickness and the absolute value of streoacuity (-0.303 for distance versus -0.101 for near in cases and -0.308 for distance and -0.416 for near in control group), decreasing the actual functional stereoacuity, therefore the cases with lower RNFL thickness had lower stereoacuity both for distance and near, however it was statistically significant only for distance (p=0.002).
Functional aspects, such as stereoacuity, may also be affected in the glaucoma because of decrease in RNFL thickness. Therefore, binocular status should also be evaluated in cases of glaucoma.
Adaptive optics optical coherence tomography in glaucoma
2017, Progress in Retinal and Eye Research
Citation Excerpt :
The first commercially available TD-OCT (Stratus OCT, Zeiss, Dublin, CA), featured nominal axial resolution of 10 μm, transverse resolution of 20 μm, and scanning speed of 400 axial scans per second. However, this technology was limited by suboptimal resolution and slow scan acquisition rates (Hougaard et al., 2007a, b; Lee et al., 2009; Manassakorn et al., 2006; Medeiros et al., 2005, 2009b; Naithani et al., 2007; Nouri-Mahdavi et al., 2008; Parikh et al., 2007; Wollstein et al., 2005). Hardware advances in commercial systems improved resolution and increased scanning speeds.
Since the introduction of commercial optical coherence tomography (OCT) systems, the ophthalmic imaging modality has rapidly expanded and it has since changed the paradigm of visualization of the retina and revolutionized the management and diagnosis of neuro-retinal diseases, including glaucoma. OCT remains a dynamic and evolving imaging modality, growing from time-domain OCT to the improved spectral-domain OCT, adapting novel image analysis and processing methods, and onto the newer swept-source OCT and the implementation of adaptive optics (AO) into OCT. The incorporation of AO into ophthalmic imaging modalities has enhanced OCT by improving image resolution and quality, particularly in the posterior segment of the eye. Although OCT previously captured in-vivo cross-sectional images with unparalleled high resolution in the axial direction, monochromatic aberrations of the eye limit transverse or lateral resolution to about 15–20 μm and reduce overall image quality. In pairing AO technology with OCT, it is now possible to obtain diffraction-limited resolution images of the optic nerve head and retina in three-dimensions, increasing resolution down to a theoretical 3 μm³. It is now possible to visualize discrete structures within the posterior eye, such as photoreceptors, retinal nerve fiber layer bundles, the lamina cribrosa, and other structures relevant to glaucoma. Despite its limitations and barriers to widespread commercialization, the expanding role of AO in OCT is propelling this technology into clinical trials and onto becoming an invaluable modality in the clinician's arsenal.
Retinal Nerve Fiber Layer (RNFL) Photography and Computer Analysis
2015, Glaucoma: Second Edition
Thickness related textural properties of retinal nerve fiber layer in color fundus images
2014, Computerized Medical Imaging and Graphics
Citation Excerpt :
However, these error values are acceptable, since they are still around the level of differences between various OCT devices (∼10–25 μm) [10,19,41]. Moreover, the mean values of RMSEP are also comparable with the general difference between normal and glaucomatous RNFL thickness (∼20–25 μm), as reported by studies [22,23]. As mentioned above, one drawback concerns the blood vessels that cover rather large area of the retina, especially in the ONH surroundings.
Images of ocular fundus are routinely utilized in ophthalmology. Since an examination using fundus camera is relatively fast and cheap procedure, it can be used as a proper diagnostic tool for screening of retinal diseases such as the glaucoma. One of the glaucoma symptoms is progressive atrophy of the retinal nerve fiber layer (RNFL) resulting in variations of the RNFL thickness. Here, we introduce a novel approach to capture these variations using computer-aided analysis of the RNFL textural appearance in standard and easily available color fundus images. The proposed method uses the features based on Gaussian Markov random fields and local binary patterns, together with various regression models for prediction of the RNFL thickness. The approach allows description of the changes in RNFL texture, directly reflecting variations in the RNFL thickness. Evaluation of the method is carried out on 16 normal (“healthy”) and 8 glaucomatous eyes. We achieved significant correlation (normals: ρ = 0.72 ± 0.14; p ≪ 0.05, glaucomatous: ρ = 0.58 ± 0.10; p ≪ 0.05) between values of the model predicted output and the RNFL thickness measured by optical coherence tomography, which is currently regarded as a standard glaucoma assessment device. The evaluation thus revealed good applicability of the proposed approach to measure possible RNFL thinning.

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: Supported by a grant from “Research to Prevent Blindness” and from the National Institutes of Health R01 EY12738.

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Original articleComparison of Retinal Nerve Fiber Layer Thickness and Optic Disk Algorithms with Optical Coherence Tomography to Detect Glaucoma

Purpose

Design

Methods

Results

Conclusion

Section snippets

Material and methods

Results

Discussion

Ophthalmology

Ophthalmology

Ophthalmology

Am J Ophthalmol

Am J Ophthalmol

Am J Ophthalmol

Am J Ophthalmol

Am J Ophthalmol

Am J Ophthalmol

Am J Ophthalmol

Ophthalmology

Ophthalmology

Original article
Comparison of Retinal Nerve Fiber Layer Thickness and Optic Disk Algorithms with Optical Coherence Tomography to Detect Glaucoma