A Single Wide-Field OCT Protocol Can Provide Compelling Information for the Diagnosis of Early Glaucoma

Evaluation of the Diagnostic Capability of Spectralis SD-OCT 8 × 8 Posterior Pole Software with the Grid Tilted at 7 Degrees and Horizontalized in Glaucoma

Background: The goal was to evaluate the diagnostic capability of different parameters obtained with the posterior pole (PP) software in Spectralis SD-OCT with the 8 × 8 grid tilted at 7° and horizontalized in glaucomatous eyes. Methods: A total of 299 eyes were included, comprising 136 healthy eyes and 163 with primary open-angle glaucoma (POAG). The following segmentations were evaluated: complete retina, retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), GCL and inner plexiform layer (GCLIPL), ganglion cell complex (GCC), outer plexiform layer and outer nuclear layer (OPLONL), inner retinal layer (IRL), and outer retinal layer (ORL). Different patterns of macular damage were represented using heatmaps for each studied layer, where the areas under the curve (AUROC) values and a retinal thickness cutoff point were defined to discriminate POAG patients. Results: There was not any difference in the diagnostic capability for detecting glaucoma between the grid tilted at 7° and horizontalized. The macular segmentations that offer the highest diagnostic ability in glaucoma discrimination were, in the following order, RNFL (AUROC = 0.796), GCC (AUROC = 0.785), GCL (AUROC = 0.784), GCLIPL (AUROC = 0.770), IRL (AUROC = 0.755), and the complete retina (AUROC = 0.752). In contrast, ORL and OPLONL do not appear to be helpful for discriminating POAG. Conclusions: Some results of PP software may be useful for discriminating POAG.

Glaucoma Detection Using Optical Coherence Tomography: Reviewing the Pitfalls of Comparison to Normative Data

PRCIS

Optical coherence tomography is essential in managing glaucoma. This review describes various artifacts that originate from using a normative database to compare the individual's scans. This is a review paper regarding artifacts in optical coherence tomography imaging for glaucoma arising from using a normative database as a reference for healthy retinal nerve fiber layer and ganglion cell layer.

Changes in Neurodegeneration and Visual Prognosis in Branch Retinal Vein Occlusion after Resolution of Macular Edema

This study aimed to examine the thicknesses of the ganglion cell layer (GCL) and peripapillary retinal nerve fiber layer (RNFL) in eyes with resolved macular edema (ME) in branch retinal vein occlusion (BRVO) and determine their relationship with visual acuity (VA). This retrospective observational case-control study included 57 eyes of BRVO patients with resolved ME after treatment. The macular GCL thickness, peripapillary RNFL thickness, and central macular thickness (CMT) measured on swept-source optical coherence tomography scans with the contralateral eyes used as controls were evaluated. The mean CMT was 270.48 ± 32.7 μm; the mean RNFL thickness was 105.46 ± 25.94 μm in BRVO eyes. Although the average RNFL thickness was decreased in BRVO eyes compared to unaffected eyes, there was no significant difference between the groups. However, the temporal and nasal RNFL thicknesses were significantly different between the groups. The mean affected quadrant had a significantly thinner GCL compared to the corresponding opposite unaffected quadrant (p = 0.02). Final VA was significantly correlated with nasal and middle GCL thicknesses in the affected area (r = -0.512, p = 0.003 and r = -0.537, p = 0.001, respectively); no correlation was found between the average RNFL thickness and mean CMT. The peripapillary RNFL and GCL thicknesses of the affected area were reduced in BRVO eyes compared to unaffected eyes. VA significantly correlated with nasal and middle GCL thicknesses in the affected area. Inner retinal damage occurring in patients with ME secondary to BRVO may be related to the visual prognosis.

Agreement, repeatability, and reproducibility of quantitative retinal layer assessment using swept-source and spectral-domain optical coherence tomography in eyes with retinal diseases

Purpose

To evaluate the agreement and precision of retinal thickness measurements obtained using swept-source optical coherence tomography (SS-OCT) and spectral-domain OCT (SD-OCT) in healthy eyes and eyes with retinopathy.

Methods

This cross-sectional prospective study involved three DRI-OCT Triton (SS-OCT) and three 3D-OCT-1 Maestro (SD-OCT) devices. One of each device (Maestro and Triton) was paired with a single operator. Healthy subjects and patients with retinal diseases were recruited, with study eye and testing order randomized. At least 3 scans per eye were captured for wide scan (12 mm × 9 mm-Triton and Maestro) and macular cube scan (7 mm × 7 mm-Triton, 6 mm × 6 mm-Maestro). Thickness of the full retina, ganglion cell layer + inner plexiform layer (GCL+), and ganglion cell complex (GCL++) were obtained from wide scan and cube scans. Agreement of the measurements between the Triton and Maestro was evaluated by Bland-Altman analysis and Deming regression for each group. Repeatability and reproducibility were assessed using a two-way random effect analysis of variance (ANOVA) model for each parameter by group.

Results

Twenty-five healthy subjects (25 eyes) and 26 patients with retinal diseases (26 eyes), including, but not limited to, age-related macular degeneration, macular hole, and diabetic retinopathy were recruited. Overall, the measurement differences between Triton and Maestro were <6 μm (mean differences of full retina, GCL++, and GCL+ thickness were ≤5.5 μm, 1.3 μm, and 2.8 μm, respectively) and not statistically significant across the parameters. The repeatability and reproducibility estimates indicate high precision in both devices and groups. Across all the parameters, the repeatability limit was ≤7.6 μm for Triton and ≤12.7 μm for Maestro; reproducibility limit was ≤9.2 μm for Triton and ≤14.4 μm for Maestro. In eyes with retinal pathology, the repeatability coefficient of variation (CV)% was ≤2.6% for Triton and ≤3.4% for Maestro; reproducibility CV% was ≤3.3% for Triton and ≤3.5% for Maestro.

Conclusion

Both Triton SS-OCT and Maestro SD-OCT provide reliable measurements of retinal thickness in healthy eyes and eyes with retinal diseases. Excellent agreement between the two devices indicates interoperability when testing healthy eyes or eyes with retinal pathology. These findings support the use of thickness measurements from Triton SS-OCT and Maestro SD-OCT in clinical practice.

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.

Agreement and precision of wide and cube scan measurements between swept-source and spectral-domain OCT in normal and glaucoma eyes

This study aimed to evaluate agreement of Wide scan measurements from swept-source optical coherence tomography (SS-OCT) Triton and spectral-domain OCT (SD-OCT) Maestro in normal/glaucoma eyes, and to assess the precision of measurements from Wide and Cube scans of both devices. Three Triton and three Maestro operator/device configurations were created by pairing three operators, with study eye and testing order randomized. Three scans were captured for Wide (12 mm × 9 mm), Macular Cube (7 mm × 7 mm-Triton; 6 mm × 6 mm-Maestro), and Optic Disc Cube (6 mm × 6 mm) scans for 25 normal eyes and 25 glaucoma eyes. Parameter measurements included circumpapillary retinal nerve fiber layer(cpRNFL), ganglion cell layer + inner plexiform layer (GCL+), and ganglion cell complex (GCL++). A two-way random effect analysis of variance model was used to estimate the repeatability and reproducibility; agreement was evaluated by Bland-Altman analysis and Deming regression. The precision estimates were low, indicating high precision, for all thickness measurements with the majority of the limits < 5 µm for the macula and < 10 µm for the optic disc. Precision of the Wide and Cube scans were comparable. Excellent agreement between the two devices was found for Wide scans, with the mean difference < 3 µm across all measurements (cpRNFL < 3 µm, GCL+  < 2 µm, GCL ++  < 1 µm), indicating interoperability. A single Wide scan covering the peripapillary and macular regions may be useful for glaucoma diagnosis and management.

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.

Agreement and Precision of Wide and Cube Scan Measurements between Swept-source and Spectral-domain OCT in Normal and Glaucoma Eyes

This study aimed to evaluate agreement of Wide scan measurements from swept-source optical coherence tomography(SS-OCT) Triton and spectral-domain OCT(SD-OCT) Maestro in normal/glaucoma eyes, and to assess the precision of measurements from Wide and Cube scans of both devices. Three Triton and three Maestro operator/device configurations were created by pairing three operators, with study eye and testing order randomized. Three scans were captured for Wide (12mm×9mm), Macular Cube (7mmx7mm-Triton; 6mmx6mm-Maestro), and Optic Disc Cube (6mmx6mm) scans for 25 normal eyes and 25 glaucoma eyes. Thickness of circumpapillary retinal nerve fiber layer(cpRNFL), ganglion cell layer+inner plexiform layer(GCL+), and ganglion cell complex(GCL++) was obtained from each scan. A two-way random effect analysis of variance model was used to estimate the repeatability and reproducibility; agreement was evaluated by Bland-Altman analysis and Deming regression. Precision limit estimates were low: <5µm for macular and <10µm for optic disc parameters. Precision for Wide and Cube scans of both devices were comparablein both groups. Excellent agreement between the two devices was found for Wide scans, with the mean difference<3µm across all measurements (cpRNFL<3µm, GCL+<2µm, GCL++<1µm), indicating interoperability. A single Wide scan covering the peripapillary and macular regions may be useful for glaucoma management.

Artificial intelligence in glaucoma: posterior segment optical coherence tomography

PURPOSE OF REVIEW

To summarize the recent literature on deep learning (DL) model applications in glaucoma detection and surveillance using posterior segment optical coherence tomography (OCT) imaging.

RECENT FINDINGS

DL models use OCT derived parameters including retinal nerve fiber layer (RNFL) scans, macular scans, and optic nerve head (ONH) scans, as well as a combination of these parameters, to achieve high diagnostic accuracy in detecting glaucomatous optic neuropathy (GON). Although RNFL segmentation is the most widely used OCT parameter for glaucoma detection by ophthalmologists, newer DL models most commonly use a combination of parameters, which provide a more comprehensive approach. Compared to DL models for diagnosing glaucoma, DL models predicting glaucoma progression are less commonly studied but have also been developed.

SUMMARY

DL models offer time-efficient, objective, and potential options in the management of glaucoma. Although artificial intelligence models have already been commercially accepted as diagnostic tools for other ophthalmic diseases, there is no commercially approved DL tool for the diagnosis of glaucoma, most likely in part due to the lack of a universal definition of glaucoma defined by OCT derived parameters alone (see Supplemental Digital Content 1 for video abstract, http://links.lww.com/COOP/A54 ).

Deep learning approaches to predict 10-2 visual field from wide-field swept-source optical coherence tomography en face images in glaucoma

Close monitoring of central visual field (VF) defects with 10-2 VF helps prevent blindness in glaucoma. We aimed to develop a deep learning model to predict 10-2 VF from wide-field swept-source optical coherence tomography (SS-OCT) images. Macular ganglion cell/inner plexiform layer thickness maps with either wide-field en face images (en face model) or retinal nerve fiber layer thickness maps (RNFLT model) were extracted, combined, and preprocessed. Inception-ResNet-V2 was trained to predict 10-2 VF from combined images. Estimation performance was evaluated using mean absolute error (MAE) between actual and predicted threshold values, and the two models were compared with different input data. The training dataset comprised paired 10-2 VF and SS-OCT images of 3,025 eyes of 1,612 participants and the test dataset of 337 eyes of 186 participants. Global prediction errors (MAE_point-wise) were 3.10 and 3.17 dB for the en face and RNFLT models, respectively. The en face model performed better than the RNFLT model in superonasal and inferonasal sectors (P = 0.011 and P = 0.030). Prediction errors were smaller in the inferior versus superior hemifields for both models. The deep learning model effectively predicted 10-2 VF from wide-field SS-OCT images and might help clinicians efficiently individualize the frequency of 10-2 VF in clinical practice.

The ICD-10 Glaucoma Severity Score Underestimates the Extent of Glaucomatous Optic Nerve Damage

PURPOSE

To evaluate the International Classification of Disease, Tenth Revision (ICD-10) codes used for glaucoma severity classification, which are based on the location of visual field (VF) defects; given the known poor sensitivity of the 24-2 visual field test to early disease and macular damage, we hypothesized that the ICD-10 codes would not accurately reflect the extent of glaucomatous damage.

DESIGN

Retrospective validity and reliability analysis.

METHODS

We evaluated 80 eyes with glaucomatous optic neuropathy (GON). Masked reviewers assigned an ICD-10 severity grade based on 24-2 VF. Two additional masked examiners determined the presence of optical coherence tomography (OCT) structural damage in each hemifield and/or central 5 degrees to define an OCT-based equivalent ICD-10 classification.

RESULTS

A total of 80 eyes with GON were classified as mild, moderate and advanced in 15, 23, and 42 cases, respectively, based on the 24-2 VF, and in 6, 7, and 67 cases, respectively, based on OCT. The OCT classifications were more severe in 29 of 80 cases (36%). In 33 cases (41.3%), macular damage detected by OCT was missed by the 24-2. In 4 of 80 cases (5%), the VF overestimated the severity, likely due to variability of the 24-2 test.

CONCLUSIONS

The ICD-10 system relies solely on damage seen on the 24-2 and as provides a 24-2 functional score rather than a "glaucoma" severity score. OCT revealed wide variation of damage across grades, with a significant proportion of the eyes showing macular structural damage missed with the 24-2 VF. Adding OCT information to the ICD-10 system would help it to more accurately reflect the extent of glaucomatous damage.

Clinicians' Use of Quantitative Information When Assessing the Rate of Structural Progression in Glaucoma

PURPOSE

OCT scans contain large amounts of information, but clinicians often rely on reported layer thicknesses when assessing the rate of glaucomatous progression. We sought to determine which of these quantifications most closely relate to the subjective assessment of glaucoma experts who had all the diagnostic information available.

DESIGN

Prospective cohort study.

PARTICIPANTS

Eleven glaucoma specialists independently scored the rate of structural progression from a series of 5 biannual clinical OCT printouts.

METHODS

A total of 100 glaucoma or glaucoma suspect eyes of 51 participants were included; 20 were scored twice to assess repeatability. Scores ranged from 1 (improvement) to 7 (very rapid progression). Generalized estimating equation linear models were used to predict the mean clinician score from the rates of change of retinal nerve fiber layer thickness (RNFLT) or minimum rim width (MRW) globally or in the most rapidly thinning of the 6 sectors.

MAIN OUTCOME MEASURES

The correlation between the objective rates of change and the average of the 11 clinicians' scores.

RESULTS

Average RNFLT within the series of study eyes was 79.3 μm (range, 41.4-126.6). Some 95% of individual clinician scores varied by ≤ 1 point when repeated. The mean clinician score was more strongly correlated with the rate of change of RNFLT in the most rapidly changing sector in %/year (pseudo-R2 = 0.657) than the rate of global RNFLT (0.372). The rate of MRW in the most rapidly changing sector had pseudo-R2 = 0.149.

CONCLUSIONS

The rate of change of RNFLT in the most rapidly changing sector predicted experts' assessment of the rate of structural progression better than global rates or MRW. Sectoral rates may be a useful addition to current clinical printouts.

Early Glaucoma Detection by Using Style Transfer to Predict Retinal Nerve Fiber Layer Thickness Distribution on the Fundus Photograph

Objective

We aimed to develop a deep learning (DL)–based algorithm for early glaucoma detection based on color fundus photographs that provides information on defects in the retinal nerve fiber layer (RNFL) and its thickness from the mapping and translating relations of spectral domain OCT (SD-OCT) thickness maps.

Design

Developing and evaluating an artificial intelligence detection tool.

Subjects

Pretraining paired data of color fundus photographs and SD-OCT images from 189 healthy participants and 371 patients with early glaucoma were used.

Methods

The variational autoencoder (VAE) network training architecture was used for training, and the correlation between the fundus photographs and RNFL thickness distribution was determined through the deep neural network. The reference standard was defined as a vertical cup-to-disc ratio of ≥0.7, other typical changes in glaucomatous optic neuropathy, and RNFL defects. Convergence indicates that the VAE has learned a distribution that would enable us to produce corresponding synthetic OCT scans.

Main Outcome Measures

Similarly to wide-field OCT scanning, the proposed model can extract the results of RNFL thickness analysis. The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) were used to assess signal strength and the similarity in the structure of the color fundus images converted to an RNFL thickness distribution model. The differences between the model-generated images and original images were quantified.

Results

We developed and validated a novel DL-based algorithm to extract thickness information from the color space of fundus images similarly to that from OCT images and to use this information to regenerate RNFL thickness distribution images. The generated thickness map was sufficient for clinical glaucoma detection, and the generated images were similar to ground truth (PSNR: 19.31 decibels; SSIM: 0.44). The inference results were similar to the OCT-generated original images in terms of the ability to predict RNFL thickness distribution.

Conclusions

The proposed technique may aid clinicians in early glaucoma detection, especially when only color fundus photographs are available.

Test of a Retinal Nerve Fiber Bundle Trajectory Model Using Eyes With Glaucomatous Optic Neuropathy

Purpose

To test a model of retinal nerve fiber bundle trajectories that predicts the arcuate-shaped patterns seen on optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) probability/deviation maps (p-maps) in glaucomatous eyes.

Methods

Thirty-one glaucomatous eyes from a database of 250 eyes had clear arcuate-shaped patterns on RNFL p-maps derived from an OCT cube scan. The borders of the arcuate patterns were extracted from the RNFL p-maps. Next, the trajectories from an arcuate model were compared against these borders via a normalized root-mean-square difference analysis. The model's parameter, β, was varied, and the best-fitting, initial clock-hour position of the trajectory to the border was found for each β. Finally, the regions, as determined by the arcuate border's best-fit, initial clock-hour positions, were compared against the abnormal regions on the circumpapillary retinal nerve fiber layer (cpRNFL) profile.

Results

The arcuate model's mean β_Sup and β_Inf parameters minimized large differences between the trajectories and the arcuate borders on the RNFL p-maps. Furthermore, on average, 68% of the cpRNFL regions defined by the arcuate border's best-fit, initial clock-hour positions were abnormal (i.e., below the ≤5% threshold).

Conclusions

The arcuate model performed well in predicting the borders of arcuate patterns seen on RNFL p-maps. It also predicted the associated abnormal regions of the cpRNFL thickness plots.

Translational Relevance

This model should prove useful in helping clinicians understand topographical comparisons among different OCT representations and should improve structure-structure, as well as structure-function agreement analyses.

Rationale and Development of an OCT-Based Method for Detection of Glaucomatous Optic Neuropathy

A specific, sensitive, and intersubjectively verifiable definition of disease for clinical care and research remains an important unmet need in the field of glaucoma. Using an iterative, consensus-building approach and employing pilot data, an optical coherence tomography (OCT)-based method to aid in the detection of glaucomatous optic neuropathy was sought to address this challenge. To maximize the chance of success, we utilized all available information from the OCT circle and cube scans, applied both quantitative and semiquantitative data analysis methods, and aimed to limit the use of perimetry to cases where it is absolutely necessary. The outcome of this approach was an OCT-based method for the diagnosis of glaucomatous optic neuropathy that did not require the use of perimetry for initial diagnosis. A decision tree was devised for testing and implementation in clinical practice and research that can be used by reading centers, researchers, and clinicians. While initial pilot data were encouraging, future testing and validation will be needed to establish its utility in clinical practice, as well as for research.

Optical coherence tomography in the diagnosis and monitoring of congenital and juvenile glaucoma

Optical coherence tomography (OCT) in everyday routine practice is the method of choice for the instrumental diagnosis of glaucoma in adults. As a non-invasive and safe method of visualizing structural changes in the retina and the optic nerve, the method is of particular value in pediatric practice. At the same time, OCT diagnostics in children is associated with certain difficulties, both during the study and when interpreting the scan results.

This review summarizes the data from the literature and our own research in the diagnosis and monitoring of congenital and juvenile glaucoma from the standpoint of our own long-term clinical experience in using optical coherence tomography. We consider the physiological changes of the retina and optic nerve, attention is focused on the need to create a pediatric regulatory database of retinal thickness, the factors that determine the normal range of the data obtained and allow distinguishing physiological processes from pathological ones are identified. Clinical cases confirming the value of OCT in combined pathology are presented as examples.

The OCT RNFL Probability Map and Artifacts Resembling Glaucomatous Damage

Purpose

The purpose of this study was to improve the diagnostic ability of the optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) probability (p-) map by understanding the frequency and pattern of artifacts seen on the p-maps of healthy control (HC) eyes resembling glaucomatous damage.

Methods

RNFL p-maps were generated from wide-field OCT cube scans of 2 groups of HC eyes, 200 from a commercial normative group (HC-norm) and 54 from a prospective study group, as well as from 62 patient eyes, which included 32 with early glaucoma (EG). These 32 EG eyes had 24-2 mean deviation (MD) better than -6 dB and perimetric glaucoma as defined by 24-2 and 10-2 criteria. For the HC groups, "glaucoma-like" arcuates were defined as any red region near the temporal half of the disc.

Results

Seven percent of the 200 HC-norm and 11% of the 54 HC RNFL p-maps satisfied the definition of "glaucoma-like," as did all the patients' p-maps. The HC p-maps showed two general patterns of abnormal regions, "arcuate" and "temporal quadrant," and these patterns resembled those seen on some of the RNFL p-maps of the EG eyes. A "vertical midline" rule, which required the abnormal region to cross the vertical midline through the fovea, had a specificity of >99%, and a sensitivity of 75% for EG and 93% for moderate to advanced eyes.

Conclusions

Glaucoma-like artifacts on RNFL p-maps are relatively common and can masquerade as arcuate and/or widespread/temporal damage.

Translational Relevance

A vertical midline rule had excellent specificity. However, other OCT information is necessary to obtain high sensitivity, especially in eyes with early glaucoma.

Detecting glaucoma with only OCT: Implications for the clinic, research, screening, and AI development

A method for detecting glaucoma based only on optical coherence tomography (OCT) is of potential value for routine clinical decisions, for inclusion criteria for research studies and trials, for large-scale clinical screening, as well as for the development of artificial intelligence (AI) decision models. Recent work suggests that the OCT probability (p-) maps, also known as deviation maps, can play a key role in an OCT-based method. However, artifacts seen on the p-maps of healthy control eyes can resemble patterns of damage due to glaucoma. We document in section 2 that these glaucoma-like artifacts are relatively common and are probably due to normal anatomical variations in healthy eyes. We also introduce a simple anatomical artifact model based upon known anatomical variations to help distinguish these artifacts from actual glaucomatous damage. In section 3, we apply this model to an OCT-based method for detecting glaucoma that starts with an examination of the retinal nerve fiber layer (RNFL) p-map. While this method requires a judgment by the clinician, sections 4 and 5 describe automated methods that do not. In section 4, the simple model helps explain the relatively poor performance of commonly employed summary statistics, including circumpapillary RNFL thickness. In section 5, the model helps account for the success of an AI deep learning model, which in turn validates our focus on the RNFL p-map. Finally, in section 6 we consider the implications of OCT-based methods for the clinic, research, screening, and the development of AI models.

Precision and Agreement of Individual and Simultaneous Macular and Optic Disc Volumetric Measurements With Spectral Domain Optical Coherence Tomography

Purpose: To evaluate the precision of individual and combined macula and optic disc volumetric analysis, and the agreement between these two scan modes with spectral domain optical coherence tomography (OCT). Methods: Macular and optic disc volumetric measurements were performed with individual and combined scan protocols in one eye of 75 healthy subjects. Three repeated measurements were performed with each protocol. From the macular area, retinal thickness in nine different sectors and ganglion cell complex thickness in eight different sectors were analyzed from both scan modes. From the optic disc area, the peripapillary retinal nerve fiber layer (pRNFL) thickness in 12 clock sectors and the optic disc parameters were evaluated. For all the parameters, repeatability limit and agreement analysis were performed. Results: For the retinal thickness measurements in macula, the combined scan had two to three times larger repeatability limit than the individual scan for all the sectors except the central sector, where the repeatability limit was five times larger. The limits of agreement intervals were lower than 20 μm for all sectors, except the central. The ganglion cell complex measurements also had larger repeatability limits for the combined scans, and the limits of agreement intervals were <10 μm for all sectors. For the pRNFL thickness, the repeatability values were distributed like a vertically elongated ellipse for both scans, but still the repeatability was better for individual scan compared to the combined scan. The shortest and widest interval are obtained for sectors 9 (9 μm) and 12 (40 μm), respectively. The repeatability limit was <0.15 units for all disc parameters with both scan modes. Conclusion: The individual macula and optic disc scans had better repeatability than the combined scan mode, and the two scan modes cannot be used interchangeability due to the wide limits of agreement.

Deep Learning-based Diagnosis of Glaucoma Using Wide-field Optical Coherence Tomography Images

PURPOSE

(1) To evaluate the performance of deep learning (DL) classifier in detecting glaucoma, based on wide-field swept-source optical coherence tomography (SS-OCT) images. (2) To assess the performance of DL-based fusion methods in diagnosing glaucoma using a variety of wide-field SS-OCT images and compare their diagnostic abilities with that of conventional parameter-based methods.

METHODS

Overall, 675 eyes, including 258 healthy eyes and 417 eyes with glaucoma were enrolled in this retrospective observational study. Each single-page wide-field report (12×9 mm) of wide-field SS-OCT imaging provides different types of images that reflect the state of the eyes. A DL-based automated diagnosis system was proposed to detect glaucoma and identify its stage based on such images. We applied the convolutional neural network to each type of image to detect glaucoma. In addition, 2 fusion strategies, fusion by convolution network (FCN) and fusion by fully connected network (FFC) were developed; they differ in terms of the level of fusion of features derived from convolutional neural networks. The diagnostic models were trained using 382 and 293 images in the training and test data sets, respectively. The diagnostic ability of this method was compared with conventional parameters of the thickness of the retinal nerve fiber layer and ganglion cell complex.

RESULTS

FCN achieved an area under the receiver operating characteristic curve (AUC) of 0.987 (95% confidence interval, CI: 0.968-0.996) and an accuracy of 95.22%. In contrast, FFC achieved an AUC of 0.987 (95% CI, 0.971-0.998) and an accuracy of 95.90%. Both FCN and FFC outperformed the conventional method (P<0.001). In detecting early glaucoma, both FCN and FFC achieved significantly higher AUC and accuracy than the conventional approach (P<0.001). In addition, the classification performance of the DL-based fusion methods in identifying the 5 stages of glaucoma is presented via a confusion matrix.

CONCLUSION

DL protocol based on wide-field OCT images outperformed the conventional method in terms of both AUC and accuracy. Therefore, DL-based diagnostic methods using wide-field OCT images are promising in diagnosing glaucoma in clinical practice.

Developing a normative database for retinal perfusion using optical coherence tomography angiography

Visualizing and characterizing microvascular abnormalities with optical coherence tomography angiography (OCTA) has deepened our understanding of ocular diseases, such as glaucoma, diabetic retinopathy, and age-related macular degeneration. Two types of microvascular defects can be detected by OCTA: focal decrease because of localized absence and collapse of retinal capillaries, which is referred to as the non-perfusion area in OCTA, and diffuse perfusion decrease usually detected by comparing with healthy case-control groups. Wider OCTA allows for insights into peripheral retinal vascularity, but the heterogeneous perfusion distribution from the macula, parapapillary area to periphery hurdles the quantitative assessment. A normative database for OCTA could estimate how much individual's data deviate from the normal range, and where the deviations locate. Here, we acquired OCTA images using a swept-source OCT system and a 12×12 mm protocol in healthy subjects. We automatically segmented the large blood vessels with U-Net, corrected for anatomical factors such as the relative position of fovea and disc, and segmented the capillaries by a moving window scheme. A total of 195 eyes were included and divided into 4 age groups: < 30 (n=24) years old, 30-49 (n=28) years old, 50-69 (n=109) years old and >69 (n=34) years old. This provides an age-dependent normative database for characterizing retinal perfusion abnormalities in 12×12 mm OCTA images. The usefulness of the normative database was tested on two pathological groups: one with diabetic retinopathy; the other with glaucoma.

Visual Field Inference From Optical Coherence Tomography Using Deep Learning Algorithms: A Comparison Between Devices

Purpose

To develop a deep learning model to estimate the visual field (VF) from spectral-domain optical coherence tomography (SD-OCT) and swept-source OCT (SS-OCT) and to compare the performance between them.

Methods

Two deep learning models based on Inception-ResNet-v2 were trained to estimate 24-2 VF from SS-OCT and SD-OCT images. The estimation performance of the two models was evaluated by using the root mean square error between the actual and estimated VF. The performance was also compared among different glaucoma severities, Garway-Heath sectorizations, and central/peripheral regions.

Results

The training dataset comprised images of 4391 eyes from 2350 subjects, and the test dataset was obtained from another 243 subjects (243 eyes). In all subjects, the global estimation errors were 5.29 ± 2.68 dB (SD-OCT) and 4.51 ± 2.54 dB (SS-OCT), and the estimation error of SS-OCT was significantly lower than that of SD-OCT (P < 0.001). In the analysis of sectors, SS-OCT showed better performance in all sectors except for the inferonasal sector in normal vision and early glaucoma. In advanced glaucoma, the estimation error of the central region was worsened in both OCTs, but SS-OCT was still significantly better in the peripheral region.

Conclusions

Our deep learning model estimated the VF 24-2 better with a wide field image of SS-OCT than did with retinal nerve fiber layer and ganglion cell–inner plexiform layer images of SD-OCT.

Translational Relevance

This deep learning method can help clinicians to determine the VF from OCT images. OCT manufacturers can equip this system to provide additional VF data.

A Simple Subjective Evaluation of Enface OCT Reflectance Images Distinguishes Glaucoma From Healthy Eyes

Purpose

We present a subjective approach to detecting glaucomatous defects in enface images and assess its diagnostic performance. We also test the hypothesis that if reflectivity changes precede thickness changes in glaucoma there should be reduced correlation between the modalities in glaucoma compared to controls.

Methods

Twenty glaucoma participants and 20 age-matched controls underwent high-resolution OCT scans of one eye. 4 µm-thick enface slabs were constructed through the retina. Enface indices were depths of first gap in visible retinal nerve fiber bundles (RNFBs) and last visible bundle, subjectively evaluated in six sectors of a 3.5 mm circle around the optic disc. Retinal nerve fiber layer thickness (RNFLT) along the same circle was extracted at angles corresponding to enface indices. Between-group differences were tested by linear mixed models. Diagnostic performance was measured by partial receiver operating characteristic area (pAUC).

Results

First gap and last visible bundle were closer to the inner limiting membrane in glaucoma eyes (both P < 0.0001). Enface indices showed excellent diagnostic performance (pAUCs 0.63-1.00), similar to RNFLT (pAUCs 0.63-0.95). Correlation between enface and RNFLT parameters was strong in healthy (r = 0.81-0.92) and glaucoma eyes (r = 0.73-0.80).

Conclusions

This simple subjective method reliably identifies glaucomatous defects in enface images with diagnostic performance at least as good as existing thickness indices. Thickness and reflectivity were similarly related in healthy and glaucoma eyes, providing no strong evidence of reflectivity loss preceding thinning. Objective analyses may realize further potential of enface OCT images in glaucoma.

Translational Relevance

Novel enface OCT indices may aid glaucoma diagnosis.

Did the OCT Show Progression Since the Last Visit?

Identifying progression is of fundamental importance to the management of glaucoma. It is also a challenge. The most sophisticated, and probably the most useful, commercially available clinical tool for identifying progression is the Guided Progression Analysis (GPA), which was initially developed to identify progression using 24-2 visual field tests. More recently, it has been extended to retinal nerve fiber layer (RNFL) and ganglion cell+inner plexiform layer thicknesses measured with optical coherence tomography (OCT). However, the OCT GPA requires a minimum of 3 tests to determine "possible loss (progression)" and a minimum of 4 tests to determine if the patient shows "likely loss (progression)." Thus, it is not designed to answer a fundamental question asked by both the clinician and the patient, namely: Did damage progress since the last visit? Some clinicians use changes in summary statistics, such as global/average circumpapillary RNFL thickness. However, these statistics have poor sensitivity and specificity due to segmentation and alignment errors. Instead of relying on the GPA analysis or summary statistics, one needs to evaluate RNFL and ganglion cell+inner plexiform layer probability maps and circumpapillary OCT B-scan images. In addition, we argue that the clinician can make a better decision about suspected progression between 2 test days by topographically comparing the changes in the different OCT maps and images, in addition to topographically comparing the changes in the visual field with the changes in OCT probability maps.

Strategies to Improve Convolutional Neural Network Generalizability and Reference Standards for Glaucoma Detection From OCT Scans

Purpose

To develop and evaluate methods to improve the generalizability of convolutional neural networks (CNNs) trained to detect glaucoma from optical coherence tomography retinal nerve fiber layer probability maps, as well as optical coherence tomography circumpapillary disc (circle) b-scans, and to explore impact of reference standard (RS) on CNN accuracy.

Methods

CNNs previously optimized for glaucoma detection from retinal nerve fiber layer probability maps, and newly developed CNNs adapted for glaucoma detection from optical coherence tomography b-scans, were evaluated on an unseen dataset (i.e., data collected at a different site). Multiple techniques were used to enhance CNN generalizability, including augmenting the training dataset, using multimodal input, and training with confidently rated images. Model performance was evaluated with different RS.

Results

Training with data augmentation and training on confident images enhanced the accuracy of the CNNs for glaucoma detection on a new dataset by 5% to 9%. CNN performance was optimal when a similar RS was used to establish labels both for the training and the testing sets. However, interestingly, the CNNs described here were robust to variation in the RS.

Conclusions

CNN generalizability can be improved with data augmentation, multiple input image modalities, and training on images with confident ratings. CNNs trained and tested with the same RS achieved best accuracy, suggesting that choosing a thorough and consistent RS for training and testing improves generalization to new datasets.

Translational Relevance

Strategies for enhancing CNN generalizability and for choosing optimal RS should be standard practice for CNNs before their deployment for glaucoma detection.

An Evaluation of a New 24-2 Metric for Detecting Early Central Glaucomatous Damage

PURPOSE

We sought to test the hypothesis that a recently proposed pattern standard deviation (PSD) metric, based upon the 24-2 visual field (VF) test, as well as the PSD of the 10-2 VF, will miss central glaucomatous damage confirmed with an objective structure-function method.

DESIGN

Cross-sectional study.

METHODS

A glaucoma (G) group (70 eyes/patients) diagnosed with glaucoma and a 24-2 mean deviation better than -6 dB and a healthy (H) group (45 eyes/patients) had 24-2 and 10-2 VFs and optical coherence tomography (OCT) scans twice within 4 weeks. The PSD(C24-2), based upon the central 12 points of the 24-2, was compared with the PSD(10-2). To evaluate central damage (CD) in G eyes with normal PSD(C24-2) values, a post hoc analysis was combined with a CD reference standard (CD-RS), which was based upon an objective, topographic comparison between abnormal points on the 10-2 VF and OCT probability maps.

RESULTS

The 115 PSD(C24-2) and PSD(10-2) values were significantly correlated (Spearman correclation coefficient: rho = 0.55; P < .001) and the number of G eyes (19) identified as abnormal by the PSD(C24-2) was not significantly different from the number (22) identified by the PSD(10-2) (P = .15). However, based upon the CD-RS, 44 of 70 G eyes were classified as abnormal. The PSD(C24-2) missed 27 (61%) of these 44 eyes, and the PSD(10-2) missed 23 (52%) of these eyes. Post hoc analysis revealed clear CD in most of these eyes.

CONCLUSION

Neither the PSD(C24-2) nor the PSD(10-2) metric is good measure of early CD. Instead we recommend a topographic approach based upon OCT probability maps and a 10-2 VF.

Optical Coherence Tomography Can Be Used to Assess Glaucomatous Optic Nerve Damage in Most Eyes With High Myopia

PRECIS

It is generally assumed that optical coherence tomography (OCT) cannot be used to diagnose glaucomatous optic neuropathy (GON) in high myopes. However, this study presents evidence that there is sufficient information in OCT scans to allow for accurate diagnosis of GON in most eyes with high myopia.

PURPOSE

The purpose of this study was to test the hypothesis that glaucomatous damage can be accurately diagnosed in most high myopes via an assessment of the OCT results.

PATIENTS AND METHODS

One hundred eyes from 60 glaucoma patients or suspects, referred for OCT scans and evaluation, had corrected spherical refractive errors worse than -6 D and/or axial lengths ≥26.5 mm. An OCT specialist judged whether the eye had GON, based upon OCT circle scans of the disc and cube scans centered on the macula. A glaucoma specialist made the same judgement using all available information (eg, family history, repeat visits, intraocular pressure, 10-2 and 24-2 visual fields, OCT). A reference standard was created based upon the glaucoma specialist's classifications. In addition, the glaucoma specialist judged whether the eyes had peripapillary atrophy (PPA), epiretinal membrane (ERM), tilted disc (TD), and/or a paravascular inner retinal defect (PIRD).

RESULTS

The OCT specialist correctly identified 97 of the 100 eyes using the OCT information. In 63% of the cases, the inner circle scan alone was sufficient. For the rest, additional scans were requested. In addition, 81% of the total eyes had: PPA (79%), ERM (18%), PIRD (26%), and/or TD (48%).

CONCLUSIONS

For most eyes with high myopia, there is sufficient information in OCT scans to allow for accurate diagnosis of GON. However, the optimal use of the OCT will depend upon training to read OCT scans, which includes taking into consideration myopia related OCT artifacts and segmentation errors, as well as PPA, ERM, PIRD, and TD.

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.

Macular imaging with optical coherence tomography in glaucoma

With the advent of spectral-domain optical coherence tomography, imaging of the posterior segment of the eye can be carried out rapidly at multiple anatomical locations, including the optic nerve head, circumpapillary retinal nerve fiber layer, and macula. There is now ample evidence to support the role of spectral-domain optical coherence tomography imaging of the macula for detection of early glaucoma. Macular spectral-domain optical coherence tomography measurements demonstrate high reproducibility, and evidence on its utility for detection of glaucoma progression is accumulating. We present a comprehensive review of macular spectral-domain optical coherence tomography imaging emerging as an essential diagnostic tool in glaucoma.

Reasons why OCT Global Circumpapillary Retinal Nerve Fiber Layer Thickness is a Poor Measure of Glaucomatous Progression

Purpose

To assess the effects of local defects, segmentation errors, and improper image alignment on the performance of the commonly used optical coherence tomography (OCT) measure of progression, that is the change in global (average) circumpapillary retinal nerve fiber layer (cpRNFL) thickness (ΔG).

Methods

One hundred fifty eyes suspected of, or with, early glaucoma had OCT circle and cube scans obtained using eye tracking on two occasions at least 1 year apart. Statistical progression was defined by fixed values of ΔG (3-8 um) and quantile regression. For a reference standard, four authors identified 30 eyes as "likely progressed," and 61 eyes that "likely had not progressed" based on OCT reports from both baseline and follow-up tests.

Results

A ΔG criterion of 4 um had the best accuracy: 77%, with 5 false positive (8.2%) and 16 false negative (53%). A post hoc analysis of circular b-scans and OCT probability maps of these eyes indicated that segmentation errors and local progression accounted for most of these mistakes. Segmentation errors, although less common, were also present in true positives and true negatives.

Conclusions

Local defects and segmentation errors are the primary reasons for the poor performance of cpRNFL thickness G metric. Because these problems are difficult, if not impossible, to eliminate, the G metric should not be relied on in isolation for detecting glaucomatous progression.

Translational Relevance

Local defects and segmentation errors are easily identified by viewing OCT circumpapillary images, which should be part of the standard protocol for detecting glaucomatous progression.

Diagnostic Accuracy of Wide-Field Map from Swept-Source Optical Coherence Tomography for Primary Open-Angle Glaucoma in Myopic Eyes

PURPOSE

To compare the accuracy of glaucomatous defects and diagnostic power for primary open-angle glaucoma (POAG) between swept-source optical coherence tomography (SS-OCT) and spectral-domain OCT (SD-OCT) in myopic eyes.

DESIGN

Prospective, case-control study.

METHODS

A total of 150 myopic POAG eyes and 100 healthy myopic eyes underwent SD-OCT and SS-OCT in random order on the same day. The locations of glaucomatous defects on SD-OCT thickness and deviation maps and SS-OCT wide-field thickness (thickness surfaces) and SuperPixel maps were rated, and the maps' accuracies were compared. The area under receiver operating characteristic (AUROC) of the peripapillary retinal nerve fiber layer (RNFL) and the macular parameters (GCL++: equivalent to ganglion cell-inner plexiform layer [GCIPL] + RNFL; GCL+: equivalent to GCIPL) from each of the devices for myopic POAG were calculated and compared.

RESULTS

The wide-field RNFL thickness (thickness surface) map showed the best accuracy for glaucomatous defects in the inferotemporal (96.4%) and superotemporal (92.4%) regions. The RNFL/GCL++/GCL+ wide-field thickness (thickness surface) map showed better accuracy for glaucomatous defects in both the superotemporal and inferotemporal regions than with the SD-OCT thickness map (all P values <.05). The average GCL++ (87.6%) and GCL+ (87.5%) thicknesses showed significantly greater AUROC for myopic POAG than GCIPL thickness from SD-OCT (83.8%, all P values <.05).

CONCLUSIONS

In myopic eyes, the SS-OCT wide-field map exhibited better accuracy for glaucomatous defect and greater diagnostic power for POAG than SD-OCT outcomes. This result might have been due to SS-OCT's wider scan and measurement area.

Prediction of visual field from swept-source optical coherence tomography using deep learning algorithms

PURPOSE

To develop a deep learning method to predict visual field (VF) from wide-angle swept-source optical coherence tomography (SS-OCT) and compare the performance of three Google Inception architectures.

METHODS

Three deep learning models (with Inception-ResNet-v2, Inception-v3, and Inception-v4) were trained to predict 24-2 VF from the macular ganglion cell-inner plexiform layer and the peripapillary retinal nerve fibre layer map obtained by SS-OCT. The prediction performance of the three models was evaluated by using the root mean square error (RMSE) between the actual and predicted VF. The performance was also compared among different glaucoma severities and Garway-Heath sectorizations.

RESULTS

The training dataset comprised images of 2220 eyes from 1120 subjects, and the test dataset was obtained from another 305 subjects (305 eyes). In all subjects, the global prediction errors (RMSEs) were 4.44 ± 2.09 dB, 4.78 ± 2.38 dB, and 4.85 ± 2.66 dB for the Inception-ResNet-v2, Inception-v3, and Inception-v4 architectures, respectively, and the prediction error of Inception-ResNet-v2 was significantly lower than the other two (P < 0.001). As glaucoma progressed, the prediction error of all three architectures significantly worsened to 6.59 dB, 7.33 dB, and 7.79 dB, respectively. In the analysis of sectors, the nasal sector had the lowest prediction error, followed by the superotemporal sector.

CONCLUSIONS

Inception-ResNet-v2 achieved the best performance, and the global prediction error (RMSE) was 4.44 dB. As glaucoma progressed, the prediction error became larger. This method may help clinicians determine VF, particularly for patients who are unable to undergo a physical VF test.

Improving the Detection of Glaucoma and Its Progression: A Topographical Approach

Glaucoma is typically defined as a progressive optic neuropathy characterized by a specific (arcuate) pattern of visual field (VF) and anatomic changes. Therefore, we should be comparing arcuate patterns of damage seen on VFs with those seen on optical coherence tomography (OCT) maps. Instead, clinicians often use summary metrics such as VF pattern standard deviation, OCT retinal nerve fiber (RNF) global thickness, etc. There are 2 major impediments to topographically comparing patterns of damage on VF and OCT maps. First, until recently, it was not easy to make these comparisons with commercial reports. While recent reports do make it easier to compare VF and OCT maps, they have shortcomings. In particular, the 24-2 VF covers a larger retinal region than the commercial OCT scans, and, further, it is not easy to understand the topographical relationship among the different maps/plots within the current OCT reports. Here we show how a model of RNF bundles can overcome these problems. The second major impediment is the lack of a quantitative, and automated, method for comparing patterns of damage seen on VF and OCT maps. However, it is now possible to objectively and automatically quantify this agreement. Together, the RNF bundle model and the automated structure-function method should improve the power of topographical methods for detecting glaucoma and its progression. This should prove useful in clinical studies and trials, as well as for training and validating artificial intelligence/deep learning approaches for these purposes.

An Automated Method for Assessing Topographical Structure-Function Agreement in Abnormal Glaucomatous Regions

Purpose

To develop an automated/objective method for topographically comparing abnormal regions on optical coherence tomography (OCT) and visual field (VF) tests of eyes with early glaucoma.

Methods

A custom R program was developed that allows for both visualization and automatic assessment of the topographical agreement between functional (24-2 and/or 10-2 VF) and structural (widefield OCT retinal nerve fiber layer and/or retinal ganglion cell layer) deviation/probability maps. It was optimized using information from 98 eyes: 53 diagnosed as "definitely glaucoma" (DG) and 45 recruited as healthy (H) controls. Different pairs of abnormal VF (P <1%, <2%, <5%) and abnormal OCT (P <5%, <10%, <15%) criteria were evaluated. The percentages of abnormal structure-abnormal function (aS-aF) agreement found in DG eyes and nonagreement found in H eyes were used to define the optimal criteria and number of aS-aF locations for the detection of aS-aF agreement.

Results

A criterion of two aS-aF locations with "OCT <10% and VF <5%" on VF pattern deviation (PD) probability and OCT deviation/probability maps yielded high overall agreement (92%) with high aS-aF agreement for the DG eyes (89%) and high aS-aF nonagreement for the H eyes (95%). Total deviation probability maps achieved slightly lower performance than PD maps.

Conclusions

The method described here can automatically and objectively evaluate aS-aF agreement with a direct comparison of abnormal regions of function and structure.

Translational Relevance

As glaucoma diagnosis often involves assessing structure-function agreement, this technique can overcome subjectivity in this assessment.

Structure-Function Agreement Is Better Than Commonly Thought in Eyes With Early Glaucoma

Purpose

To assess the agreement between structural (optical coherence tomography [OCT]) and functional (visual field [VF]) glaucomatous damage with an automated method and deviation/probability maps, and to compare this method to a metric method.

Methods

Wide-field spectral-domain OCT scans, including the disc and macula, and 24-2 and 10-2 VFs were obtained from 45 healthy control (H) eyes/individuals, and 53 eyes/patients with 24-2 mean deviation (MD) better than -6 dB diagnosed as "definite glaucoma" (DG) by experts. Abnormal structure-abnormal function (aS-aF) agreement was assessed with an automated topographic (T) method based upon VF pattern deviation and OCT probability maps. Results were compared to a metric (M) method optimized for accuracy, (abnormal 24-2 glaucoma hemifield test [GHT] or pattern standard deviation [PSD], or 10-2 PSD AND abnormal OCT [quadrant]).

Results

For the T-method, 47 (88.7%) of the 53 DG eyes showed aS-aF agreement, compared to 2 (4.5%) of the 45 H eyes. The aS-aF agreement for these two H eyes was easily identified as mistaken, and did not replicate on a subsequent test. Without the 10-2, the aS-aF agreement decreased from 47 to 34 (64.2%) of 53 DG eyes. For the M-method, 37 (69.8%) of the 53 DG eyes showed aS-aF agreement, while omitting the 10-2 VF resulted in agreement in only 33 (62.3%) eyes.

Conclusions

There is good agreement between structural and functional damage, even in eyes with confirmed early glaucomatous damage, if both 24-2 and 10-2 VFs are obtained, and abnormal locations on the VFs are compared to abnormal regions seen on OCT macular and disc scans. This can be done in an objective, automated fashion. (ClinicalTrials.gov number, NCT02547740.).

Association of Macular Visual Field Measurements With Glaucoma Staging Systems

Importance

Macular function is important for daily activities but is underestimated when tested with 24-2 visual fields, which are often used to classify glaucoma severity.

Objective

To test the hypothesis that current glaucoma staging systems underestimate glaucoma severity by not detecting macular damage.

Design, Setting, and Participants

This cross-sectional study was carried out in a glaucoma referral practice. The eyes of participants with manifest glaucoma and 24-2 mean deviation (MD) better than -6 dB were included. All participants were tested with 24-2, 10-2 visual fields, and spectral-domain optical coherence tomography of the optic disc and macula.

Exposures

Macular damage was based on the topographic agreement between visual field results and retinal ganglion cell plus inner plexiform layer probability plots. Classifications from the Hodapp-Parrish-Anderson (HPA), visual field index (VFI), and Brusini staging systems were examined and compared with visual field and spectral-domain optical coherence tomography results.

Main Outcomes and Measures

The association between the presence of macular damage and glaucoma severity scores.

Results

Fifty-seven eyes of 57 participants were included; 33 participants (57%) were women, and 43 (75%) were white. Their mean (SD) age was 57 (14) years. Forty-eight of the eyes (84% [95% CI, 72%-92%]) had macular damage by the study definition. These had a 24-2 MD mean (SD) of -2.5 (1.8); corresponding results for the 10-2 MD were -3.0 (2.4) dB and for the VFI were 94.2% (4.5%). The HPA system classified 70% (95% CI, 55%-83%) of eyes with macular damage as having early defects; the VFI system classified 81% (95% CI, 67%-91%) of eyes with macular damage as having early defects, and the Brusini system 68% (95% CI, 53%-81%).

Conclusions and Relevance

These findings suggest that current glaucoma staging systems based on 24-2 (or 30-2) visual fields underestimate disease severity and the presence of macular damage. If these results are confirmed and generalizable to other participants, new systems using macular measures (from 10-2 and spectral-domain optical coherence tomography results) might improve staging of glaucoma severity.

Four Questions for Every Clinician Diagnosing and Monitoring Glaucoma

We pose 4 questions for the clinician diagnosing and monitoring glaucoma, and supply evidence-based answers. The first question is: "When do you perform a 10-2 (2-degree grid) visual field (VF) test?" We argue the best answer is: anyone you would do, or have done, a 24-2 (6-degree grid) VF on should have both a 24-2 and a 10-2 VF within the first 2 visits. Second, "When do you perform an optical coherence tomography (OCT) scan of the macula?" We argue that, if you are performing an OCT test, then it should include both the macula and disc, either as a single scan or as 2 scans, one centered on the macula and the other on the disc. Third, "How do you know if the VF and OCT tests agree?" The poor answer is, "I use summary statistics such as 24-2 mean deviation and global or quadrant average of retinal nerve fiber layer (RNFL) thickness." It is much better to topographically compare abnormal regions on the OCT to abnormal regions on the VF. Finally, the fourth question is: "When do you look at OCT images?" We argue that, at a minimum, the clinician should be directly examining an image of the circumpapillary RNFL, and this image should be sufficiently large and with sufficient resolution so that local damage can be seen, and the segmentation evaluated.

Within-subject variability in human retinal nerve fiber bundle width

With the growing availability of high-resolution imaging there has been increased interest in developing new metrics for integrity of the retinal nerve fiber layer. In particular, it has been suggested that measurement of width of retinal nerve fiber bundles (RNFBs) may be useful in glaucoma, due to low between-subject variability in mean RNFB width. However, there have also been reports of substantial within-subject variability in the width of individual RNFBs. To assess within-subject variability as a potential source of selection bias in measurements of RNFB width, we used an adaptive optics scanning laser ophthalmoscope (AOSLO) to measure widths of individual RNFBs in one eye each of 11 young adults in good ocular health. In a pilot study we analyzed a large AOSLO image of RNFL in one participant then, based on those findings, in the main study we used AOSLO to image a smaller region in 10 additional healthy young adults. The pilot study of one eye found RNFB widths ranging from 10 μm to 44 μm. This suggested that biological variability was too high for measuring small changes arising from disease processes. This was confirmed in measurements of 10 eyes in the main study, RNFB widths ranged from 9 μm to 55 μm and every eye had large within-subject variability (exceeding 19 μm in all eyes) in RNFB width for nearby bundles. The within-subject variability in RNFB width, as well as variation in the width of single RNFBs over relatively short distances (<300 um) depending on the precise location of measurement, suggests that bundle width measurements would be highly susceptible to selection bias and therefore of limited clinical use.

Glaucoma detection using image processing techniques: A literature review

The term glaucoma refers to a group of heterogeneous diseases that cause the degeneration of retinal ganglion cells (RGCs). The degeneration of RGCs leads to two main issues: (i) structural changes to the optic nerve head as well as the nerve fiber layer, and (ii) simultaneous functional failure of the visual field. These two effects of glaucoma may lead to peripheral vision loss and, if the condition is left to progress it may eventually lead to blindness. No cure for glaucoma exists apart from early detection and treatment by optometrists and ophthalmologists. The degeneration of RGCs is normally detected from retinal images which are assessed by an expert. These retinal images also provide other vital information about the health of an eye. Thus, it is essential to develop automated techniques for extracting this information. The rapid development of digital images and computer vision techniques have increased the potential for analysis of eye health from images. This paper surveys current approaches to detect glaucoma from 2D and 3D images; both the limitations and possible future directions are highlighted. This study also describes the datasets used for retinal analysis along with existing evaluation algorithms. The main topics covered by this study may be enumerated as follows.

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.

Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps

PURPOSE

To develop and evaluate a deep learning system for differentiating between eyes with and without glaucomatous visual field damage (GVFD) and predicting the severity of GFVD from spectral domain OCT (SD OCT) optic nerve head images.

DESIGN

Evaluation of a diagnostic technology.

PARTICIPANTS

A total of 9765 visual field (VF) SD OCT pairs collected from 1194 participants with and without GVFD (1909 eyes).

METHODS

Deep learning models were trained to use SD OCT retinal nerve fiber layer (RNFL) thickness maps, RNFL en face images, and confocal scanning laser ophthalmoscopy (CSLO) images to identify eyes with GVFD and predict quantitative VF mean deviation (MD), pattern standard deviation (PSD), and mean VF sectoral pattern deviation (PD) from SD OCT data.

MAIN OUTCOME MEASURES

Deep learning models were compared with mean RNFL thickness for identifying GVFD using area under the curve (AUC), sensitivity, and specificity. For predicting MD, PSD, and mean sectoral PD, models were evaluated using R² and mean absolute error (MAE).

RESULTS

In the independent test dataset, the deep learning models based on RNFL en face images achieved an AUC of 0.88 for identifying eyes with GVFD and 0.82 for detecting mild GVFD significantly (P < 0.001) better than using mean RNFL thickness measurements (AUC = 0.82 and 0.73, respectively). Deep learning models outperformed standard RNFL thickness measurements in predicting all quantitative VF metrics. In predicting MD, deep learning models based on RNFL en face images achieved an R² of 0.70 and MAE of 2.5 decibels (dB) compared with 0.45 and 3.7 dB for RNFL thickness measurements. In predicting mean VF sectoral PD, deep learning models achieved high accuracy in the inferior nasal (R² = 0.60) and superior nasal (R² = 0.67) sectors, moderate accuracy in inferior (R² = 0.26) and superior (R² = 0.35) sectors, and lower accuracy in the central (R² = 0.15) and temporal (R² = 0.12) sectors.

CONCLUSIONS

Deep learning models had high accuracy in identifying eyes with GFVD and predicting the severity of functional loss from SD OCT images. Accurately predicting the severity of GFVD from SD OCT imaging can help clinicians more effectively individualize the frequency of VF testing to the individual patient.

Severity of Visual Field Loss at First Presentation to Glaucoma Clinics in England and Tanzania

Purpose: To compare severity of visual field (VF) loss at first presentation in glaucoma clinics in England and Tanzania.Methods: Large archives of VF records from automated perimetry were used to retrospectively examine vision loss at first presentation in glaucoma clinics in Tanzania (N = 1,502) and England (N = 9,264). Mean deviation (MD) of the worse eye at the first hospital visit was used as an estimate of detectable VF loss severity.Results: In Tanzania, 44.7% {CI_95%: 42.2, 47.2} of patients presented with severe VF loss (< -20 dB), versus 4.6% {4.1, 5.0} in England. If we consider late presentation to also include cases of advanced loss (-12.01 dB to -20 dB), then the proportion of patients presenting late was 58.1% {55.6, 60.6} and 14.0% {13.3, 14.7}, respectively. The proportion of late presentations was greater in Tanzania at all ages, but the difference was particularly pronounced among working-age adults, with 50.3% {46.9, 53.7} of 18-65-year-olds presenting with advanced or severe VF loss, versus 10.2% {9.3, 11.3} in England. In both countries, men were more likely to present late than women.Conclusions: Late presentation of glaucoma is a problem in England, and an even greater challenge in Tanzania. Possible solutions are discussed, including increased community eye-care, and a more proactive approach to case finding through the use of disruptive new technologies, such as low-cost, portable diagnostic aids.

An Examination of the Frequency of Paravascular Defects and Epiretinal Membranes in Eyes With Early Glaucoma Using En-face Slab OCT Images

PURPOSE

To examine the frequency of paravascular defects (PDs) and macular epiretinal membranes (ERMs) in eyes categorized as having mild glaucoma or glaucoma suspect using en-face slab analysis of optical coherence tomography (OCT) scans.

MATERIALS AND METHODS

Fifty-seven glaucomatous eyes, 44 low-risk suspect eyes, and 101 healthy control eyes were included in the study. The 101 glaucomatous and suspect eyes had a mean deviation better than -6 dB on the 24-2 visual field, and a spherical refractive error between±6 D or axial length <26.5 mm. Two OCT-graders masked to eye classification identified ERMs and PDs on en-face slab images of the macula and peripapillary retina using horizontal B-scans and derived vertical B-scans.

RESULTS

Glaucomatous eyes had a significantly higher number of PDs and ERMs than healthy controls (PD, P<0.001; ERM, P=0.046) and low-risk glaucoma suspects (PD, P=0.004; ERM, P=0.043). PDs and/or ERMs were present in 16 of 57 (28.1%) glaucomatous eyes, 2 of 44 (4.5%) suspect eyes, and 3 of 101 (3.0%) control eyes. Further, PDs were present in 11 of the 57 (19.3%) glaucomatous eyes, 1 of the 44 (2.3%) suspect eyes and 0 of the 101 (0%) control eyes, ERMs were seen in 7 of the 57 (12.3%) glaucomatous eyes, 1 of the 44 (2.3%) suspects, and 3 of the 101 (3.0%) control eyes.

CONCLUSIONS

Eyes with early glaucoma have a higher frequency of PDs and ERMs than suspects or controls and exhibit PDs even in the absence of ERMs or high myopia.

Artificial intelligence in glaucoma

PURPOSE OF REVIEW

The use of computers has become increasingly relevant to medical decision-making, and artificial intelligence methods have recently demonstrated significant advances in medicine. We therefore provide an overview of current artificial intelligence methods and their applications, to help the practicing ophthalmologist understand their potential impact on glaucoma care.

RECENT FINDINGS

Techniques used in artificial intelligence can successfully analyze and categorize data from visual fields, optic nerve structure [e.g., optical coherence tomography (OCT) and fundus photography], ocular biomechanical properties, and a combination thereof to identify disease severity, determine disease progression, and/or recommend referral for specialized care. Algorithms have become increasingly complex in recent years, utilizing both supervised and unsupervised methods of artificial intelligence. Impressive performance of these algorithms on previously unseen data has been reported, often outperforming standard global indices and expert observers. However, there remains no clearly defined gold standard for determining the presence and severity of glaucoma, which undermines the training of these algorithms. To improve upon existing methodologies, future work must employ more robust definitions of disease, optimize data inputs for artificial intelligence analysis, and improve methods of extracting knowledge from learned results.

SUMMARY

Artificial intelligence has the potential to revolutionize the screening, diagnosis, and classification of glaucoma, both through the automated processing of large data sets, and by earlier detection of new disease patterns. In addition, artificial intelligence holds promise for fundamentally changing research aimed at understanding the development, progression, and treatment of glaucoma, by identifying novel risk factors and by evaluating the importance of existing ones.

Evaluation of Pupil Fields Using a Newly Developed Perimeter in Glaucoma Patients

PURPOSE

To evaluate objective pupil fields using a newly developed perimeter for the detection of glaucomatous damage.

MATERIALS AND METHODS

Forty-three eyes of 32 glaucoma patients (42-69 years) were examined. Glaucomatous eyes were classified into three stages using the Hodapp-Anderson-Parrish grading scale (early, 16; moderate, 14; and severe, 13 eyes). The head-mounted perimeter "imo" was used to measure the percentage pupil constriction (PPC) of the pupil fields at 36 test points. A stimulus target size of Goldmann V with 0 decibels (dB) light under 31.4 apostilbs (asb) background was presented. Visual fields were measured with the Humphrey Field Analyzer 10-2 program. Using the 3D OCT-2000, 10 × 10 grid of the macular thickness were also obtained. Median correlation coefficients (r) of each examined eye were analyzed between the PPC and visual field sensitivity (dB), and the thickness of the retinal nerve fiber layer (RNFL), ganglion cell layer (GCL)⁺ (GCL + inner plexiform layer [IPL]), and GCL⁺⁺ (RNFL + GCL + IPL), respectively.

RESULTS

Moderate correlations between the PPC and dB (r = 0.44-0.55), and GCL⁺⁺ (r = 0.43-0.45) were obtained in the correspondence analysis of 12 test points. There were no significant differences in glaucoma severity (P = 0.924-1.000). However, some patients with extremely early stage glaucoma (visual field index ≥90%) tended to have poor correlation.

CONCLUSIONS

Pupil fields of the imo generally corresponded to the visual fields and the RNFL + GCL + IPL thickness, even in early glaucoma; however, the examiner must clearly understand the criteria of patient selection.

Does Foveal Position Relative to the Optic Disc Affect Optical Coherence Tomography Measurements in Glaucoma?

Objectives:

To determine interindividual variability in the angle between the anatomic axis connecting the fovea and optic disc center and the horizontal meridian using spectral domain optical coherence tomography (OCT).

Materials and Methods:

A total of 260 eyes of 133 subjects (81 women, 52 men) with glaucoma or suspected glaucoma were included in the study retrospectively. Fovea-disc angle (FoDi angle) measurements, determined as the angle between the horizontal meridian passing through the Bruch’s membrane opening (BMO) center and the line connecting the fovea and BMO center, were recorded from spectral domain-OCT scans performed by the same investigator. FoDi angle was defined as negative if the fovea was located below the horizontal meridian through the BMO center and positive if the fovea was located above it.

Results:

The mean age of the participants was 56.5±14.6 years (27-83 years). The mean FoDi angle was -6.43±4.96° (range: -24.40° to +11.60°). Absolute deviation of the fovea BMO axis from the horizontal axis was 0-5° in 83 eyes (31.92%), 5-10° in 124 eyes (47.69%), 10-15° in 41 eyes (15.76%), 15-20° in 10 eyes (3.84%), and greater than 20° in 2 eyes (0.79%).

Conclusion:

Most OCT devices currently used in the treatment and follow-up of glaucoma patients provide peripapillary retinal nerve fiber layer (RNFL) thickness measurements that are made based on a clinical axis in reference to the horizontal meridian passing through the optic disc center. The results of our study reveal interindividual variation in FoDi angle as well as intraindividual differences in FoDi angle between fellow eyes in the same individual. Disparity between clinical and anatomic quadrants could impact RNFL thickness measurements, which may lead to errors in the diagnosis of glaucoma.

Gene therapy in inherited retinal degenerative diseases, a review

Hereditary diseases of the retina represent a group of diseases with several heterogeneous mutations that have the common end result of progressive photoreceptor death leading to blindness. Retinal degenerations encompass multifactorial diseases such as age-related macular degeneration, Leber congenital amaurosis, Stargardt disease, and retinitis pigmentosa. Although there is currently no cure for degenerative retinal diseases, ophthalmology has been at the forefront of the development of gene therapy, which offers hope for the treatment of these conditions. This article will explore an overview of the clinical trials of gene supplementation therapy for retinal diseases that are underway or planned for the near future.

Effectiveness of a Qualitative Approach Toward Evaluating OCT Imaging for Detecting Glaucomatous Damage

Purpose

The purpose of this study was to determine whether a qualitative approach toward evaluating optical coherence tomography (OCT) imaging improves the ability to detect glaucomatous damage compared to a conventional metric of global circumpapillary retinal nerve fiber layer (cpRNFL) thickness.

Methods

A total of 394 healthy eyes and 272 glaucoma eyes were evaluated. Glaucoma eyes were categorized as perimetric (156 eyes) based on a history of three or more consecutive abnormal 24-2 visual field tests or suspected glaucoma if they did not (116 eyes). Customized one-page reports derived using OCT volume scans of the optic disc and macula from these eyes were qualitatively graded for the probability of optic neuropathy affecting the eye.

Results

The sensitivity of detecting perimetric glaucoma eyes with the global circumpapillary RNFL thickness metric and qualitative evaluation of the OCT imaging results were 86.5% and 95.5% at a specificity of 95%, being significantly higher for the latter (P < 0.001). There were seven eyes with perimetric glaucoma missed by the qualitative evaluation. Based upon examination of all available visual fields, at least four of these seven eyes had visual fields that either improved or had abnormalities that were inconsistent over time or with patterns of glaucomatous damage.

Conclusions

Qualitative evaluation of OCT imaging results allows glaucoma eyes with repeatable visual field abnormalities to be detected with a high level of accuracy, performing better than a conventional summary metric of global cpRNFL thickness.

Translational Relevance

Clinical detection of glaucomatous damage with OCT imaging can be optimized through a qualitative evaluation of its results.

Evaluation of a Qualitative Approach for Detecting Glaucomatous Progression Using Wide-Field Optical Coherence Tomography Scans

Purpose

To determine the effectiveness of detecting glaucomatous progression by a qualitative evaluation of wide-field (12 × 9 mm) scans on optical coherence tomography imaging. This method was compared to a conventional quantitative analysis of the global circumpapillary retinal nerve fiber layer (cpRNFL) thickness.

Methods

A total of 409 eyes with a clinical diagnosis of glaucoma or suspected glaucoma for which two wide-field scans were obtained at least 1 year apart (n = 125) and within one session (n = 284) were included to determine the sensitivity of detecting progression at 95% specificity. Qualitative OCT evaluation was performed in a similar manner to flicker chronoscopy by superimposing the two scans, and the progression probability was graded. A quantitative event-based analysis of the global cpRNFL thickness also was performed.

Results

Thirty-three and 25 eyes were deemed to have progressed based on qualitative and quantitative approaches, respectively (P = 0.152). A post hoc review of cases where the two methods disagreed revealed that all eyes missed by the quantitative analysis had established glaucomatous damage that appeared to show characteristic patterns of progression. All eyes missed by the qualitative evaluation appeared to be free of such established damage, and instead showed a generalized reduction in cpRNFL thickness.

Conclusions

Qualitative evaluation of OCT imaging information more frequently detected change consistent with known patterns of glaucomatous progression than global cpRNFL thickness, warranting further studies to evaluate its value.

Translational Relevance

A framework for qualitatively evaluating progressive glaucomatous changes on OCT imaging clinically shows promise.

Comparison of Widefield and Circumpapillary Circle Scans for Detecting Glaucomatous Neuroretinal Thinning on Optical Coherence Tomography

PURPOSE

Our purpose was to compare the effectiveness of detecting progressive retinal nerve fiber layer (RNFL) thickness changes using widefield scans compared to circumpapillary circle scans derived from optic disc volume scans when using a manual region-of-interest (ROI) approach.

METHODS

In a prospective observational study, a total of 125 eyes diagnosed clinically with glaucoma or suspected glaucoma that had both widefield (12 × 9 mm) and optic disc (6 × 6 mm) scans obtained at least one year apart were included. Changes in the RNFL thickness between the two visits were evaluated within region(s) of observed or suspected glaucomatous damage, which were manually outlined after reviewing key features from each scan on the second visit (described as a manual ROI approach). Within ROI(s), changes in the widefield and circumpapillary RNFL thickness (wfRNFLROI and cpRNFLROI), as well as in the global circumpapillary RNFL thickness (cpRNFLG), were determined. The performance of these three methods for detecting progressive changes was compared using longitudinal signal-to-noise ratios (SNRs), whereby the rate of change determined by each method was normalized by individualized estimates of measurement variability and age-related change.

RESULTS

On average, the longitudinal SNRs for the wfRNFLROI, cpRNFLROI, and cpRNFLG methods were -0.57, -0.38, and -0.23 y-1, respectively, being significantly more negative for the wfRNFLROI than the latter two methods (P ≤ 0.009).

CONCLUSIONS

Progressive RNFL thickness changes were more effectively detected on widefield optical coherence tomography (OCT) scans using a manual ROI approach compared to conventional derived circumpapillary circle scans.

TRANSLATIONAL RELEVANCE

Widefield OCT scans show promise for improving the detection of glaucomatous progression.