A perimetric test procedure that uses structural information

Frequency-of-seeing curves (psychometric functions) for perimetric stimuli in age-related macular degeneration

PURPOSE

Frequency-of-seeing (FoS) curves (psychometric functions) for perimetric stimuli have been widely used in computer simulations of new visual field test procedures. FoS curves for age-related macular degeneration (AMD) are not available in the literature and are needed for the development of improved microperimetry test procedures, which are of particular interest for use as clinical trial endpoints.

METHODS

Data were refitted from a previous study to generate FoS curves for 20 participants with AMD, each tested at nine locations within the central 10°. Stimulus parameters, background luminance and dB scale were matched to the MAIA-2 microperimeter, and stimuli were presented in a method of constant stimuli to build up FoS curves over multiple runs. FoS curves were fitted with a modified cumulative Gaussian function. The relationship between sensitivity and slope of fitted FoS curves was modelled by robust linear regression, producing models both with and without an eccentricity parameter.

RESULTS

FoS curves were satisfactorily fitted to data from 174 visual field locations in 20 participants (age 65-83 years, 11 female). Each curve was made up of a median of 243 (range 177-297) stimulus presentations over a median of 12 (range 9-32) levels. Median sensitivity was 25.5 dB (range 3.8-31.4 dB). The median slope (SD of fitted function) was 1.6 dB (range 0.5-8.5 dB). As in previous studies of other conditions, the slope of fitted FoS curves increased as sensitivity decreased (p < 0.001).

CONCLUSIONS

FoS are provided for participants with AMD, as well as models of the relationship between sensitivity and slope. These fitted models and data may be useful for computer simulation studies of microperimetry procedures. Full details of the fitted curves are provided as supporting information.

A Practical Framework for the Integration of Structural Data Into Perimetric Examinations

Purpose

We sought to develop and evaluate a practical framework that supports structurally enhanced perimetric examinations.

Methods

Two perimetric strategies were compared: standard Zippy Estimation through Sequential Testing (ZEST) procedure, a traditional visual field test with population-based prior distributions, and structural-ZEST (S-ZEST), enhanced with individual optical coherence tomography data to determine the starting parameters. The integration and collection of data was facilitated by a bespoke application developed in Shiny R (R Studio). The test was implemented using the Open Perimetry Interface on the Compass perimeter (CentreVue-iCare, Italy). The strategies were evaluated via simulations and on 10 visually healthy participants. The usability of the application was assessed in a simulated environment with 10 test users.

Results

In simulations, the S-ZEST improved test speed in patients with glaucoma. In the practical implementation, there was a statistically significant decrease in the testing time (approximately 26%) and in the number of presentations per test with S-ZEST (P < 0.001). The structure-function relationship was similar between the two strategies. The time taken for users to complete the sequence of actions on the application was 52.9 ± 11.5 seconds (mean ± standard deviation).

Conclusions

Structurally enhanced perimetric examination can significantly improve test time in healthy subjects and can be delivered through a user-friendly interface. Further testing will need to assess feasibility and performance of S-ZEST in patients with glaucoma.

Translational Relevance

We have developed a user-friendly web application based within the Shiny environment for R, which implements an automated extraction of optical coherence tomography data from raw files and performs real-time calculations of structural features to inform the perimetric strategy.

Improving the Accuracy and Speed of Visual Field Testing in Glaucoma With Structural Information and Deep Learning

Purpose

To assess the performance of a perimetric strategy using structure-function predictions from a deep learning (DL) model.

Methods

Visual field test-retest data from 146 eyes (75 patients) with glaucoma with (median [5th-95th percentile]) 10 [7, 10] tests per eye were used. Structure-function predictions were generated with a previously described DL model using cicumpapillary optical coherence tomography (OCT) scans. Structurally informed prior distributions were built grouping the observed measured sensitivities for each predicted value and recalculated for each subject with a leave-one-out approach. A zippy estimation by sequential testing (ZEST) strategy was used for the simulations (1000 per eye). Ground-truth sensitivities for each eye were the medians of the test-retest values. Two variations of ZEST were compared in terms of speed (average total number of presentations [NP] per eye) and accuracy (average mean absolute error [MAE] per eye), using either a combination of normal and abnormal thresholds (ZEST) or the calculated structural distributions (S-ZEST) as prior information. Two additional versions of these strategies employing spatial correlations were tested.

Results

S-ZEST was significantly faster, with a mean average NP of 213.87 (SD = 28.18), than ZEST, with a mean average NP of 255.65 (SD = 50.27) (P < 0.001). The average MAE was smaller for S-ZEST (1.98; SD = 2.37) than ZEST (2.43; SD = 2.69) (P < 0.001). Spatial correlations further improved both strategies (P < 0.001), but the differences between ZEST and S-ZEST remained significant (P < 0.001).

Conclusions

DL structure-function predictions can significantly improve perimetric tests.

Translational Relevance

DL structure-function predictions from clinically available OCT scans can improve perimetry in glaucoma patients.

The Usefulness of Assessing Glaucoma Progression With Postprocessed Visual Field Data

Purpose

Data postprocessing with statistical techniques that are less sensitive to noise can be used to reduce variability in visual field (VF) series. We evaluated the detection of glaucoma progression with postprocessed VF data generated with the dynamic structure–function (DSF) model and MM-estimation robust regression (MRR).

Method

The study included 118 glaucoma eyes with at least 15 visits selected from the Rotterdam dataset. The DSF and MRR models were each applied to observed mean deviation (MD) values from the first three visits (V_1–3) to predict the MD at V_4. MD at V₅ was predicted with data from V_1–4 and so on until the MD at V₉ was predicted, creating two additional datasets: DSF-predicted and MRR-predicted. Simple linear regression was performed to assess progression at the ninth visit. Sensitivity was evaluated by adjusting for false-positive rates estimated from patients with stable glaucoma and by using longer follow-up series (12th and 15th visits) as a surrogate for progression.

Results

For specificities of 80% to 100%, the DSF-predicted dataset had greater sensitivity than the observed and MRR-predicted dataset when positive rates were normalized with corresponding false-positive estimates. The DSF-predicted and observed datasets had similar sensitivity when the surrogate reference standard was applied.

Conclusions

Without compromising specificity, the use of DSF-predicted measurements to identify progression resulted in a better or similar sensitivity compared to using existing VF data.

Translational Relevance

The DSF model could be applied to postprocess existing visual field data, which could then be evaluated to identify patients at risk of progression.

Policy-Driven, Multimodal Deep Learning for Predicting Visual Fields from the Optic Disc and Optical Coherence Tomography Imaging

PURPOSE

To develop and validate a deep learning (DL) system for predicting each point on visual fields (VF) from disc and optical coherence tomography (OCT) imaging and derive a structure-function mapping.

DESIGN

Retrospective, cross-sectional database study PARTICIPANTS: 6437 patients undergoing routine care for glaucoma in three clinical sites in the UK.

METHODS

OCT and infrared reflectance (IR) optic disc imaging was paired with the closest VF within 7 days. Efficient-Net B2 was used to train two single modality DL models to predict each of the 52 sensitivity points on the 24-2 VF pattern. A policy DL model was designed and trained to fuse the two model predictions.

MAIN OUTCOME MEASURES

Pointwise Mean Absolute Error (PMAE) RESULTS: A total of 5078 imaging to VF pairs were used as a held-out test set to measure the final performance. The improvement in PMAE with the policy model was 0.485 [0.438, 0.533] dB compared to the IR image of the disc alone and 0.060 [0.047, 0.073] dB compared to the OCT alone. The improvement with the policy fusion model was statistically significant (p < 0.0001). Occlusion masking shows that the DL models learned the correct structure function mapping in a data-driven, feature agnostic fashion.

CONCLUSIONS

The multimodal, policy DL model performed the best; it provided explainable maps of its confidence in fusing data from single modalities and provides a pathway for probing the structure-function relationship in glaucoma.

Enhanced Objective Detection of Retinal Nerve Fiber Bundle Defects in Glaucoma With a Novel Method for En Face OCT Slab Image Construction and Analysis

Purpose

To introduce and evaluate the performance in detecting glaucomatous abnormalities of a novel method for extracting en face slab images (SMAS), which considers varying individual anatomy and configuration of retinal nerve fiber bundles.

Methods

Dense central retinal spectral domain optical coherence tomography scans were acquired in 16 participants with glaucoma and 19 age-similar controls. Slab images were generated by averaging reflectivity over different depths below the inner limiting membrane according to several methods. SMAS considered multiple 16 µm thick slabs from 8 to 116 µm below the inner limiting membrane, whereas 5 alternative methods considered single summary slabs of various thicknesses and depths. Superpixels in eyes with glaucoma were considered abnormal if below the first percentile of distributions fitted to control data for each method. The ability to detect glaucoma defects was measured by the proportion of abnormal superpixels. Proportion of superpixels below the fitted first percentile in controls was used as a surrogate false-positive rate. The effects of slab methods on performance measures were evaluated with linear mixed models.

Results

The ability to detect glaucoma defects varied between slab methods, χ2(5) = 120.9, P < 0.0001, with SMAS showing proportion of abnormal superpixels 0.05 to 0.09 greater than alternatives (all P < 0.0001). No slab method found abnormal superpixels in controls.

Conclusions

SMAS outperformed alternatives in detecting abnormalities in eyes with glaucoma. SMAS evaluates all depths with potential retinal nerve fiber bundle presence by combining multiple slabs, resulting in greater detection of reflectance abnormalities with no increase in surrogate false positives.

Translational Relevance

SMAS may be used to objectively detect glaucoma defects in en face optical coherence tomography images.

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.

Comparison of 10-2 and 24-2C Test Grids for Identifying Central Visual Field Defects in Glaucoma and Suspect Patients

PURPOSE

To compare the ability of 24-2C and 10-2 test grids in measuring visual field global indices, identifying central visual field defects, and facilitating macular structure-function analysis with OCT scans in glaucoma and glaucoma suspect patients.

DESIGN

Prospective, cross-sectional study.

PARTICIPANTS

One eye from 131 glaucoma and 57 glaucoma suspect patients recruited from a referral-only, university-based glaucoma clinic.

METHODS

Each subject underwent perimetric testing using 24-2C SITA-Faster and 10-2 SITA-Fast in random order, and Cirrus OCT macular imaging (Ganglion Cell Analysis) for structure-function correlations.

MAIN OUTCOME MEASURES

Visual field global indices (mean deviation, pattern standard deviation, binarized "cluster" pass/fail, and central mean sensitivity), number and proportion of visual field defects, and structure-function concordance with the Cirrus OCT deviation map following visual field location displacement for correspondence with underlying retinal ganglion cell position.

RESULTS

Global indices (mean deviation, pattern standard deviation, and central mean sensitivity) were similar between both grids. The 10-2 detected more defects compared with the 24-2C (P < 0.0001 for all patients, P = 0.006 for glaucoma patients). This was preserved when analyzing the proportion of defects in the central visual field for all patients (P = 0.02) but was not significantly different for glaucoma patients (P = 0.051). The 10-2 identified more central "clusters" of 2+ contiguous points of deficit (P < 0.0001). Structure-function comparisons performed at locations where visual field and OCT test locations were colocalized revealed greater concordance of structural and functional deficits using the 10-2 (P < 0.0001). The 10-2 took a median of 201 seconds, and the 24-2C took a median of 154 seconds, corresponding to the different thresholding algorithms.

CONCLUSIONS

The 24-2C and 10-2 test grids return similar global indices of visual field performance and proportionally similar amounts of central visual field loss. The additional points in the 10-2 grid return more "clusters" of defects and a greater rate of structure-function concordance compared with the 24-2C test grid. Thus, the 24-2C can identify the presence of a clustered central visual field defect using similar probability criteria, whereas the 10-2 may be more useful in comprehensively characterizing the defect and predicting central visual function.

Improving Personalized Structure to Function Mapping From Optic Nerve Head to Visual Field

Purpose

Maps are required to relate visual field locations to optic nerve head regions. We compare individualized structure-to-function mapping (CUSTOM-MAP) to a population-derived mapping schema (POP-MAP).

Methods

Maps were compared for 118 eyes with glaucomatous field loss, circumpapillary retinal nerve fiber layer (cpRNFL) thickness measured using spectral domain optical coherence tomography (OCT), and two landmarks: the optic nerve head (ONH) position relative to the fovea and the temporal raphe angle. Locations with visual field damage (total deviation < −6 dB) were mapped to 30° ONH sectors centered on the angle given by each mapping schema. The concordance between damaged function and damaged structure was determined per location for various cpRNFL damage probability levels, with the number of concordant locations divided by the total number of damaged field locations providing a concordance ratio per eye.

Results

For the strictest concordance criteria (minimum cpRNFL thickness < 1% of normal), CUSTOM-MAP had higher mean concordance ratio than POP-MAP (60.5% c.f. 57.0% paired Wilcoxon, P = 0.005), with CUSTOM-MAP having a higher ratio in 43 eyes and POP-MAP having a higher ratio in 21 eyes. For all cpRNFL probability levels <20% of normal, more locations concorded for CUSTOM-MAP than POP-MAP. Inspection of the spatial patterns of differences revealed that CUSTOM-MAP often performed better in the arcuate regions, whereas POP-MAP had benefits inferior to the macula.

Conclusions

Anatomic parameters required for individualized structure-function mapping are readily measured with OCT and can provide improved concordance for some eyes.

Translational Relevance

Personalizing structure-function mapping may improve concordance between these measures. We provide a web-based tool for creating customized maps.

Effects of Criterion Bias on Perimetric Sensitivity and Response Variability in Glaucoma

Purpose

The purpose of this study was to isolate and quantify the effects of observer response criterion on perimetric sensitivity, response variability, and maximum response probability.

Methods

Twelve people with glaucoma were tested at three locations in the visual field (age = 47-77 years, mean deviation = -0.61 to -14.54 dB, test location Humphrey field analyzer [HFA] sensitivities = 1 to 30 dB). Frequency of seeing (FoS) curves were measured using a method of constant stimuli with two response paradigms: a "yes-no" paradigm similar to static automated perimetry and a criterion-free two interval forced choice (2IFC) paradigm. Comparison measures of sensitivity, maximum response probability, and response variability were derived from the fitted FoS curves.

Results

Sensitivity differences between the tasks varied widely (range = -11.3 dB to 21.6 dB) and did not correlate with visual field sensitivity nor whether the visual field location was in an area of steep sensitivity gradient within the visual field. Due to the wide variation in differences between the methods, there was no significant difference in mean sensitivity between the 2IFC task relative to the yes-no task, but a trend for higher sensitivity (mean = 1.9 dB, SD = 6.0 dB, P = 0.11). Response variability and maximum response probability did not differ between the tasks (P > 0.99 and 0.95, respectively).

Conclusions

Perimetric sensitivity estimates are demonstrably altered by observer response criterion but the effect varies widely and unpredictably, even within a single test. Response bias should be considered a factor in perimetric test variability and when comparing sensitivities to nonperimetric data.

Translational Relevance

The effect of response criterion on perimetric response variability varies widely and unpredictably, even within a single test.

Depth-resolved variations in visibility of retinal nerve fibre bundles across the retina in enface OCT images of healthy eyes

PURPOSE

Recent developments in optical coherence tomography (OCT) technology enable direct enface visualisation of retinal nerve fibre bundle (RNFB) loss in glaucoma. However, the optimum depth at which to visualise RNFBs across the retina is unknown. We aimed to evaluate the range of depths and optimum depth at which RNFBs can be visualised across the retina in healthy eyes.

METHODS

The central ± 25° retina of 10 healthy eyes from 10 people aged 57-75 years (median 68.5 years) were imaged with spectral domain OCT. Slab images of maximum axial resolution (4 μm) containing depth-resolved attenuation coefficients were extracted from 0 to 193.5 μm below the inner limiting membrane (ILM). Bundle visibility within 10 regions of a superimposed grid was assessed subjectively by trained optometrists (n = 8), according to written instructions. Anterior and posterior limits of RNFB visibility and depth of best visibility were identified for each grid sector. Effects of retinal location and individual eye on RNFB visibility were explored using linear mixed modelling with likelihood ratio tests. Intraclass correlation coefficient (ICC) was used to measure overall agreement and repeatability of grading. Spearman's correlation was used to measure correlation between depth range of visible RNFBs and retinal nerve fibre layer thickness (RNFLT).

RESULTS

Retinal location and individual eye affected anterior limit of visibility (χ² ₍₉₎  = 58.6 and 60.5, both p < 0.0001), but none of the differences exceeded instrument resolution, making anterior limit consistent across the retina and different eyes. Greater differences were observed in the posterior limit of visibility across retinal areas (χ² ₍₉₎  = 1671.1, p < 0.0001) and different eyes (χ² ₍₉₎  = 88.7, p < 0.0001). Optimal depth for visualisation of RNFBs was around 20 µm below the ILM in most regions. It varied slightly with retinal location (χ² ₍₉₎  = 58.8, p < 0.0001), but it was not affected by individual eye (χ² ₍₉₎  = 10.7, p = 0.29). RNFB visibility showed good agreement between graders (ICC 0.89, 95%CI 0.87-0.91), and excellent repeatability (ICC 0.96-0.99). Depth range of visible RNFBs was highly correlated with RNFLT (ρ = 0.9, 95%CI: 0.86-0.95).

CONCLUSIONS

The range of depths with visible RNFBs varies markedly across the healthy retina, consistently with RNFLT. To extract all RNFB information consistently across the retina, slab properties should account for differences across retinal locations and between individual eyes.

Clinical Evaluation of Swedish Interactive Thresholding Algorithm-Faster Compared With Swedish Interactive Thresholding Algorithm-Standard in Normal Subjects, Glaucoma Suspects, and Patients With Glaucoma

PURPOSE

To compare the visual fields results obtained using the Swedish interactive thresholding algorithm-Standard (SS) and the Swedish interactive thresholding algorithm-Faster (SFR) in normal subjects, glaucoma suspects, and patients with glaucoma and to quantify potential time-saving benefits of the SFR algorithm.

DESIGN

Prospective, cross-sectional study.

METHODS

One randomly selected eye from 364 patients (77 normal subjects, 178 glaucoma suspects, and 109 patients with glaucoma) seen in a single institution underwent testing using both SS and SFR on the Humphrey Field Analyzer. Cumulative test time using each algorithm was compared after accounting for different rates of test reliability. Pointwise and cluster analysis was performed to determine whether there were systematic differences between algorithms.

RESULTS

Using SFR had a greater rate of unreliable results (29.3%) compared with SS (7.7%, P < .0001). This was mainly because of high false positive rates and seeding point errors. However, modeled test times showed that using SFR could obtain a greater number of reliable results within a shorter period of time. SFR resulted in higher sensitivity values (on average 0.5 dB for patients with glaucoma) that was greater under conditions of field loss (<19 dB). Cluster analysis showed no systematic patterns of sensitivity differences between algorithms.

CONCLUSIONS

After accounting for different rates of test reliability, SFR can result in significant time savings compared with SS. Clinicians should be cognizant of false positive rates and seeding point errors as common sources of error for SFR. Results between algorithms are not directly interchangeable, especially if there is a visual field deficit <19 dB.

Correlating Structural and Functional Damage in Glaucoma

Structural and functional tests are essential for detecting and monitoring glaucomatous damage. However, the correlations between structural and functional tests in glaucoma are complex and faulty, with the combination of both modalities being recommended for better assessment of glaucoma. The objective of this review is to explore investigations from the last 5 years in the field of structure-function correlation in glaucoma that contributed to increment in the understanding of this correlation and have the potential to improve the diagnosis and detection of glaucoma progression.

Performance of a Defect-Mapping Microperimetry Approach for Characterizing Progressive Changes in Deep Scotomas

Purpose

To examine whether a microperimetry testing strategy based on quantifying the spatial extent of functional abnormalities (termed “defect-mapping” strategy) could improve the detection of progressive changes in deep scotomas compared to the conventional thresholding strategy.

Methods

A total of 30 healthy participants underwent two microperimetry examinations, each using the defect-mapping and thresholding strategies at the first visit to examine the test–retest variability of each method. Testing was performed using an isotropic stimulus pattern centered on the optic nerve head (ONH), which acted as a model of a deep scotoma. These tests were repeated at a second visit, except using a smaller stimulus pattern and thereby increasing the proportion of test locations falling within the ONH (to simulate the progressive enlargement of a deep scotoma). The extent of change detected between visits relative to measurement variability was compared between the two strategies.

Results

Relative to their effective dynamic ranges, the test–retest variability of the defect-mapping strategy (1.8%) was significantly lower compared to the thresholding strategy (3.3%; P < 0.001). The defect-mapping strategy also captured a significantly greater extent of change between visits relative to variability (−4.70 t⁻¹) compared to the thresholding strategy (2.74 t⁻¹; P < 0.001).

Conclusions

A defect-mapping microperimetry testing strategy shows promise for capturing the progressive enlargement of deep scotomas more effectively than the conventional thresholding strategy.

Translational Relevance

Microperimetry testing with the defect-mapping strategy could provide a more accurate clinical trial outcome measure for capturing progressive changes in deep scotomas in eyes with atrophic retinal diseases, warranting further investigations.

Patient-attentive sequential strategy for perimetry-based visual field acquisition

Perimetry is a non-invasive clinical psychometric examination used for diagnosing ophthalmic and neurological conditions. At its core, perimetry relies on a subject pressing a button whenever they see a visual stimulus within their field of view. This sequential process then yields a 2D visual field image that is critical for clinical use. Perimetry is painfully slow however, with examinations lasting 7-8 minutes per eye. Maintaining high levels of concentration during that time is exhausting for the patient and negatively affects the acquired visual field. We introduce PASS, a novel perimetry testing strategy, based on reinforcement learning, that requires fewer locations in order to effectively estimate 2D visual fields. PASS uses a selection policy that determines what locations should be tested in order to reconstruct the complete visual field as accurately as possible, and then separately reconstructs the visual field from sparse observations. Furthermore, PASS is patient-specific and non-greedy. It adaptively selects what locations to query based on the patient's answers to previous queries, and the locations are jointly selected to maximize the quality of the final reconstruction. In our experiments, we show that PASS outperforms state-of-the-art methods, leading to more accurate reconstructions while reducing between 30% and 70% the duration of the patient examination.

Improving Visual Field Examination of the Macula Using Structural Information

Purpose

To investigate a novel approach for structure-function modeling in glaucoma to improve visual field testing in the macula.

Methods

We acquired data from the macular region in 20 healthy eyes and 31 with central glaucomatous damage. Optical coherence tomography (OCT) scans were used to estimate the local macular ganglion cell density. Perimetry was performed with a fundus-tracking device using a 10-2 grid. OCT scans were matched to the retinal image from the fundus perimeter to accurately map the tested locations onto the structural damage. Binary responses from the subjects to all presented stimuli were used to calculate the structure-function model used to generate prior distributions for a ZEST (Zippy Estimation by Sequential Testing) Bayesian strategy. We used simulations based on structural and functional data acquired from an independent dataset of 20 glaucoma patients to compare the performance of this new strategy, structural macular ZEST (MacS-ZEST), with a standard ZEST.

Results

Compared to the standard ZEST, MacS-ZEST reduced the number of presentations by 13% in reliable simulated subjects and 14% with higher rates (≥20%) of false positive or false negative errors. Reduction in mean absolute error was not present for reliable subjects but was gradually more important with unreliable responses (≥10% at 30% error rate).

Conclusions

Binary responses can be modeled to incorporate detailed structural information from macular OCT into visual field testing, improving overall speed and accuracy in poor responders.

Translational Relevance

Structural information can improve speed and reliability for macular testing in glaucoma practice.

Relating optical coherence tomography to visual fields in glaucoma: structure-function mapping, limitations and future applications

Combining information from optical coherence tomography (OCT) imaging and visual field testing is useful in the clinical assessment and monitoring of patients with glaucoma. Measurements of retinal nerve fibre layer thickness or neuroretinal rim width taken around the optic nerve head may be related to the visual field using a structure-function map. In this review, the structure-function mapping methods in clinical use are discussed. Typical clinical maps provide a population average, 'one size fits all' representation, but in recent years methods for customising structure-function maps to individual eyes have been developed and these are reviewed here. In the macula, visual field stimuli stimulate photoreceptors for which associated retinal ganglion cells are peripherally displaced. Recently developed methods that relate OCT measurements to visual field test locations in the macula are therefore also reviewed. The use of structure-function maps to relate OCT measurements to localised visual field sensitivity in new applications is also explored. These new applications include the selection of visual field test locations and stimulus intensities based on OCT data, and the formal post-test combination of results across modalities. Such applications promise to exploit the structure-function relationship in glaucoma to improve disease diagnosis and monitoring of progression. Limitations in the validation and use of current structure-function mapping techniques are discussed.

Improving Spatial Resolution and Test Times of Visual Field Testing Using ARREST

Purpose

Correctly classifying progression in moderate to advanced glaucoma is difficult. Pointwise visual field test–retest variability is high for sensitivities below approximately 20 dB; hence, reliably detecting progression requires many test repeats. We developed a testing approach that does not attempt to threshold accurately in areas with high variability, but instead expends presentations increasing spatial fidelity.

Methods

Our visual field procedure Australian Reduced Range Extended Spatial Test (ARREST; a variant of the Bayesian procedure Zippy Estimation by Sequential Testing [ZEST]) applies the following approach: once a location has an estimated sensitivity of <17 dB (a “defect”), it is checked that it is not an absolute defect (<0 dB, “blind”). Saved presentations are used to test extra locations that are located near the defect. Visual field deterioration events are either: (1) decreasing in the range of 40 to 17 dB, (2) decreasing from >17 dB to “defect”, or (3) “defect” to blind. To test this approach we used an empirical database of progressing moderate-advanced 24-2 visual fields (121 eyes) that we “reverse engineered” to create visual field series that progressed from normal to the end observed field. ARREST and ZEST were run on these fields with test accuracy, presentation time, and ability to detect progression compared.

Results

With specificity for detecting progression matched at 95%, ZEST and ARREST showed similar sensitivity for detecting progression. However, ARREST used approximately 25% to 40% fewer test presentations to achieve this result in advanced visual field damage. ARREST spatially defined the visual field deficit with greater precision than ZEST due to the addition of non–24-2 locations.

Conclusions

Spending time trying to accurately measure visual field locations that have high variability is not productive. Our simulations indicate that giving up attempting to quantify size III white-on-white sensitivities below 17 dB and using the presentations saved to test extra locations should better describe progression in moderate-to-advanced glaucoma in shorter time.

Translational Relevance

ARREST is a new visual field test algorithm that provides better spatial definition of visual field defects in faster test time than current procedures. This outcome is achieved by substituting inaccurate quantification of sensitivities <17 dB with new spatial locations.

Development of a Visual Field Simulation Model of Longitudinal Point-Wise Sensitivity Changes From a Clinical Glaucoma Cohort

Purpose

To develop a new visual field simulation model that can recreate real-world longitudinal results at a point-wise level from a clinical glaucoma cohort.

Methods

A cohort of 367 glaucoma eyes from 265 participants seen over 10.1 ± 2.5 years were included to obtain estimates of “true” longitudinal visual field point-wise sensitivity and estimates of measurement variability. These two components were then combined to reconstruct visual field results in a manner that accounted for correlated measurement error. To determine how accurately the simulated results reflected the clinical cohort, longitudinal variability estimates of mean deviation (MD) were determined by calculating the SD of the residuals from linear regression models fitted to the MD values over time for each eye in the simulated and clinical cohorts. The new model was compared to a previous model that does not account for spatially correlated errors.

Results

The SD of all the residuals for the clinical and simulated cohorts was 1.1 dB (95% confidence interval [CI]: 1.1–1.2 dB) and 1.1 dB (95% CI: 1.1–1.1 dB), respectively, whereas it was 0.4 dB (95% CI: 0.4–0.4 dB) using the previous simulation model that did not account for correlated errors.

Conclusions

A new simulation model accounting for correlated measurement errors between visual field locations performed better than a previous model in estimating visual field variability in glaucoma.

Translational Relevance

This model can provide a powerful framework to better understand use of visual field testing in clinical practice and trials and to evaluate new methods for detecting progression.