Detection of glaucoma progression with stratus OCT retinal nerve fiber layer, optic nerve head, and macular thickness measurements

HDB-Net: hierarchical dual-branch network for retinal layer segmentation in diseased OCT images

Optical coherence tomography (OCT) retinal layer segmentation is a critical procedure of the modern ophthalmic process, which can be used for diagnosis and treatment of diseases such as diabetic macular edema (DME) and multiple sclerosis (MS). Due to the difficulties of low OCT image quality, highly similar retinal interlayer morphology, and the uncertain presence, shape and size of lesions, the existing algorithms do not perform well. In this work, we design an HDB-Net network for retinal layer segmentation in diseased OCT images, which solves this problem by combining global and detailed features. First, the proposed network uses a Swin transformer and Res50 as a parallel backbone network, combined with the pyramid structure in UperNet, to extract global context and aggregate multi-scale information from images. Secondly, a feature aggregation module (FAM) is designed to extract global context information from the Swin transformer and local feature information from ResNet by introducing mixed attention mechanism. Finally, the boundary awareness and feature enhancement module (BA-FEM) is used to extract the retinal layer boundary information and topological order from the low-resolution features of the shallow layer. Our approach has been validated on two public datasets, and Dice scores were 87.61% and 92.44, respectively, both outperforming other state-of-the-art technologies.

Comparison of Retinal Nerve Fiber Layer and Ganglion Cell Complex Rates of Change in Patients With Moderate to Advanced Glaucoma

PURPOSE

To compare ganglion cell complex (GCC) and retinal nerve fiber layer (RNFL) rates of change (RoC) in eyes with central or moderate to advanced glaucoma.

DESIGN

Prospective cohort study.

PARTICIPANTS

A total of 918 matched macular and RNFL OCT scan pairs from 109 eyes (109 patients) enrolled in the Advanced Glaucoma Progression Study with ≥2 years of follow-up and ≥4 OCT scans.

METHODS

We exported GCC and RNFL thickness measurements in 49 central macular superpixels and 12 RNFL clock-hour sectors, respectively. We applied our latest Bayesian hierarchical longitudinal model to estimate population and subject-specific baseline thickness (intercepts) and rates of change (RoC) in macular superpixels and RNFL sectors. Global RNFL and GCC RoC were analyzed in a single bivariate longitudinal model to properly compare them accounting for the correlation between their RoC.

MAIN OUTCOME MEASURES

Proportion of significant negative (deteriorating) and positive (improving) RoC expressed in μm/year. Standardized RoC were calculated by dividing RoC by the corresponding population SD. Analyses were repeated in eyes with visual field mean deviation (MD) ≤-6 and > -6 dB.

RESULTS

Average (SD) 24-2 visual field MD and follow-up length were -8.6 (6.3) dB and 4.2 (0.5) years, respectively. Global RNFL RoC (-0.70 µm/year) were faster than GCC (-0.44 µm/year) (P < .001); corresponding normalized RoC were not significantly different (P = .052). In bivariate analysis, patients with a significant negative global RNFL RoC (n = 63, 57%) or GCC (n = 56, 51%) frequently did so for both outcomes (n = 49, 45%). The average proportion of significantly decreasing RNFL sectors within an eye was 30.7% in eyes with MD > -6 dB compared to 20.5% in those with MD ≤ -6 dB (P = .014); the proportions for GCC superpixels were 21.1% versus 18.7%, respectively (P = .63).

CONCLUSIONS

Both GCC and RNFL measures can detect structural progression in glaucoma patients with central damage or moderate to advanced glaucoma. The clinical utility of RNFL imaging decreases with worsening severity of glaucoma.

Time to Glaucoma Progression Detection by Optical Coherence Tomography and Visual Field in Glaucoma Individuals of African Descent

PURPOSE

To examine the time to glaucoma progression detection by retinal nerve fiber layer thickness (RNFLT) and visual field (VF) among individuals of African descent (AD).

DESIGN

Retrospective cohort study.

METHODS

This multicenter study included eyes with glaucoma from individuals of AD from the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study with ≥2 years/5 visits of optic nerve head RNFLT and 24-2 VF examinations.

INTERVENTION OR OBSERVATION PROCEDURE

Rates of VF mean deviation (MD) and RNFLT worsening were analyzed using linear mixed-effects models, and longitudinal data were simulated using the variability estimates.

MAIN OUTCOME MEASURE

The simulated time to detect trend-based glaucoma progression was assessed with assumed rates of VF MD and RNFLT change derived from the cohort (25th, 50th, and 75th percentile [as p25, median, and p75] slopes and mean slopes). Severity-stratified analyses were also performed.

RESULTS

We included 184 eyes from 128 subjects of AD (mean baseline age 63.4 years; VF MD -4.2 dB; RNFLT 80.2 µm). The p25, median, mean, and p75 rates of change were -0.43, -1.01, -1.15, and -1.64 µm per year for RNFLT, and 0.00, -0.21, -0.30, and -0.51 dB per year for VF MD, respectively. Compared with VF MD, RNFLT showed an overall shorter mean time to progression detection (time difference 0.4-1.7 years), with the mean rates showing the largest difference (RNFLT 5.2 years vs VF MD 6.9 years). Similarly, we found an overall shorter time to detect RNFLT progression, compared with that of VF MD progression, in eyes with mild glaucoma (≥1 year earlier) and in eyes with moderate to advanced glaucoma (∼0.5 year earlier).

CONCLUSIONS

Computer simulation showed a potentially shorter time to detect RNFLT progression than VF MD progression in eyes from individuals of AD. Our findings support the importance of using RNFLT to detect progressive glaucoma in individuals of AD.

Prediction of Visual Field Progression with Baseline and Longitudinal Structural Measurements Using Deep Learning

PURPOSE

Identifying glaucoma patients at high risk of progression based on widely available structural data is an unmet task in clinical practice. We test the hypothesis that baseline or serial structural measures can predict visual field (VF) progression with deep learning (DL).

DESIGN

Development of a DL algorithm to predict VF progression.

METHODS

3,079 eyes (1,765 patients) with various types of glaucoma and ≥5 VFs, and ≥3 years of follow-up from a tertiary academic center were included. Serial VF mean deviation (MD) rates of change were estimated with linear-regression. VF progression was defined as negative MD slope with p<0.05. A Siamese Neural Network with ResNet-152 backbone pre-trained on ImageNet was designed to predict VF progression using serial optic-disc photographs (ODP), and baseline retinal nerve fiber layer (RNFL) thickness. We tested the model on a separate dataset (427 eyes) with RNFL data from different OCT. The Main Outcome Measure was Area under ROC curve (AUC).

RESULTS

Baseline average (SD) MD was 3.4 (4.9)dB. VF progression was detected in 900 eyes (29%). AUC (95% CI) for model incorporating baseline ODP and RNFL thickness was 0.813 (0.757-0.869). After adding the second and third ODPs, AUC increased to 0.860 and 0.894, respectively (p<0.027). This model also had highest AUC (0.911) for predicting fast progression (MD rate <1.0 dB/year). Model's performance was similar when applied to second dataset using RNFL data from another OCT device (AUC=0.893; 0.837-0.948).

CONCLUSIONS

DL model predicted VF progression with clinically relevant accuracy using baseline RNFL thickness and serial ODPs and can be implemented as a clinical tool after further validation.

Time to Glaucoma Progression Detection by Optical Coherence Tomography in Individuals of African and European Descents

PURPOSE

To examine the time to detectable retinal nerve fiber layer thickness (RNFLT) progression by optical coherence tomography (OCT) among glaucoma patients of African descent (AD) and European descent (ED).

DESIGN

Retrospective cohort study.

METHODS

AD and ED glaucoma eyes from the Diagnostic Innovations in Glaucoma Study (DIGS)/African Descent and Glaucoma Evaluation Study (ADAGES) with ≥2 years/4 visits of optic nerve head RNFLT measurements were included after homogenization on age, diagnosis, and baseline visual field (VF) measurement. RNFLT variability estimates based on linear mixed-effects models were used to simulate longitudinal RNFLT data for both races. Times to trend-based RNFLT progression detection were calculated under standardized scenarios (same RNFLT baseline/thinning rates for both races) and real-world scenarios (AD and ED cohort-specific RNFLT baseline/thinning rates).

RESULTS

We included 332 and 542 eyes (216 and 317 participants) of AD and ED, respectively. In standardized scenarios, the time to detect RNFLT progression appeared to be similar (difference, <0.2 years) for AD and ED across different assumed RNFLT thinning rates/baseline. In real-world scenarios, compared to ED, AD had a faster RNFLT thinning rate (-0.8 vs -0.6 µm/y) and thicker baseline RNFLT (84.6 vs 81.8 µm). With a faster thinning rate, the mean (SD) time to progression detection was shorter in AD (4.8 [2.0] vs ED: 5.4 [2.4] years), and the 5-year progression rate appeared to be higher (AD: 59% vs ED: 47%).

CONCLUSIONS

Time to progression detection was similar for both races when assuming identical RNFLT baseline/thinning rates, and shorter in AD eyes under real-world simulation when AD had faster RNFLT thinning. In contrast to prior results on VF, which detected progression later in AD eyes than in ED eyes, OCT may detect progression more consistently across these races.

Assessment of linear regression of peripapillary optical coherence tomography retinal nerve fiber layer measurements to forecast glacuoma trajectory

Linear regression of optical coherence tomography measurements of peripapillary retinal nerve fiber layer thickness is often used to detect glaucoma progression and forecast future disease course. However, current measurement frequencies suggest that clinicians often apply linear regression to a relatively small number of measurements (e.g., less than a handful). In this study, we estimate the accuracy of linear regression in predicting the next reliable measurement of average retinal nerve fiber layer thickness using Zeiss Cirrus optical coherence tomography measurements of average retinal nerve fiber layer thickness from a sample of 6,471 eyes with glaucoma or glaucoma-suspect status. Linear regression is compared to two null models: no glaucoma worsening, and worsening due to aging. Linear regression on the first M ≥ 2 measurements was significantly worse at predicting a reliable M+1st measurement for 2 ≤ M ≤ 6. This range was reduced to 2 ≤ M ≤ 5 when retinal nerve fiber layer thickness measurements were first "corrected" for scan quality. Simulations based on measurement frequencies in our sample-on average 393 ± 190 days between consecutive measurements-show that linear regression outperforms both null models when M ≥ 5 and the goal is to forecast moderate (75th percentile) worsening, and when M ≥ 3 for rapid (90th percentile) worsening. If linear regression is used to assess disease trajectory with a small number of measurements over short time periods (e.g., 1-2 years), as is often the case in clinical practice, the number of optical coherence tomography examinations needs to be increased.

A Model of Progression to Help Identify Macular Damage Due to Glaucoma

The central macula contains a thick donut shaped region of the ganglion cell layer (GCL) that surrounds the fovea. This region, which is about 12 degrees (3.5 mm) in diameter, is essential for everyday functions such as driving, reading, and face recognition. Here, we describe a model of progression of glaucomatous damage to this GCL donut. This model is based upon assumptions supported by the literature, and it predicts the patterns of glaucomatous damage to the GCL donut, as seen with optical coherence tomography (OCT). After describing the assumptions and predictions of this model, we test the model against data from our laboratory, as well as from the literature. Finally, three uses of the model are illustrated. One, it provides an aid to help clinicians focus on the essential central macula and to alert them to look for other, non-glaucomatous causes, when the GCL damage does not fit the pattern predicted by the model. Second, the patterns of progression predicted by the model suggest alternative end points for clinical trials. Finally, the model provides a heuristic for future research concerning the anatomic basis of glaucomatous damage.

Predicting Visual Field Worsening with Longitudinal OCT Data Using a Gated Transformer Network

PURPOSE

To identify visual field (VF) worsening from longitudinal OCT data using a gated transformer network (GTN) and to examine how GTN performance varies for different definitions of VF worsening and different stages of glaucoma severity at baseline.

DESIGN

Retrospective longitudinal cohort study.

PARTICIPANTS

A total of 4211 eyes (2666 patients) followed up at the Johns Hopkins Wilmer Eye Institute with at least 5 reliable VF results and 1 reliable OCT scan within 1 year of each reliable VF test.

METHODS

For each eye, we used 3 trend-based methods (mean deviation [MD] slope, VF index slope, and pointwise linear regression) and 3 event-based methods (Guided Progression Analysis, Collaborative Initial Glaucoma Treatment Study scoring system, and Advanced Glaucoma Intervention Study [AGIS] scoring system) to define VF worsening. Additionally, we developed a "majority of 6" algorithm (M6) that classifies an eye as worsening if 4 or more of the 6 aforementioned methods classified the eye as worsening. Using these 7 reference standards for VF worsening, we trained 7 GTNs that accept a series of at least 5 as input OCT scans and provide as output a probability of VF worsening. Gated transformer network performance was compared with non-deep learning models with the same serial OCT input from previous studies-linear mixed-effects models (MEMs) and naive Bayes classifiers (NBCs)-using the same training sets and reference standards as for the GTN.

MAIN OUTCOME MEASURES

Area under the receiver operating characteristic curve (AUC).

RESULTS

The M6 labeled 63 eyes (1.50%) as worsening. The GTN achieved an AUC of 0.97 (95% confidence interval, 0.88-1.00) when trained with M6. Gated transformer networks trained and optimized with the other 6 reference standards showed an AUC ranging from 0.78 (MD slope) to 0.89 (AGIS). The 7 GTNs outperformed all 7 MEMs and all 7 NBCs accordingly. Gated transformer network performance was worse for eyes with more severe glaucoma at baseline.

CONCLUSIONS

Gated transformer network models trained with OCT data may be used to identify VF worsening. After further validation, implementing such models in clinical practice may allow us to track functional worsening of glaucoma with less onerous structural testing.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references.

Optical Coherence Tomography and Optical Coherence Tomography Angiography: Essential Tools for Detecting Glaucoma and Disease Progression

Early diagnosis and detection of disease progression are critical to successful therapeutic intervention in glaucoma, the leading cause of irreversible blindness worldwide. Optical coherence tomography (OCT) is a non-invasive imaging technique that allows objective quantification in vivo of key glaucomatous structural changes in the retina and the optic nerve head (ONH). Advances in OCT technology have increased the scan speed and enhanced image quality, contributing to early glaucoma diagnosis and monitoring, as well as the visualization of critically important structures deep within the ONH, such as the lamina cribrosa. OCT angiography (OCTA) is a dye-free technique for noninvasively assessing ocular microvasculature, including capillaries within each plexus serving the macula, peripapillary retina and ONH regions, as well as the deeper vessels of the choroid. This layer-specific assessment of the microvasculature has provided evidence that retinal and choroidal vascular impairments can occur during early stages of glaucoma, suggesting that OCTA-derived measurements could be used as biomarkers for enhancing detection of glaucoma and its progression, as well as to reveal novel insights about pathophysiology. Moreover, these innovations have demonstrated that damage to the macula, a critical region for the vision-related quality of life, can be observed in the early stages of glaucomatous eyes, leading to a paradigm shift in glaucoma monitoring. Other advances in software and hardware, such as artificial intelligence-based algorithms, adaptive optics, and visible-light OCT, may further benefit clinical management of glaucoma in the future. This article reviews the utility of OCT and OCTA for glaucoma diagnosis and disease progression detection, emphasizes the importance of detecting macula damage in glaucoma, and highlights the future perspective of OCT and OCTA. We conclude that the OCT and OCTA are essential glaucoma detection and monitoring tools, leading to clinical and economic benefits for patients and society.

Comparing the Accuracy of Peripapillary OCT Scans and Visual Fields to Detect Glaucoma Worsening

PURPOSE

To compare the accuracy of detecting moderate and rapid rates of glaucoma worsening over a 2-year period with different numbers of OCT scans and visual field (VF) tests in a large sample of glaucoma and glaucoma suspect eyes.

DESIGN

Descriptive and simulation study.

PARTICIPANTS

The OCT sample comprised 12 150 eyes from 7392 adults with glaucoma or glaucoma suspect status followed up at the Wilmer Eye Institute from 2013 through 2021. The VF sample comprised 20 583 eyes from 10 958 adults from the same database. All eyes had undergone at least 5 measurements over follow-up from the Zeiss Cirrus OCT or Humphrey Field Analyzer.

METHODS

Within-eye rates of change in retinal nerve fiber layer (RNFL) thickness and mean deviation (MD) were measured using linear regression. For each measured rate, simulated measurements of RNFL thickness and MD were generated using the distributions of residuals. Simulated rates of change for different numbers of OCT scans and VF tests over a 2-year period were used to estimate the accuracy of detecting moderate (75th percentile) and rapid (90th percentile) worsening for OCT and VF. Accuracy was defined as the percentage of simulated eyes in which the true rate of worsening (the rate without measurement error) was at or less than a criterion rate (e.g., 75th or 90th percentile).

MAIN OUTCOME MEASURES

The accuracy of diagnosing moderate and rapid rates of glaucoma worsening for different numbers of OCT scans and VF tests over a 2-year period.

RESULTS

Accuracy was less than 50% for both OCT and VF when diagnosing worsening after a 2-year period. OCT accuracy was 5 to 10 percentage points higher than VF accuracy at detecting moderate worsening and 10 to 15 percentage points higher for rapid worsening. Accuracy increased by more than 17 percentage points when using both OCT and VF to detect worsening, that is, when relying on either OCT or VF to be accurate.

CONCLUSIONS

More frequent OCT scans and VF tests are needed to improve the accuracy of diagnosing glaucoma worsening. Accuracy greatly increases when relying on both OCT and VF to detect worsening.

FINANCIAL DISCLOSURE(S)

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Deep Learning-Assisted Detection of Glaucoma Progression in Spectral-Domain OCT

PURPOSE

To develop and validate a deep learning (DL) model for detection of glaucoma progression using spectral-domain (SD)-OCT measurements of retinal nerve fiber layer (RNFL) thickness.

DESIGN

Retrospective cohort study.

PARTICIPANTS

A total of 14 034 SD-OCT scans from 816 eyes from 462 individuals.

METHODS

A DL convolutional neural network was trained to assess SD-OCT RNFL thickness measurements of 2 visits (a baseline and a follow-up visit) along with time between visits to predict the probability of glaucoma progression. The ground truth was defined by consensus from subjective grading by glaucoma specialists. Diagnostic performance was summarized by the area under the receiver operator characteristic curve (AUC), sensitivity, and specificity, and was compared with conventional trend-based analyses of change. Interval likelihood ratios were calculated to determine the impact of DL model results in changing the post-test probability of progression.

MAIN OUTCOME MEASURES

The AUC, sensitivity, and specificity of the DL model.

RESULTS

The DL model had an AUC of 0.938 (95% confidence interval [CI], 0.921-0.955), with sensitivity of 87.3% (95% CI, 83.6%-91.6%) and specificity of 86.4% (95% CI, 79.9%-89.6%). When matched for the same specificity, the DL model significantly outperformed trend-based analyses. Likelihood ratios for the DL model were associated with large changes in the probability of progression in the vast majority of SD-OCT tests.

CONCLUSIONS

A DL model was able to assess the probability of glaucomatous structural progression from SD-OCT RNFL thickness measurements. The model agreed well with expert judgments and outperformed conventional trend-based analyses of change, while also providing indication of the likely locations of change.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references.

Evidence-Based Guidelines for the Number of Peripapillary OCT Scans Needed to Detect Glaucoma Worsening

PURPOSE

To estimate the number of OCT scans necessary to detect moderate and rapid rates of retinal nerve fiber layer (RNFL) thickness worsening at different levels of accuracy using a large sample of glaucoma and glaucoma-suspect eyes.

DESIGN

Descriptive and simulation study.

PARTICIPANTS

Twelve thousand one hundred fifty eyes from 7392 adult patients with glaucoma or glaucoma-suspect status followed up at the Wilmer Eye Institute from 2013 through 2021. All eyes had at least 5 measurements of RNFL thickness on the Cirrus OCT (Carl Zeiss Meditec) with signal strength of 6 or more.

METHODS

Rates of RNFL worsening for average RNFL thickness and for the 4 quadrants were measured using linear regression. Simulations were used to estimate the accuracy of detecting worsening-defined as the percentage of patients in whom the true rate of RNFL worsening was at or less than different criterion rates of worsening when the OCT-measured rate was also at or less than these criterion rates-for two different measurement strategies: evenly spaced (equal time intervals between measurements) and clustered (approximately half the measurements at each end point of the period).

MAIN OUTCOME MEASURES

The 75th percentile (moderate) and 90th percentile (rapid) rates of RNFL worsening for average RNFL thickness and the accuracy of diagnosing worsening at these moderate and rapid rates.

RESULTS

The 75th and 90th percentile rates of worsening for average RNFL thickness were -1.09 μm/year and -2.35 μm/year, respectively. Simulations showed that, for the average measurement frequency in our sample of approximately 3 OCT scans over a 2-year period, moderate and rapid RNFL worsening were diagnosed accurately only 47% and 40% of the time, respectively. Estimates for the number of OCT scans needed to achieve a range of accuracy levels are provided. For example, 60% accuracy requires 7 measurements to detect both moderate and rapid worsening within a 2-year period if the more efficient clustered measurement strategy is used.

CONCLUSIONS

To diagnose RNFL worsening more accurately, the number of OCT scans must be increased compared with current clinical practice. A clustered measurement strategy reduces the number of scans required compared with evenly spacing measurements.

[Optical coherence tomography and optical coherence tomography angiography for detecting glaucoma progression. Part 1. Study methods, measurement variability and the role of age-related changes]

This paper reviews the literature on the role of optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) in the diagnosis of glaucoma and considers the significance of evaluating retinal nerve fiber layer and ganglion cell complex in assessment of glaucoma progression, variability and reproducibility of the method, as well as the influence of age-related retinal changes on the results, analyzes the role of OCTA in glaucoma monitoring. Optical coherence tomography is a modern standard for glaucoma diagnosis and monitoring, and OCTA shows high potential as an auxiliary diagnostic tool.

Effect of Testing Frequency on the Time to Detect Glaucoma Progression With Optical Coherence Tomography (OCT) and OCT Angiography

PURPOSE

To determine how the frequency of testing affects the time required to detect statistically significant glaucoma progression for circumpapillary retinal nerve fiber layer (cpRNFL) with optical coherence tomography (OCT) and circumpapillary capillary density (cpCD) with OCT angiography (OCTA).

DESIGN

Retrospective, observational cohort study.

METHODS

In this longitudinal study, 156 eyes of 98 patients with glaucoma followed up over an average of 3.5 years were enrolled. Participants with 4 or more OCT and OCTA tests were included to measure the longitudinal rates of cpRNFL thickness and cpCD change over time using linear regression. Estimates of variability were then used to re-create real-world cpRNFL and cpCD data by computer simulation to evaluate the time required to detect progression for various loss rates and different testing frequencies.

RESULTS

The time required to detect a statistically significant negative cpRNFL and cpCD slope decreased as the testing frequency increased, albeit not proportionally. cpCD detected progression slightly earlier than cpRNFL. Eighty percent of eyes with a cpCD loss of -1%/y were detected after 6.0, 4.2, and 4 years when testing was performed 1, 2, and 3 times per year, respectively. Progression in 80% of eyes with a cpRNFL loss of -1 µm/y was detected after 6.3, 5.0, and 4.2 years, respectively.

CONCLUSIONS

cpRNFL and cpCD are comparable in detecting progression. As there were only small changes in the time to detect progression when testing increased from 2 to 3 times per year, testing twice per year may provide sufficient information for detecting progression with either OCT or OCTA in clinical settings.

[Optical coherence tomography and optical coherence tomography angiography for detecting glaucoma progression. Part 2. Clinical and functional correlations, monitoring of advanced glaucoma and limitations of the method]

The purpose of this study is to analyze the literature on the role of optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) in the diagnosis of glaucoma. This review considers the structural and functional correlations observed during the progression of glaucomatous optic neuropathy, as well as the capabilities of the method in late glaucoma, describes the strengths and weaknesses of OCT and OCTA, and pays particular attention to the role of OCT in assessing the effectiveness of treatment. Optical coherence tomography is the main method for determining the progression of glaucoma, which plays a key role in the choice of treatment algorithm. However, the use of OCT in far advanced glaucoma has certain particularities and limitations. OCTA can be helpful in overcoming this problem.

Multivariate Longitudinal Modeling of Macular Ganglion Cell Complex: Spatiotemporal Correlations and Patterns of Longitudinal Change

Purpose

To investigate spatiotemporal correlations among ganglion cell complex (GCC) superpixel thickness measurements and explore underlying patterns of longitudinal change across the macular region.

Design

Longitudinal cohort study.

Subjects

One hundred eleven eyes from 111 subjects from the Advanced Glaucoma Progression Study with ≥ 4 visits and ≥ 2 years of follow-up.

Methods

We further developed our proposed Bayesian hierarchical model for studying longitudinal GCC thickness changes across macular superpixels in a cohort of glaucoma patients. Global priors were introduced for macular superpixel parameters to combine data across superpixels and better estimate population slopes and intercepts.

Main Outcome Measures

Bayesian residual analysis to inspect cross-superpixel correlations for subject random effects and residuals. Principal component analysis (PCA) to explore underlying patterns of longitudinal macular change.

Results

Average (standard deviation [SD]) follow-up and baseline 10-2 visual field mean deviation were 3.6 (0.4) years and -8.9 (5.9) dB, respectively. Superpixel-level random effects and residuals had the greatest correlations with nearest neighbors; correlations were higher in the superior than in the inferior region and strongest among random intercepts, followed by random slopes, residuals, and residual SDs. PCA of random intercepts showed a first large principal component (PC) across superpixels that approximated a global intercept, a second PC that contrasted the superior and inferior macula, and a third PC, contrasting inner and nasal superpixels with temporal and peripheral superpixels. PCs for slopes, residual SDs, and residuals were remarkably similar to those of random intercepts.

Conclusions

Introduction of cross-superpixel random intercepts and slopes is expected to improve estimation of population and subject parameters. Further model enhancement may be possible by including cross-superpixel random effects and correlations to address spatiotemporal relationships in longitudinal data sets.

Quantifying biomarkers of axonal degeneration in early glaucoma to find the disc at risk

To evaluate regional axonal-related parameters as a function of disease stage in primary open angle glaucoma (POAG) and visual field (VF) sensitivity. Spectral domain optical coherence tomography was used to acquire 20° scans of POAG (n = 117) or healthy control (n = 52) human optic nerve heads (ONHs). Region specific and mean nerve fibre layer (NFL) thicknesses, border NFL and peripapillary NFL, minimum rim width (MRW)/ area (MRA) and prelamina thickness; and volume were compared across POAG disease stages and with visual field sensitivity. Differences identified between early glaucoma (EG), preperimetric glaucoma (PG) and control (C) ONHs included thinner PG prelamina regions than in controls (p < 0.05). Mean border NFL was thinner in EG (p < 0.001) and PG (p = 0.049) compared to control eyes; and EG mean, and inferior and ST, border NFL was thinner than in PG (p < 0.01). Mean, superior and inferior PG peripapillary NFL were thinner than in controls (p < 0.05), and EG ST peripapillary NFL was thinner than in PG (p = 0.023). MRW differences included: PG SN and inferior less than in controls (p < 0.05); thinner EG mean regional, inferior, nasal, and ST MRW versus PG MRW (p < 0.05). Regional border NFL, peripapillary NFL, MRW, MRA, prelamina thickness (except centre, p = 0.127) and prelamina volume (p < 0.05) were significantly associated with VF mean deviation (MD). Novel axon-derived indices hold potential as biomarkers to detect early glaucoma and identify ONHs at risk.

Circumpapillary OCT-focused hybrid learning for glaucoma grading using tailored prototypical neural networks

Glaucoma is one of the leading causes of blindness worldwide and Optical Coherence Tomography (OCT) is the quintessential imaging technique for its detection. Unlike most of the state-of-the-art studies focused on glaucoma detection, in this paper, we propose, for the first time, a novel framework for glaucoma grading using raw circumpapillary B-scans. In particular, we set out a new OCT-based hybrid network which combines hand-driven and deep learning algorithms. An OCT-specific descriptor is proposed to extract hand-crafted features related to the retinal nerve fibre layer (RNFL). In parallel, an innovative CNN is developed using skip-connections to include tailored residual and attention modules to refine the automatic features of the latent space. The proposed architecture is used as a backbone to conduct a novel few-shot learning based on static and dynamic prototypical networks. The k-shot paradigm is redefined giving rise to a supervised end-to-end system which provides substantial improvements discriminating between healthy, early and advanced glaucoma samples. The training and evaluation processes of the dynamic prototypical network are addressed from two fused databases acquired via Heidelberg Spectralis system. Validation and testing results reach a categorical accuracy of 0.9459 and 0.8788 for glaucoma grading, respectively. Besides, the high performance reported by the proposed model for glaucoma detection deserves a special mention. The findings from the class activation maps are directly in line with the clinicians' opinion since the heatmaps pointed out the RNFL as the most relevant structure for glaucoma diagnosis.

Agreement Between Trend-Based and Qualitative Analysis of the Retinal Nerve Fiber Layer Thickness for Glaucoma Progression on Spectral-Domain Optical Coherence Tomography

INTRODUCTION

To evaluate the agreement between trend-based analysis and qualitative assessment of the retinal nerve fiber layer (RNFL) thickness for glaucomatous progression on spectral-domain optical coherence tomography (SDOCT).

METHODS

Retrospective review of 190 eyes from 103 patients with glaucoma or suspected glaucoma that underwent SDOCT imaging during four consecutive clinic visits. Trend-based progression was characterized by a significantly negative slope. Progression by qualitative analysis was determined by review of raw SDOCT B-scans.

RESULTS

The slope was significantly greater in those with progression than without progression for both trend-based and qualitative analysis (p < 0.001). However, the qualitative grading classified a significantly greater proportion of eyes as progressing compared to trend-based analysis in both the superotemporal (ST) (23.2% vs. 10.5%, p = 0.001) and inferotemporal (IT) RNFL (27.4% vs 8.4%, p < 0.001). The trend-based and qualitative classifications of progression showed poor agreement in both the ST (kappa = 0.0135) and IT RNFL (kappa = 0.1222). The agreement between trend-based and qualitative analysis was lower for eyes with artifacts (ST = 58.11%; IT = 68.7%) than those without artifacts (ST = 80.2%; IT = 74.8%). Moreover, among eyes with artifacts, there was no significant difference in slope between those qualitatively categorized as progressing versus not progressing (p > 0.05).

CONCLUSIONS

Poor agreement was found between a trend-based and qualitative analysis of change in RNFL on SDOCT. Careful qualitative review of SDOCT imaging may identify specific areas of glaucoma progression not captured by trend-based methods, especially in the presence of artifacts. Such an approach may also prove useful for detecting glaucoma progression in a clinical setting when there are few data points available.

Predictive Analytics for Glaucoma Using Data From the All of Us Research Program

PURPOSE

To (1) use All of Us (AoU) data to validate a previously published single-center model predicting the need for surgery among individuals with glaucoma, (2) train new models using AoU data, and (3) share insights regarding this novel data source for ophthalmic research.

DESIGN

Development and evaluation of machine learning models.

METHODS

Electronic health record data were extracted from AoU for 1,231 adults diagnosed with primary open-angle glaucoma. The single-center model was applied to AoU data for external validation. AoU data were then used to train new models for predicting the need for glaucoma surgery using multivariable logistic regression, artificial neural networks, and random forests. Five-fold cross-validation was performed. Model performance was evaluated based on area under the receiver operating characteristic curve (AUC), accuracy, precision, and recall.

RESULTS

The mean (standard deviation) age of the AoU cohort was 69.1 (10.5) years, with 57.3% women and 33.5% black, significantly exceeding representation in the single-center cohort (P = .04 and P < .001, respectively). Of 1,231 participants, 286 (23.2%) needed glaucoma surgery. When applying the single-center model to AoU data, accuracy was 0.69 and AUC was only 0.49. Using AoU data to train new models resulted in superior performance: AUCs ranged from 0.80 (logistic regression) to 0.99 (random forests).

CONCLUSIONS

Models trained with national AoU data achieved superior performance compared with using single-center data. Although AoU does not currently include ophthalmic imaging, it offers several strengths over similar big-data sources such as claims data. AoU is a promising new data source for ophthalmic research.

Changes of Neuroretinal Rim and Retinal Nerve Fiber Layer Thickness Assessed by Optical Coherence Tomography After Filtration Surgery in Glaucomatous Eyes

Purpose

Lowering the intraocular pressure (IOP) in patients with primary open-angle glaucoma (POAG) with filtration surgery can induce morphological changes to the bulbus and structures of the retina. In this study, we have evaluated changes of Bruch's membrane-based parameters and retinal nerve fiber layer (RNFL) derived by spectral-domain optical coherence tomography (SD-OCT) in eyes that have undergone glaucoma filtration surgery.

Patients and Methods

SD-OCT imaging of the optic nerve head (ONH) and of the RNFL was performed in 54 eyes of 54 patients with medically uncontrolled POAG before and after IOP-lowering surgery (trabeculectomy or deep sclerectomy). The ONH parameter minimum rim width (MRW) and the size of the Bruch's membrane opening (BMO-Area) were derived from 24 radial B-scans centered on the ONH.

Results

The average preoperative IOP was 23.1 ± 7.5 mmHg. One month postoperatively, the average IOP decreased to 12.1 ± 4.6 mmHg (p < 0.01), which caused a significant increase in the thickness of neuroretinal rim. There was no significant change in the automatically detected BMO-Area (p = 0.32). The pressure-related increase in MRW correlated well with the postoperative IOP and cup-to-disc ratio (CDR). In regression analysis, the alteration in thickness of the neuroretinal rim could be well predicted in a model including CDR, change of IOP and mean deviation (MD) (R² = 0.414, p < 0.001). RNFL showed a significant increase as well.

Conclusion

IOP-lowering surgery in patients with medically uncontrolled POAG causes an increased thickness of the SD-OCT derived ONH parameters. The changes of the RNFL after surgery showed no significant correlations with IOP changes. In contrast to this, highly significant correlations of MRW values with the IOP could be observed. The BMO-Area remained completely stable A preferred use of RNFL for follow-up should be discussed.

Glaucoma Detection from Raw SD-OCT Volumes: A Novel Approach Focused on Spatial Dependencies

BACKGROUND AND OBJECTIVE

Glaucoma is the leading cause of blindness worldwide. Many studies based on fundus image and optical coherence tomography (OCT) imaging have been developed in the literature to help ophthalmologists through artificial-intelligence techniques. Currently, 3D spectral-domain optical coherence tomography (SD-OCT) samples have become more important since they could enclose promising information for glaucoma detection. To analyse the hidden knowledge of the 3D scans for glaucoma detection, we have proposed, for the first time, a deep-learning methodology based on leveraging the spatial dependencies of the features extracted from the B-scans.

METHODS

The experiments were performed on a database composed of 176 healthy and 144 glaucomatous SD-OCT volumes centred on the optic nerve head (ONH). The proposed methodology consists of two well-differentiated training stages: a slide-level feature extractor and a volume-based predictive model. The slide-level discriminator is characterised by two new, residual and attention, convolutional modules which are combined via skip-connections with other fine-tuned architectures. Regarding the second stage, we first carried out a data-volume conditioning before extracting the features from the slides of the SD-OCT volumes. Then, Long Short-Term Memory (LSTM) networks were used to combine the recurrent dependencies embedded in the latent space to provide a holistic feature vector, which was generated by the proposed sequential-weighting module (SWM).

RESULTS

The feature extractor reports AUC values higher than 0.93 both in the primary and external test sets. Otherwise, the proposed end-to-end system based on a combination of CNN and LSTM networks achieves an AUC of 0.8847 in the prediction stage, which outperforms other state-of-the-art approaches intended for glaucoma detection. Additionally, Class Activation Maps (CAMs) were computed to highlight the most interesting regions per B-scan when discerning between healthy and glaucomatous eyes from raw SD-OCT volumes.

CONCLUSIONS

The proposed model is able to extract the features from the B-scans of the volumes and combine the information of the latent space to perform a volume-level glaucoma prediction. Our model, which combines residual and attention blocks with a sequential weighting module to refine the LSTM outputs, surpass the results achieved from current state-of-the-art methods focused on 3D deep-learning architectures.

Local Glaucomatous Defects of the Circumpapillary Retinal Nerve Fiber Layer Show a Variety of Patterns of Progression

PRECIS

The region of glaucomatous progression, seen on optical coherence tomography (OCT) images of the circumpapillary retinal nerve fiber layer (cRNFL), increases in width and depth in all eyes, but shows a variety of different patterns of loss across eyes.

PURPOSE

The purpose of this study was to examine the patterns of cRNFL loss secondary to glaucomatous progression in a region associated with the superior hemifield of the 24-2/30-2 visual field (VF).

METHODS

Twenty-four eyes (20 patients) with a diagnosis of glaucoma and evidence of progression on OCT had OCT disc cube scans on at least 3 separate visits (mean follow-up 7.4 y; range: 3.9 to 11.4). Circumpapillary b-scans were derived after enface images were aligned to assure that the study region (ie, 0 to -135 degrees, where 0 degree is 9 o'clock, on a right eye) coincided. Within this region, a region of progression (ROP) was defined based on the loss in cRNFL thickness between the first and subsequent visits. The width of the ROP was determined, along with the locations of its leading (close to fixation) and trailing edges. In addition, for each ROP, the location and depth at the point of maximal loss, total loss, and average remaining retinal nerve fiber layer were measured.

RESULTS

The ROP proceeded both toward and away from fixation. Across eyes, the ROP varied widely in width (32 to 131 degrees, mean 82.7 degrees), location, and loss at point of deepest loss (22 to 99 μm, mean 52.9 μm), as well as total cRNFL loss.

CONCLUSIONS

All eyes showed a widening and deepening of the ROP, but a variety of different patterns of progressive cRNFL loss. Thus, one should expect considerable variation in patterns of VF loss. Furthermore, conventional metrics (global or quadrant cRNFL thickness) do not fully depict the progressive changes that can be appreciated by inspecting OCT images.

Global optical coherence tomography measures for detecting the progression of glaucoma have fundamental flaws

Objective

To understand the problems involved in using global OCT measures for detecting progression in early glaucoma.

Subjects/Methods

Eyes from 76 patients and 28 healthy controls (HC) had a least two OCT scans at least 1 year apart. To determine the 95% confidence intervals (CI), 151 eyes (49 HC and 102 patients) had at least two scans within 6 months. All eyes had 24-2 mean deviation ≥-6dB. The average (global) thicknesses of the circumpapillary retinal nerve fibre layer (cRNFL), G_ONH, and of the retinal ganglion cell layer plus inner plexiform layer (RGCLP), G_mac, were calculated. Using quantile regression, the 95% CI intervals were determined. Eyes outside the CIs were classified as “progressors.” For a reference standard (RS), four experts evaluated OCT and VF information.

Results

Compared to the RS, 31 of the 76 (40.8%) patient eyes were identified as progressors (RS-P), and 45 patient, and all 28 HC, eyes as nonprogressors (RS-NP). The metrics missed (false negative, FN) 15 (48%) (G_ONH) and 9 (29%) (G_mac) of the 31 RS-P. Further, G_ONH and/or G_mac falsely identified (false positive, FP) 10 (22.2%) of 45 patient RS-NP eyes and 7 (25%) of the 28 HC eyes as progressing. Post-hoc analysis identified three reasons (segmentation, centring, and local damage) for these errors.

Conclusions

Global metrics lead to FPs and FNs because of problems inherent in OCT scanning (segmentation and centring), and to FNs because they can miss local damage. These problems are difficult, if not impossible, to correct, and raise concerns about the advisability of using G_ONH and G_mac for detecting progression.

Association Between Structure-function Characteristics and Visual Field Outcomes in Glaucoma Subjects With Intraocular Pressure Reduction After Trabeculectomy

PRECIS

Improvements in post-trabeculectomy visual field (VF) outcomes were found to be significantly associated with preoperative nerve fiber layer thickness parameters extracted from the sectorized structure-function relationship, baseline VF, and severity of glaucoma.

OBJECTIVE

To determine whether the preoperative structure-function relationship helps to predict visual outcomes at 1-year post-trabeculectomy.

PATIENTS AND METHODS

In total, 91 eyes from 87 participants who successfully underwent trabeculectomy were included in our study. All eyes received optical coherence tomography imaging and VF assessment using 30-2 standard automated perimetry preoperatively at baseline and postoperatively 1 year after trabeculectomy. The linear mixed-model analysis was used to assess the association of structure and function at baseline, and multivariate analysis to investigate factors associated with postoperative VF outcomes.

RESULTS

Results from multivariate and univariate analysis for VF 1 year after trabeculectomy showed that a positive preoperative retinal nerve fiber layer thickness deviation from the structure-function model was found to be significantly associated with improved postoperative VF outcomes [β=0.06 dB/μm; 95% confidence interval (CI), 0.03-0.09]. Other significant factors included baseline VF MD (β=-0.18; 95% CI, -0.23 to -0.13) and the presence of severe glaucoma (β=-1.69; 95% CI, -2.80 to -0.57). Intraocular pressure was positively associated with improved VF outcomes only in univariate analysis (β=0.06; 95% CI, 0.01-0.11).

CONCLUSIONS AND RELEVANCE

Characteristics derived from the baseline structure-function relationship were found to be strongly associated with postoperative VF outcomes. These findings suggest that the structure-function relationship could potentially have a role in predicting VF progression after trabeculectomy.

Retinal layer thicknesses retrieved with different segmentation algorithms from optical coherence tomography scans acquired under different signal-to-noise ratio conditions

Glaucomatous damage can be quantified by measuring the thickness of different retinal layers. However, poor image quality may hamper the accuracy of the layer thickness measurement. We determined the effect of poor image quality (low signal-to-noise ratio) on the different layer thicknesses and compared different segmentation algorithms regarding their robustness against this degrading effect. For this purpose, we performed OCT measurements in the macular area of healthy subjects and degraded the image quality by employing neutral density filters. We also analysed OCT scans from glaucoma patients with different disease severity. The algorithms used were: The Canon HS-100's built-in algorithm, DOCTRAP, IOWA, and FWHM, an approach we developed. We showed that the four algorithms used were all susceptible to noise at a varying degree, depending on the retinal layer assessed, and the results between different algorithms were not interchangeable. The algorithms also differed in their ability to differentiate between young healthy eyes and older glaucoma eyes and failed to accurately separate different glaucoma stages from each other.

Analysis of retinal nerve fiber layer birefringence in patients with glaucoma and diabetic retinopathy by polarization sensitive OCT

The retinal nerve fiber layer (RNFL) is a fibrous tissue that shows form birefringence. This optical tissue property is related to the microstructure of the nerve fiber axons that carry electrical signals from the retina to the brain. Ocular diseases that are known to cause neurologic changes, like glaucoma or diabetic retinopathy (DR), might alter the birefringence of the RNFL, which could be used for diagnostic purposes. In this pilot study, we used a state-of-the-art polarization sensitive optical coherence tomography (PS-OCT) system with an integrated retinal tracker to analyze the RNFL birefringence in patients with glaucoma, DR, and in age-matched healthy controls. We recorded 3D PS-OCT raster scans of the optic nerve head area and high-quality averaged circumpapillary PS-OCT scans, from which RNFL thickness, retardation and birefringence were derived. The precision of birefringence measurements was 0.005°/µm. As compared to healthy controls, glaucoma patients showed a slightly reduced birefringence (0.129 vs. 0.135°/µm), although not statistically significant. The DR patients, however, showed a stronger reduction of RNFL birefringence (0.103 vs. 0.135°/µm) which was highly significant. This result might open new avenues into early diagnosis of DR and related neurologic changes.

Comparing the Rule of 5 to Trend-based Analysis for Detecting Glaucoma Progression on OCT

PURPOSE

The rule of 5 is a simple rule for detecting retinal nerve fiber layer (RNFL) change on spectral-domain OCT (SD-OCT), in which a loss of 5 μm of global RNFL on a follow-up test is considered evidence of significant change when compared with the baseline. The rule is based on short-term test-retest variability of SD-OCT and is often used in clinical practice. The purpose of this study was to compare the rule of 5 with trend-based analysis of global RNFL thickness over time for detecting glaucomatous progression.

DESIGN

Prospective cohort.

PARTICIPANTS

A total of 300 eyes of 210 glaucoma subjects followed for an average of 5.4±1.5 years with a median of 11 (interquartile range, 7-14) visits.

METHODS

Trend-based analysis was performed by ordinary least-squares (OLS) linear regression of global RNFL thickness over time. For estimation of specificity, false-positives were obtained by assessing for progression on series of randomly permutated follow-up visits for each eye, which removes any systematic trend over time. The specificity of trend-based analysis was matched to that of the rule of 5 to allow meaningful comparison of the "hit rate," or the proportion of glaucoma eyes categorized as progressing at each time point, using the original sequence of visits.

MAIN OUTCOME MEASURES

Comparison between hit rates of trend-analysis versus rule of 5 at matched specificity.

RESULTS

After 5 years, the simple rule of 5 identified 37.5% of eyes as progressing at a specificity of 81.1%. At the same specificity, the hit rate for trend-based analysis was significantly greater than that of the rule of 5 (62.9% vs. 37.5%; P < 0.001). If the rule of 5 was required to be repeatable on a consecutive test, specificity improved to 93.4%, but hit rate decreased to 21.0%. At this higher specificity, trend-based analysis still had a significantly greater hit rate than the rule of 5 (47.4% vs. 21.0%, respectively; P < 0.001).

CONCLUSIONS

Trend-based analysis was superior to the simple rule of 5 for identifying progression in glaucoma eyes and should be preferred as a method for longitudinal assessment of global SD-OCT RNFL change over time.

Study of correlation between stereopsis and retinal nerve fiber layer thickness in cases of glaucoma

Background

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.

Methods

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.

Results

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).

Conclusion

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.

A 3D Deep Learning System for Detecting Referable Glaucoma Using Full OCT Macular Cube Scans

Purpose

The purpose of this study was to develop a 3D deep learning system from spectral domain optical coherence tomography (SD-OCT) macular cubes to differentiate between referable and nonreferable cases for glaucoma applied to real-world datasets to understand how this would affect the performance.

Methods

There were 2805 Cirrus optical coherence tomography (OCT) macula volumes (Macula protocol 512 × 128) of 1095 eyes from 586 patients at a single site that were used to train a fully 3D convolutional neural network (CNN). Referable glaucoma included true glaucoma, pre-perimetric glaucoma, and high-risk suspects, based on qualitative fundus photographs, visual fields, OCT reports, and clinical examinations, including intraocular pressure (IOP) and treatment history as the binary (two class) ground truth. The curated real-world dataset did not include eyes with retinal disease or nonglaucomatous optic neuropathies. The cubes were first homogenized using layer segmentation with the Orion Software (Voxeleron) to achieve standardization. The algorithm was tested on two separate external validation sets from different glaucoma studies, comprised of Cirrus macular cube scans of 505 and 336 eyes, respectively.

Results

The area under the receiver operating characteristic (AUROC) curve for the development dataset for distinguishing referable glaucoma was 0.88 for our CNN using homogenization, 0.82 without homogenization, and 0.81 for a CNN architecture from the existing literature. For the external validation datasets, which had different glaucoma definitions, the AUCs were 0.78 and 0.95, respectively. The performance of the model across myopia severity distribution has been assessed in the dataset from the United States and was found to have an AUC of 0.85, 0.92, and 0.95 in the severe, moderate, and mild myopia, respectively.

Conclusions

A 3D deep learning algorithm trained on macular OCT volumes without retinal disease to detect referable glaucoma performs better with retinal segmentation preprocessing and performs reasonably well across all levels of myopia.

Translational Relevance

Interpretation of OCT macula volumes based on normative data color distributions is highly influenced by population demographics and characteristics, such as refractive error, as well as the size of the normative database. Referable glaucoma, in this study, was chosen to include cases that should be seen by a specialist. This study is unique because it uses multimodal patient data for the glaucoma definition, and includes all severities of myopia as well as validates the algorithm with international data to understand generalizability potential.

Longitudinal Macular Structure-Function Relationships in Glaucoma

PURPOSE

To investigate the relationship between longitudinal changes in macular thickness measurements from OCT and changes in central visual field (VF) in patients with glaucoma with central or advanced damage at baseline.

DESIGN

Longitudinal cohort study.

PARTICIPANTS

A total of 116 eyes with ≥3 years of follow-up and ≥5 macular OCT images and central 10° VF tests were selected.

METHODS

OCT superpixels and VF locations were matched correcting for retinal ganglion cell (RGC) displacement. Superpixel thickness and VF total deviation (TD) values, in both logarithmic and linear scales, were averaged within 3 eccentricities (3.4°, 5.6°, and 6.8°) and superior and inferior hemiretinas and hemifields. We estimated pointwise TD rates of change and rates of change at superpixels for full macular thickness (FMT), ganglion cell complex (GCC), ganglion cell inner plexiform layer (GCIPL), and ganglion cell layer (GCL). Correlation of structure-function (SF) rates of change was investigated with parametric tests. We compared the proportion of worsening and positive slopes for superpixels and VF test locations (negative vs. positive rates of change with P < 0.05) throughout the follow-up period. Permutation analyses were used to control specificity.

MAIN OUTCOME MEASURES

Magnitude of correlation between structural and functional rates of change and proportion of worsening and positive slopes as a function of follow-up time.

RESULTS

The median (interquartile range) follow-up and number of exams were 4.2 (3.7-4.6) years and 8 (7-9), respectively. The highest correlation of change rates was observed at 3.4° and 5.6° eccentricities (r = 0.24, 0.41, 0.40, and 0.40 for FMT, GCC, GCIPL, and GCL for 3.4° eccentricity and r = 0.28, 0.32, 0.31, and 0.32 for FMT, GCC, GCIPL, and GCL for 5.6° eccentricity, respectively). Although GCC measures demonstrated the highest overall longitudinal SF correlations, the differences were not statistically significant. Significant structural worsening was more frequently detected than functional deterioration at 3- and 5-year time points (P < 0.025). Permutation analyses also confirmed this finding.

CONCLUSIONS

Correlations between central structural and functional rates of change were weak to fair in this cohort. Structural changes were detected more frequently than functional changes. Measurements of both structure and function are required for optimal detection of central progression.

A meta-analysis to study the effect of pan retinal photocoagulation on retinal nerve fiber layer thickness in diabetic retinopathy patients

Background. Diabetic retinopathy is a microvascular disease, it is associated with changes in peripapillary retinal nerve fiber layer thickness, these changes being more pronounced in PDR (Proliferative diabetic retinopathy) patients undergoing laser photocoagulation. Objective. To assess changes in peripapillary retinal nerve fiber layer thickness in proliferative diabetic retinopathy patients using optical coherence tomogram (OCT). Methods. The database search was conducted in June 2018 and continued until October 2018. The search engines used included Pubmed, Medline, OVID and Google Scholar. A meta-analysis of weighted mean difference and standard deviation was conducted. Results. A total of 10 studies containing 377 eyes of PDR patients were selected. The analysis of the included studies revealed no significant effect of PRP on average retinal nerve fiber layer thickness (0.249, 95% CI: -0.985 to 1.483) using OCT. Conclusion. Hence, to conclude, our meta-analysis revealed that there was no significant effect of PRP on RNFL thickness and the impact of PRP could vary. Measurement of peripapillary RNFL thickness may yield erroneous and unpredictable results in this subgroup of patients, further confounding the evaluation of nerve fiber layer damage and its progression.

Risk factors associated with progressive nerve fiber layer thinning in open-angle glaucoma with mean intraocular pressure below 15 mmHg

The purpose of this study was to identify risk factors associated with progressive retinal nerve fiber layer(RNFL) thinning of open-angle glaucoma(OAG) in patients whose intraocular pressure(IOP) was maintained low with medical treatment. Based on a retrospective review of medical records, OAG patients with ≥60 months of follow-up and mean IOP below 15 mmHg were recruited. All eyes underwent IOP measurement with Goldmann applanation tonometer(GAT), standard automated perimetry(SAP), and cirrus optical coherence tomography(cirrus OCT) at 6 month or 1 year intervals. RNFL thinning was assessed using the Guided Progression Analysis(GPA) software. Forty-one eyes of 41 patients (mean age 54.9 ± 13.5) were followed up for 77.8 ± 7.8 months. GPA detected 20 eyes (48.8%) with progressive RNFL thinning(−1.5 ± 0.5 um/year), who were subsequently classified as the ‘rapid progression group.’ Those whose rate of change in RNFL thickness was slower than −1.00 µm/year was classified as the ‘slow progression group’ (n = 21, −0.0 ± 0.4 um/year, P < 0.001). Mean IOP after initiating therapy was 13.2 ± 1.1 mmHg in the rapid progression group and 13.1 ± 1.3 mmHg in the slow progression group (P = 0.300; 14.8 ± 10.0% vs. 19.6 ± 12.4% reduction, P = 0.155). Disc hemorrhage was found to more frequently occur in the rapid progression group (P = 0.001). Multivariate logistic regression analysis showed that patients with disc hemorrhage were at a higher risk for progressive RNFL thinning in OAG (OR 37.529 95% CI 2.915–483.140) after adjusting for baseline co-variates (P = 0.005). In conclusion, disc hemorrhage is associated with progressive RNFL thinning in OAG with well-maintained IOP. Factors other than IOP appear to also play a role in OAG progression.

Serial Combined Wide-Field Optical Coherence Tomography Maps for Detection of Early Glaucomatous Structural Progression

Importance

Both parapapillary and macular areas are important in determining the progression of early glaucoma. However, no attempt has been made to assess the progression of glaucoma in images that combine the 2 areas.

Objective

To evaluate the potential usefulness of serial analysis of combined wide-field optical coherence tomography (OCT) maps for detection of structural progression in patients with early glaucoma.

Design, Setting, and Participants

Retrospective observational study. Patients with early primary open-angle glaucoma with a minimum of 3-year follow-up involving serial spectral-domain OCT measurement were analyzed. Patients were divided into a nonprogressor group (n = 47) and a progressor group (n = 47) on the basis of serial stereo disc photography and red-free photography. Serial combined wide-field OCT maps integrating parapapillary retinal nerve fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GCIPL) maps were generated with the embedded software of serial spectral-domain OCT. Glaucoma specialists then assessed the structural progression detection ability of those serial wide-field OCT maps for early glaucomatous eyes and compared their sensitivity with those of RNFL and GCIPL guided progression analyses (GPAs).

Main Outcomes and Measures

The diagnostic ability of the serial wide-field OCT maps for early glaucomatous structural progression.

Results

Ninety-four patients (mean [SD] age, 51.4 [12.3] years; 48 [51.1%] women; all Korean) were included. The serial wide-field OCT map analysis showed good agreement for detection of structural progression between the 2 glaucoma graders (wide-field OCT thickness map: κ = 0.649; wide-field OCT deviation map: κ = 0.833). These maps showed early glaucomatous structural progression detection abilities comparable with those of RNFL and GCIPL GPAs (sensitivities of wide-field OCT thickness map, wide-field OCT deviation map, RNFL GPA, and GCIPL GPA = 63.8%, 83.0%, 83.0%, and 66.0%, respectively, all P > .05; specificities of wide-field OCT thickness map, wide-field OCT deviation map, RNFL GPA, and GCIPL GPA = 93.6%, 95.7%, 84.8%, and 93.6%, respectively, all P > .05).

Conclusions and Relevance

The serial combined wide-field OCT maps integrating RNFL and GCIPL maps performed well in detecting structural progression in early glaucomatous eyes. Confirmation in an independent prospective study might provide greater confidence in this conclusion.

Fully Convolutional Boundary Regression for Retina OCT Segmentation

A major goal of analyzing retinal optical coherence tomography (OCT) images is retinal layer segmentation. Accurate automated algorithms for segmenting smooth continuous layer surfaces, with correct hierarchy (topology) are desired for monitoring disease progression. State-of-the-art methods use a trained classifier to label each pixel into background, layer, or surface pixels. The final step of extracting the desired smooth surfaces with correct topology are mostly performed by graph methods (e.g. shortest path, graph cut). However, manually building a graph with varying constraints by retinal region and pathology and solving the minimization with specialized algorithms will degrade the flexibility and time efficiency of the whole framework. In this paper, we directly model the distribution of surface positions using a deep network with a fully differentiable soft argmax to obtain smooth, continuous surfaces in a single feed forward operation. A special topology module is used in the deep network both in the training and testing stages to guarantee the surface topology. An extra deep network output branch is also used for predicting lesion and layers in a pixel-wise labeling scheme. The proposed method was evaluated on two publicly available data sets of healthy controls, subjects with multiple sclerosis, and diabetic macular edema; it achieves state-of-the art sub-pixel results.

Deep learning based topology guaranteed surface and MME segmentation of multiple sclerosis subjects from retinal OCT

Optical coherence tomography (OCT) is a noninvasive imaging modality that can be used to obtain depth images of the retina. Patients with multiple sclerosis (MS) have thinning retinal nerve fiber and ganglion cell layers, and approximately 5% of MS patients will develop microcystic macular edema (MME) within the retina. Segmentation of both the retinal layers and MME can provide important information to help monitor MS progression. Graph-based segmentation with machine learning preprocessing is the leading method for retinal layer segmentation, providing accurate surface delineations with the correct topological ordering. However, graph methods are time-consuming and they do not optimally incorporate joint MME segmentation. This paper presents a deep network that extracts continuous, smooth, and topology-guaranteed surfaces and MMEs. The network learns shape priors automatically during training rather than being hard-coded as in graph methods. In this new approach, retinal surfaces and MMEs are segmented together with two cascaded deep networks in a single feed forward propagation. The proposed framework obtains retinal surfaces (separating the layers) with sub-pixel surface accuracy comparable to the best existing graph methods and MMEs with better accuracy than the state-of-the-art method. The full segmentation operation takes only ten seconds for a 3D volume.

Long term effect of panretinal photocoagulation on retinal nerve fiber layer parameters in patients with proliferative diabetic retinopathy

PURPOSE:

This study aimed to evaluate the long-term effect of panretinal photocoagulation (PRP) on the retinal nerve fiber layer (RNFL) in patients with proliferative diabetic retinopathy (PDR).

METHODS:

This was a prospective longitudinal cohort study examining 42 eyes of 42 patients with PDR undergoing PRP. Peripapillary RNFL thickness (RNFLT) was measured using spectral-domain optical coherence tomography at baseline, 1 year, and 3 years following PRP.

RESULTS:

The mean “average RNFLT” was 89.88 ± 14.26 μm at baseline, 85.75 ± 11.36 μm at 1-year follow-up, and 83.33 ± 11.96 μm at 3-year follow-up. There was a statistically significant difference in the average RNFL thickness at baseline and 1 year and 3 years after PRP. At 1-year follow-up, superior, inferior, and nasal RNFL measurements reduced significantly from baseline (P < 0.01). The reduction in RNFL remained statistically significant for superior and inferior quadrants 3 years after PRP.

CONCLUSION:

PRP causes a reduction in RNFL thickness until 3 years after the procedure. Caution should be exercised while interpreting peripapillary RNFL thickness scans in patients who have undergone PRP for diabetic retinopathy.

A feature agnostic approach for glaucoma detection in OCT volumes

Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell with inner plexiform layer (GCIPL) are commonly employed for the diagnosis and monitoring of glaucoma. Previously, machine learning techniques have relied on segmentation-based imaging features such as the peripapillary RNFL thickness and the cup-to-disc ratio. Here, we propose a deep learning technique that classifies eyes as healthy or glaucomatous directly from raw, unsegmented OCT volumes of the optic nerve head (ONH) using a 3D Convolutional Neural Network (CNN). We compared the accuracy of this technique with various feature-based machine learning algorithms and demonstrated the superiority of the proposed deep learning based method. Logistic regression was found to be the best performing classical machine learning technique with an AUC of 0.89. In direct comparison, the deep learning approach achieved a substantially higher AUC of 0.94 with the additional advantage of providing insight into which regions of an OCT volume are important for glaucoma detection. Computing Class Activation Maps (CAM), we found that the CNN identified neuroretinal rim and optic disc cupping as well as the lamina cribrosa (LC) and its surrounding areas as the regions significantly associated with the glaucoma classification. These regions anatomically correspond to the well established and commonly used clinical markers for glaucoma diagnosis such as increased cup volume, cup diameter, and neuroretinal rim thinning at the superior and inferior segments.

Joint retina segmentation and classification for early glaucoma diagnosis

We propose a joint segmentation and classification deep model for early glaucoma diagnosis using retina imaging with optical coherence tomography (OCT). Our motivation roots in the observation that ophthalmologists make the clinical decision by analyzing the retinal nerve fiber layer (RNFL) from OCT images. To simulate this process, we propose a novel deep model that joins the retinal layer segmentation and glaucoma classification. Our model consists of three parts. First, the segmentation network simultaneously predicts both six retinal layers and five boundaries between them. Then, we introduce a post processing algorithm to fuse the two results while enforcing the topology correctness. Finally, the classification network takes the RNFL thickness vector as input and outputs the probability of being glaucoma. In the classification network, we propose a carefully designed module to implement the clinical strategy to diagnose glaucoma. We validate our method both in a collected dataset of 1004 circular OCT B-Scans from 234 subjects and in a public dataset of 110 B-Scans from 10 patients with diabetic macular edema. Experimental results demonstrate that our method achieves superior segmentation performance than other state-of-the-art methods both in our collected dataset and in public dataset with severe retina pathology. For glaucoma classification, our model achieves diagnostic accuracy of 81.4% with AUC of 0.864, which clearly outperforms baseline methods.

Posterior pole asymmetry analysis and retinal nerve fibre layer thickness measurements in primary angle-closure suspect patients

Purpose

To measure peripapillary retinal nerve fiber layer (RNFL) thickness and posterior pole retinal thickness in primary angle-closure suspects (PACS) by Spectral domain optical coherence tomography (SD-OCT) and to be compared with normal subjects.

Methods

Thirty five primary angle-closure suspect patients and thirty normal subjects were enrolled in this study. Peripapillary RNFL and posterior pole retinal thickness by posterior pole asymmetry analysis (PPAA) in SD-OCT were measured.

Results

No significant difference was found in both groups on age, sex distribution, refractive error, intraocular pressure (IOP) and axial length. The PACS group exhibited significantly thinner macular retinal thickness and larger asymmetry on posterior pole region compared with the control group. Yet no significant difference of peripapillary RNFL parameters was found between PACS group and normal control group. A negative correlation was observed between the total retinal thickness on posterior pole region and age when all the PACS participants were analyzed.

Conclusions

Posterior pole retinal thickness measurements obtained by Heidelberg Spectralis SD-OCT using PPAA showed significant thinner change in PACS group than healthy controls. Only age seemed to be an indicator in the occurrence of glaucomatous damage in PACS patients.

Assessing Trends in Functional and Structural Characteristics: A Survey of Statistical Methods With an Example From Ophthalmology

Purpose

Clinical decisions on treatment are usually based on short-term records of consecutive measurements of structure and function. Useful models for analyzing average trends and a description of statistical methods for classifying individual subjects on the basis of subject-specific trend progressions are presented.

Methods

Random effects trend models allow intercepts and slopes of the trend regression to vary across subjects around group-specific mean intercepts and mean slopes. Model results assess whether average intercepts and slopes and subject variability in intercepts and slopes are the same across groups. Fisher's discriminant functions are used for classification.

Results

Methods are presented and illustrated on structural visual data from a multiyear perimetry study. Average thickness of the ganglion cell layer from the optical coherence tomography macula scan and of the retinal nerve fiber layer from the optic disc scan for both glaucoma patients on optimal treatment and normal subjects are analyzed. The random effects trend model shows that average intercepts of glaucoma patients and normal subjects are quite different, but that average slopes are the same, and that the subject variability in both intercepts and slopes is larger for the glaucoma group. These findings explain why the subject-specific trend progression is not a good classifier; it is the level of the measurement (intercept or baseline value) that carries useful information in this particular cohort example.

Translational Relevance

Clinicians base decisions on short-term records of consecutive measurements and need simple statistical tools to analyze the information. This paper discusses useful methods for analyzing short time series data. Model results assess whether there exist significant trends and whether average trends are different across groups. The paper discusses whether clinical measures classify patients reliably into disease groups, given their variability. With ever more available data, classification plays a central role of personalized medicine.

Analysis of peripapillary retinal nerve fiber layer and inner macular layers by spectral-domain optical coherence tomography for detection of early glaucoma

AIM

To analyze the diagnostic capabilities of peripapillary retinal nerve fiber layer (pRNFL) thickness and segmented inner macular layer (IML) thickness measured by spectral-domain optical coherence tomography for detection of early glaucoma.

METHODS

Fifty-three patients with primary open angle glaucoma (POAG), 60 patients with normal tension glaucoma (NTG) and 32 normal control subjects were enrolled. Thicknesses of pRNFL, total macular layers (TML), and the IML, including macular RNFL (mRNFL) and macular ganglion cell layer (mGCL) were assessed. The areas under the receiver operating characteristic curves (AROC) were calculated to compare the diagnostic power of different parameters.

RESULTS

There were no differences in the parameters of pRNFL, TML, and IML between POAG and NTG groups. The thicknesses of superior and inferior mGCL showed significant correlation with mean deviation of visual field (R²=0.071, P=0.004; R²=0.08, P=0.002). The mGCL thickness significantly correlated with the pRNFL thickness in the superior and inferior quadrants (R²=0.156, P<0.001; R²=0.407, P<0.001). The thickness of the inferior-outer sector of macula had greater AROCs than those in the inferior-inner sector of macula. The AROCs for superior (0.894) and inferior (0.879) pRNFL thicknesses were similar with the AROCs for superior (0.839) and inferior mGCL (0.864) thicknesses. Sensitivities at 80% specificity for global pRNFL, inferior-outer mGCL and inferior-outer mRNFL thicknesses were 0.938, 0.867, and 0.725, respectively.

CONCLUSION

The diagnostic capability of the mGCL thickness is comparable to that of the pRNFL thickness in patients with early glaucoma. The inferior-outer sector of IML has a better diagnostic capability than the inferior-inner sector of IML for detection of early glaucoma.

[Structural endpoints for glaucoma studies]

BACKGROUND

Structural endpoints have been discussed as surrogate endpoints for the approval of neuroprotective drugs in glaucoma.

OBJECTIVE

Is the evidence strong enough to establish structural endpoints as surrogate endpoints?

MATERIAL AND METHODS

Review of current understanding between structure and function in glaucoma.

RESULTS

The introduction of optical coherence tomography has revolutionized imaging in glaucoma patients. Clinically either the nerve fiber layer thickness can be measured along a circle centered in the optic nerve head or the ganglion cell layer thickness can be assessed in the macular region, the latter being quantified in combination with other inner retinal layers. On a microscopic level there is a strong correlation between structural and functional loss but this relation can only partially be described with currently available clinical methods. This is particularly true for longitudinal course of the disease in glaucoma patients. Novel imaging techniques that are not yet used clinically may have the potential to increase our understanding between structure and function in glaucoma but further research in this field is required.

CONCLUSION

The current evidence does not allow the establishment of structural endpoints as surrogate endpoints for phase 3 studies in glaucoma. Neuroprotective drugs have to be approved on the basis of visual field data because this is the patient-relevant endpoint. Structural endpoints can, however, play an important role in phase 2 and proof of concept studies.

Intensity inhomogeneity correction of SD-OCT data using macular flatspace

Images of the retina acquired using optical coherence tomography (OCT) often suffer from intensity inhomogeneity problems that degrade both the quality of the images and the performance of automated algorithms utilized to measure structural changes. This intensity variation has many causes, including off-axis acquisition, signal attenuation, multi-frame averaging, and vignetting, making it difficult to correct the data in a fundamental way. This paper presents a method for inhomogeneity correction by acting to reduce the variability of intensities within each layer. In particular, the N3 algorithm, which is popular in neuroimage analysis, is adapted to work for OCT data. N3 works by sharpening the intensity histogram, which reduces the variation of intensities within different classes. To apply it here, the data are first converted to a standardized space called macular flat space (MFS). MFS allows the intensities within each layer to be more easily normalized by removing the natural curvature of the retina. N3 is then run on the MFS data using a modified smoothing model, which improves the efficiency of the original algorithm. We show that our method more accurately corrects gain fields on synthetic OCT data when compared to running N3 on non-flattened data. It also reduces the overall variability of the intensities within each layer, without sacrificing contrast between layers, and improves the performance of registration between OCT images.

Detecting Structural Progression in Glaucoma with Optical Coherence Tomography

Optical coherence tomography (OCT) is increasingly used to obtain objective measurements of the retinal nerve fiber layer (RNFL), optic nerve head, and macula for assessing glaucoma progression. Although OCT has been adopted widely in clinical practice, uncertainty remains concerning its optimal role. Questions include: What is the best structure to measure? What quantity of change is significant? Are structural changes relevant to the patient? How are longitudinal measurements affected by aging? How can changes resulting from aging be differentiated from true progression? How best should OCT be used alongside visual fields, and how often should OCT be performed? Recent studies have addressed some of these questions. Important developments include appreciation of the need to use a consistent point of reference for structural measurements, leading to the introduction of Bruch's membrane opening (BMO)-based measurements, including BMO-minimum rim width and BMO-minimum rim area. Commercially available OCT devices also permit analysis of macular changes over time, for example, changes in the ganglion cell and inner plexiform layers, the sites of the retinal ganglion cell bodies and dendrites, respectively. Several longitudinal studies have compared rates of change in RNFL and macular measurements, with some suggesting that the relative value of each parameter may differ at different stages of disease. In early disease, looking for change over time also may be useful for glaucoma diagnosis, with advantages over classifying eyes using cross-sectional normative databases. Optimal glaucoma management requires information from imaging and visual fields, and efforts have been made to combine information, reducing the noise inherent in both tests to benefit from their different performances according to the stage of disease. Combining information from different structural measurements may also be useful. There is now substantial evidence that progressive structural changes are of direct clinical relevance, with progressive changes on OCT often preceding functional loss and patients with faster change on OCT at increased risk of worsening visual losses. Identification of such patients offers the possibility of commencing or escalating treatment at an earlier stage. This review appraises recent developments in the use of OCT for assessing glaucoma progression.

Towards Topological Correct Segmentation of Macular OCT from Cascaded FCNs

Optical coherence tomography (OCT) is used to produce high resolution depth images of the retina and is now the standard of care for in-vivo ophthalmological assessment. In particular, OCT is used to study the changes in layer thickness across various pathologies. The automated image analysis of these OCT images has primarily been performed with graph based methods. Despite the preeminence of graph based methods, deep learning based approaches have begun to appear within the literature. Unfortunately, they cannot currently guarantee the strict biological tissue order found in human retinas. We propose a cascaded fully convolutional network (FCN) framework to segment eight retina layers and preserve the topological relationships between the layers. The first FCN serves as a segmentation network which takes retina images as input and outputs the segmentation probability maps of the layers. We next perform a topology check on the segmentation and those patches that do not satisfy the topology criterion are passed to a second FCN for topology correction. The FCNs have been trained on Heidelberg Spectralis images and validated on both Heidelberg Spectralis and Zeiss Cirrus images.