Visual Field Data Research Articles

Prediction of visual field progression in glaucoma: existing methods and artificial intelligence.

Timely treatment is essential in the management of glaucoma. However, subjective assessment of visual field (VF) progression is not recommended, because it can be unreliable.There are two types of artificial intelligence (AI) strong and weak (machine learning). Weak AIs can perform specific tasks. Linear regression is a method of weak AI. Using linear regression in the real-world clinic has enabled analyzing and predicting VF progression. However, caution is still required when interpreting the results, because whenever the number of VF data sets investigated is small, the predictions can be inaccurate. Several other non-ordinal, or modern AI methods have been constructed to improve prediction accuracy, such as clustering and more modern AI methods of Analysis with Non-Stationary Weibull Error Regression and Spatial Enhancement (ANSWERS), Variational Bayes Linear Regression (VBLR), Kalman Filter and sparse modeling (The least absolute shrinkage and selection operator regression: Lasso). It is also possible to improve the prediction performance using retinal thickness measured with optical coherence tomography by using machine learning methods, such as multitask learning.

Racial, Ethnic, and Socioeconomic Disparities in Glaucoma Onset and Severity in a Diverse Nationwide Cohort in the United States.

Racial/ethnic minorities are diagnosed with glaucoma at younger ages, and Blacks are more likely to be diagnosed with moderate-to-severe glaucoma. In addition, we highlight a gap in the use of diagnosis codes. The purpose of this study was to analyze patterns of diagnosis coding usage and validate epidemiologic patterns of glaucoma onset and severity among primary glaucoma patients within the National Institutes of Health All of Us database. We used International Classification of Disease diagnosis codes to build 4 cohorts of patients with mild, moderate, severe, and unspecified stage glaucoma (N=2982). Descriptive analyses were stratified by disease stage, and mean age at diagnosis was compared across racial and ethnic groups. Multivariable ordinal regression was used to examine risk factors for increasing glaucoma severity. Of 2982 participants, 1714 (57%) had unspecified severity staging. Black/African Americans and other races were diagnosed with glaucoma at significantly younger ages compared with Whites (means 60 and 60 vs. 66y; P <0.001). Hispanic/Latino participants also had an earlier mean age of diagnosis (61 vs. 65y; P =0.001). Black/African Americans had higher odds of more severe glaucoma (odds ratio: 2.20, 95% CI, 1.62-3.30; P <0.001) than Whites when adjusting for socioeconomic characteristics. Black, Hispanic/Latino, and other minority participants are diagnosed with glaucoma at younger ages, and Blacks are more likely to be diagnosed with moderate-to-severe glaucoma. These findings validate prior population-based studies. Furthermore, we observed a gap in the use of diagnosis codes, as only 43% of participants had a specified severity stage in this national cohort. This may have implications for large-scale observational research concerning glaucoma severity, as electronic health records and claims databases typically lack other measures of disease progression, such as imaging and visual field data.

Improving glaucoma staging in clinical practice by combining the ICD-10 glaucoma severity classification system and optical coherence tomography

ObjectiveThe International Classification of Disease, 10th revision (ICD-10) codes used for glaucoma severity classification are based on the 24-2 visual-field (VF) test. This study aim was to assess the added value of providing clinicians with optical coherence tomography (OCT) data, in addition to functional data, for glaucoma staging in clinical practice.ExposureDisease classification was determined for 54 glaucoma eyes, according to the principles of the ICD-10 guidelines. Eyes were independently graded in a masked fashion using the 24-2 VF test and 10-2 VF test, with and without OCT information. The reference standard (RS) for severity was determined using a previously published automated structure-function topographic agreement for glaucomatous damage using all available information.ResultsThe RS classified eyes as mild, moderate and advanced in 3, 16 and 35 cases, respectively. Individual and combined 24-2 and 10-2 based gradings were significantly different from the RS (all P < 0.005), with Kappa agreements of 0.26, 0.45 and 0.42 respectively (P < 0.001). Classifications using OCT combined with either of the VF were not-significantly different from the RS (P > 0.3) with Kappa agreements of 0.56 and 0.57 respectively (P < 0.001). Combining 24-2 with OCT had less severity overestimations while 10-2 with OCT had fewer underestimations.ConclusionCombining OCT and VF data provides better staging of glaucoma severity than VF data alone. The 24-2 and OCT combination seems most appropriate given the high concordance with the RS and less overestimation of severity. Incorporating structural information into disease stages allows clinicians to set more appropriate severity-based treatment targets for individual patients.

A Data-Driven Model for Simulating Longitudinal Visual Field Tests in Glaucoma.

To develop a simulation model for glaucomatous longitudinal visual field (VF) tests with controlled progression rates. Longitudinal VF tests of 1008 eyes from 755 patients with glaucoma were used to learn the statistical characteristics of VF progression. The learned statistics and known anatomic correlations between VF test points were used to automatically generate progression patterns for baseline fields of patients with glaucoma. VF sequences were constructed by adding spatially correlated noise templates to the generated progression patterns. The two one-sided test (TOST) procedure was used to analyze the equivalence between simulated data and data from patients with glaucoma. VF progression detection rates in the simulated VF data were compared to those in patients with glaucoma using mean deviation (MD), cluster, and pointwise trend analysis. VF indices (MD, pattern standard deviation), MD linear regression slopes, and progression detection rates for the simulated and patients' data were practically equivalent (TOST P < 0.01). In patients with glaucoma, the detection rates in 7 years using MD, cluster, and pointwise trend analysis were 24.4%, 26.2%, and 38.4%, respectively. In the simulated data, the mean detection rates (95% confidence interval) for MD, cluster, and pointwise trend analysis were 24.7% (24.1%-25.2%), 24.9% (24.2%-25.5%), and 35.7% (34.9%-36.5%), respectively. A novel simulation model generates glaucomatous VF sequences that are practically equivalent to longitudinal VFs from patients with glaucoma. Simulated VF sequences with controlled progression rates can support the evaluation and optimization of methods to detect VF progression and can provide guidance for the interpretation of longitudinal VFs.

Descriptive study of patients with nonsyndromic retinitis pigmentosa

Purpose The aim of this study was to describe the demographic and clinical characteristics of a large cohort of patients with retinitis pigmentosa (RP) in Egypt. Patients and methods An extensive electronic medical record search was performed to gather data on all patients with RP who were evaluated at two major referral hospitals: Ain Shams University Hospital and Watany Eye Hospital, Cairo, Egypt during a 7-year period from January 2015 to October 2022. The age at last visit, sex, family history, history of parental consanguinity, status of the fundus, lens, and macula, visual acuity, visual field, and electroretinogram (ERG) data were recorded. Results The study included 766 patients with nonsyndromic RP. They were diagnosed based on either clinical examination alone or clinical examination and superadded investigations in the form of any of the following: color fundus photography, fundus autofluorescence, fundus fluorescein angiography, optical coherence tomography, and/or electrophysiological studies such as full-field and pattern ERG. The mean age at the last visit was 39.6±17.3 years, and 57.9% of the patients were males. Cataract was found in 343 (44.8%) patients, of whom 178 (51.9%) patients underwent cataract surgery. ERG was reduced in 12.9% and abolished in 87.1% of the patients. The foveal area was preserved in five (4.9%) patients, atrophic in 72 (69.9%), edematous in 13 (12.6%), and revealed an epiretinal membrane in 37 (35.9%) patients. A hyperautofluorescent ring was present in 87.5% of the patients. Conclusions Cataract was a significant comorbidity in patients with RP, and atrophic macula was the most prevalent among the macular status changes. RP is the subject of clinical intervention trials, and population-based epidemiological data can guide genetic testing and provide more candidates for possible gene therapy and other new treatment modalities.

Frequency of Visual Fields Needed to Detect Glaucoma Progression: A Computer Simulation Using Linear Mixed Effects Model.

Irregular visual field test frequency at relatively short intervals initially and longer intervals later on in the disease provided acceptable results in detecting glaucoma progression. It is challenging to maintain a balance between the frequency of visual field testing and the long-term costs that may result from insufficient treatment of glaucoma patients. This study aims to simulate real-world circumstances of visual field data to determine the optimum follow-up scheme for the timely detection of glaucoma progression using a linear mixed effects model (LMM). An LMM with random intercept and slope was used to simulate the series of mean deviation sensitivities over time. A cohort study including 277 glaucoma eyes that were followed for 9.0±1.2 years was used to derive residuals. Data were generated from patients with early-stage glaucoma having various regular and irregular follow-up scenarios and different rates of visual field loss. For each condition, 10,000 series of eyes were simulated, and one confirmatory test was conducted to identify progression. By doing one confirmatory test, the percentage of incorrect progression detection decreased considerably. The time to detect progression was shorter for eyes with an evenly spaced 4-monthly schedule, particularly in the first 2 years. From then onward, results from twice-a-year testing were similar to results from examinations scheduled 3 times per year. Irregular visual field test frequency at relatively short intervals initially and longer intervals later on in the disease provided acceptable results in detecting glaucoma progression. This approach could be considered for improving glaucoma monitoring. Moreover, simulating data using LMM may provide a better estimate of the disease progression time.

Forecasting Risk of Future Rapid Glaucoma Worsening Using Early Visual Field, OCT, and Clinical Data

To assess whether we can forecast future rapid visual field (VF) worsening using deep learning models (DLMs) trained on early VF, OCT, and clinical data. A retrospective cohort study. In total, 4536 eyes from 2962 patients. Overall, 263 (5.80%) eyes underwent rapid VF worsening (mean deviation slope less than -1 dB/year across all VFs). We included eyes that met the following criteria: (1) followed for glaucoma or suspect status; (2) had at least 5 longitudinal reliable VFs (VF1, VF2, VF3, VF4, and VF5); and (3) had 1 reliable baseline OCT scan (OCT1) and 1 set of baseline clinical measurements (clinical1) at the time of VF1. We designed a DLM to forecast future rapid VF worsening. The input consisted of spatially oriented total deviation values from VF1 (including or not including VF2 and VF3 in some models) and retinal nerve fiber layer thickness values from the baseline OCT. We passed this VF/OCT stack into a vision transformer feature extractor, the output of which was concatenated with baseline clinical data before putting it through a linear classifier to predict the eye's risk of rapid VF worsening across the 5 VFs. We compared the performance of models with differing inputs by computing area under the curve (AUC) in the test set. Specifically, we trained models with the following inputs: (1) model V: VF1; (2) VC: VF1+ Clinical1; (3) VO: VF1+ OCT1; (4) VOC: VF1+ Clinical1+ OCT1; (5) V2: VF1+ VF2; (6) V2OC: VF1+ VF2+ Clinical1+ OCT1; (7) V3: VF1+ VF2+ VF3; and (8) V3OC: VF1+ VF2+ VF3+ Clinical1+ OCT1. The AUC of DLMs when forecasting rapidly worsening eyes. Model V3OC best forecasted rapid worsening with an AUC (95% confidence interval [CI]) of 0.87 (0.77-0.97). Remaining models in descending order of performance and their respective AUC (95% CI) were as follows: (1) model V3 (0.84 [0.74-0.95]), (2) model V2OC (0.81 [0.70-0.92]), (3) model V2 (0.81 [0.70-0.82]), (4) model VOC (0.77 [0.65-0.88]), (5) model VO (0.75 [0.64-0.88]), (6) model VC (0.75 [0.63-0.87]), and (7) model V (0.74 [0.62-0.86]). Deep learning models can forecast future rapid glaucoma worsening with modest to high performance when trained using data from early in the disease course. Including baseline data from multiple modalities and subsequent visits improves performance beyond using VF data alone. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Five-Year Visual Field Outcomes of the HORIZON Trial

To compare visual field (VF) progression between glaucoma patients receiving cataract surgery alone (CS) or with a Hydrus microstent (CS-HMS). Post hoc analysis of VF data from the HORIZON multicenter randomized controlled trial. A total of 556 patients with glaucoma and cataract were randomized 2:1 to either CS-HMS (369) or CS (187) and followed up for 5 years. VF was performed at 6 months and then every year after surgery. We analyzed data for all participants with at least 3 reliable VFs (false positives < 15%). Average between-group difference in rate of progression (RoP) was tested using a Bayesian mixed model and a 2-sided Bayesian P value <.05 (main outcome). A multivariable model measured the effect of intraocular pressure (IOP). A survival analysis compared the probability of global VF sensitivity dropping by predefined cutoffs (2.5, 3.5, 4.5, and 5.5 dB) from baseline. Data from 352 eyes in the CS-HMS arm and 165 in the CS arm were analyzed (2966 VFs). The mean RoP was -0.26 dB/y (95% credible interval -0.36, -0.16) for CS-HMS and -0.49 dB/y (95% credible interval -0.63, -0.34) for CS. This difference was significant (P=.0138). The difference in IOP only explained 17% of the effect (P < .0001). Five-year survival analysis showed an increased probability of VF worsening by 5.5 dB (P=.0170), indicating a greater proportion of fast progressors in the CS arm. CS-HMS has a significant effect on VF preservation in glaucoma patients compared with CS alone, reducing the proportion of fast progressors.

Survey on electronic visual field data transfer practices among Japan Glaucoma Society board members

BackgroundVisual field (VF) testing in combination with a specialized VF analysis software is critical for characterizing and monitoring visual loss in glaucoma. Although performing glaucoma progression analysis requires original VF data rather than printouts or image files, extent of VF data transfer between referring and referred ophthalmologists is unclear. Here, we surveyed glaucoma specialists who belong to the Japan Glaucoma Society (JGS).MethodsAn internet survey of daily practice patterns regarding electronic VF data transfer at the time of glaucoma referrals (referring/referred) was sent to all 50 JGS board members. The survey consisted with 11 questionnaires, and the response rate was 100%.ResultsThe respondents included 33 university hospital ophthalmologists (66%) (Q1), and those scattered throughout Japan (Q2). All respondents used Humphrey Visual Filed Analyzer (HFA) (Q3) and at least one of a VF progression analysis software (Q4). Ten respondents (20%) actively transferred electronic VF data, while 40 (80%) did not (Q5). The major reasons for not actively transferring data electronically were that there was no support for data transfer by neighboring (n = 26, 65%) and/or own (25, 63%) institutes (Q6). All 40 inactive respondents responded that electronic data transfer is ideal (Q7). All 10 active respondents transferred data using USB flash memory (Q8). Of the 10 active respondents, seven (70%) reported that the percentage of referral letters accompanying electronic VF data in a format that allows for progression analysis from the beginning was less than 25% (Q9). When the referral letters did not accompany the electronic VF data, four (40%) reported that they further requested the data transfer in < 25% of cases (Q10). When the 10 active respondents were requested to transfer data, six (60%) had experienced rejection due to various reasons (Q11).ConclusionAn internet survey showed that 80% of the JGS board members were not actively transferring VF data mainly because of the absence of a system in place at institutions for sending and receiving data, although they feel that the electronic VF data transfer is ideal. The results provide basic data for future discussions on the promotion of the VF data transfer.

Multimodal Deep Learning Model of Predicting Future Visual Field for Glaucoma Patients

Glaucoma is one of the most common reasons for blindness worldwide, especially in elderly people. Glaucoma can be monitored using visual field (VF) tests. Therefore, predicting the future VF to monitor progression of glaucoma is important. In this paper, we proposed a deep learning model to predict future VF based on previous VF and optical coherence tomography (OCT) images (including thickness map, vertical tomogram, and horizontal tomogram). The image data were analyzed using a ResNet-50 model. Image features and previous VFs were combined, and a long short-term memory (LSTM) network was used to predict future VF. A weighted method was used to detect noisy data. The proposed method was improved when applying weighted loss. The mean absolute error (MAE) was 3.31 ± 1.37, and the root mean square error (RMSE) was 4.58 ± 1.77. The model showed high performance when combining VF data and OCT image data. Furthermore, the model was useful for detecting and re-weighting noisy data.

Effect of trabeculectomy on the rate of progression of visual field damage

ObjectivesThis study quantifies the effect of trabeculectomy on the rate of progression (RoP) of visual field (VF) damage utilising pre- and post-operative visual function as the outcome instead of surrogate outcomes of success.MethodsClinical and VF data from 199 sequential patients who underwent trabeculectomy between 2015 and 2016 were extracted from the network of sites of Moorfields Eye Hospital NHS Foundation Trust. Of these, we analysed 80 eyes of 74 patients who met our inclusion criteria of at least three reliable VFs before and after surgery (false positive rate <15%). The change in mean RoP (dB/year) was tested using point-wise sensitivity values through a mixed effect model with random effects on both intercepts and slopes. A broken-stick regression of sensitivity over time, with a breakpoint at the day of surgery, modelled the individual change in RoP.ResultsWe analysed 10 [9,12] VFs per subject (Median [Interquartile Range]). At surgery, the age was 67 [57, 72] years, mean deviation was −10.84 [−14.7, −5.6] dB and the IOP was 18 [15, 20] mmHg. One year after surgery, the IOP was 10 [8,13] mmHg (p = 0.002). Mean RoP before surgery was −0.94 [−1.20, −0.69] dB/year (Mean [95% credible intervals]) and it was slowed down by 0.62 [0.26, 0.97] dB/year (p < 0.001) after surgery.ConclusionsTrabeculectomy leads to a significant reduction in the RoP of VF loss postoperatively.

Visual field‐based AI in glaucoma

AbstractArtificial intelligence (AI) has recently evoked tremendous excitement for research in the field of Ophthalmology and glaucoma. One of the areas where AI could come into play relates to the prediction of visual field progression. Currently linear trend‐based analysis is the most widely used method for assessing visual field progression. In the recent years a variety of deep learning based approaches have been proposed to better predict visual field progression. Techniques including archetypal analysis, Gaussian mixture model with expectation maximization and Visual Geometry Group standard deep Convolutional Neural Network (CNN) will be discussed. AI has also been used to study mapping of structure to function in glaucoma. This is of importance because it may be used to reduce the variability of visual field data. Hence it may increase our ability to study functional progression by combining perimetry and OCT data. Moreover, the structure/function relationship has been shown to be an independent risk factor for glaucoma progression.

Papillary and Peripapillary Hemorrhages in Eyes With Pathologic Myopia.

To determine the prevalence, characteristics, and causes of papillary and peripapillary hemorrhages (PPHs) in eyes with pathologic myopia (PM). PM patients were retrospectively studied between 2011 and 2018. Fundus images were used to diagnose and classify the PPHs. Fundus fluorescein angiographic (FFA) and optical coherence tomographic (OCT) images were used to determine the status of the retinal vessels and tissue at and around the PPH sites. Visual field data determined by Goldmann perimetry and Humphrey visual field analyzer were also analyzed. A total of 2171 PM patients (3774 eyes) were examined. Eighty-eight patients (97 eyes) had PPHs (mean age 66.8 ± 11.9 years; mean axial length 30.79 ± 2.17 mm) for a prevalence of 4.05%. Thirty (30.9%) eyes recurred. Among the 90 eyes with a single-site PPH, the most common type and location were the conus type (49 eyes, 54.4%) and the temporal side (66 eyes, 73.3%), respectively. Regression analysis showed that patchy atrophy reduced the risk of recurrences than diffuse atrophy (P < 0.05), whereas a longer axial length and potential glaucoma increased the risk (both P < 0.05). FFA and OCT showed that PPHs developed in the area of straightened retinal arterioles (24 eyes), at or beside the peak of a ridge (10 eyes), in an area of compressed retinal tissue (two eyes). PPHs are present in 4.05% of PM eyes, and they are most often located in the temporal peripapillary atrophic region of the retina. Axial elongation, mild myopic maculopathy, and potential glaucoma are risk factors for recurrences.

Two-Year Visual Field Outcomes of the Treatment of Advanced Glaucoma Study (TAGS)

to compare visual field (VF) progression between the 2 arms of the Treatment of Advanced Glaucoma Study (TAGS). Post hoc analysis of VF data from a 2-arm, multicenter, randomized controlled clinical trial. A total of 453 patients with newly diagnosed advanced open-angle glaucoma in at least 1 eye from 27 centers in the United Kingdom were randomized to either trabeculectomy (n=227) or medication in their index eye (n=226) and followed-up for 2 years with 2 24-2 VF tests at baseline, 4, 12, and 24 months. Data were analyzed for participants with a reliable VF (false positive rate < 15%) at baseline and at least 2 other time points. Average difference in rate of progression (RoP) was analyzed using a hierarchical Bayesian model. Time for each eye to progress from baseline beyond specific cut-offs (0.5, 1, 1.5, and 2 dB) was compared using survival analysis. This study analyzed 211 eyes in the trabeculectomy first arm and 203 eyes in the medication first arm. The average RoP (estimate [95% credible intervals]) was -0.59 [-0.88, -0.31] dB/year in the medication first arm and -0.40 [-0.67, -0.13] dB/year in the trabeculectomy first arm. The difference was not significant (Bayesian P-value=.353). More eyes progressed in the medication first arm, but this difference was not significant. There was no significant difference in the average RoP at 2 years.

Improving Visual Field Forecasting by Correcting for the Effects of Poor Visual Field Reliability.

PurposeThe purpose of this study was to accurately forecast future reliable visual field (VF) mean deviation (MD) values by correcting for poor reliability.MethodsFour linear regression techniques (standard, unfiltered, corrected, and weighted) were fit to VF data from 5939 eyes with a final reliable VF. For each eye, all VFs, except the final one, were used to fit the models. Then, the difference between the final VF MD value and each model's estimate for the final VF MD value was used to calculate model error. We aggregated the error for each model across all eyes to compare model performance. The results were further broken down into eye-level reliability subgroups to track performance as reliability levels fluctuate.ResultsThe standard method, used in the Humphrey Field Analyzer (HFA), was the worst performing model with an average residual that was 0.69 dB higher than the average from the unfiltered method, and 0.79 dB higher than that of the weighted and corrected methods. The weighted method was the best performing model, beating the standard model by as much as 1.75 dB in the 40% to 50% eye-level reliability subgroup. However, its average 95% prediction interval was relatively large at 7.67 dB.ConclusionsIncluding all VFs in the trend estimation has more predictive power for future reliable VFs than excluding unreliable VFs. Correcting for VF reliability further improves model accuracy.Translational RelevanceThe VF correction methods described in this paper may allow clinicians to catch VF worsening at an earlier stage.

The Usefulness of Assessing Glaucoma Progression With Postprocessed Visual Field Data

PurposeData 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).MethodThe 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 (V1–3) to predict the MD at V4. MD at V5 was predicted with data from V1–4 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.ResultsFor 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.ConclusionsWithout 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 RelevanceThe DSF model could be applied to postprocess existing visual field data, which could then be evaluated to identify patients at risk of progression.

The Open Perimetry Initiative: A framework for cross-platform development for the new generation of portable perimeters.

The Open Perimetry Initiative was formed in 2010 with the aim of reducing barriers to clinical research with visual fields and perimetry. Our two principal tools are the Open Perimetry Interface (OPI) and the visualFields package with analytical tools. Both are fully open source. The OPI package contains a growing number of drivers for commercially available perimeters, head-mounted devices, and virtual reality headsets. The visualFields package contains tools for the analysis and visualization of visual field data, including methods to compute deviation values and probability maps. We introduce a new frontend, the opiApp, that provides tools for customization for visual field testing and can be used as a frontend to run the OPI. The app can be used on the Octopus 900 (Haag-Streit), the Compass (iCare), the AP 7000 (Kowa), and the IMO (CREWT) perimeters, with permission from the device manufacturers. The app can also be used on Android phones with virtual reality headsets via a new driver interface, the PhoneHMD, implemented on the OPI. The use of the tools provided by the OPI library is showcased with a custom static automated perimetry test for the full visual field (up to 50 degrees nasally and 80 degrees temporally) developed with the OPI driver for the Octopus 900 and using visualFields for statistical analysis. With more than 60 citations in clinical and translational science journals, this initiative has contributed significantly to expand research in perimetry. The continued support of researchers, clinicians, and industry are key in transforming perimetry research into an open science.

Classification of Visual Field Abnormalities in Highly Myopic Eyes without Pathologic Change

To develop a classification system of visual field (VF) abnormalities in highly myopic eyes with and without glaucoma. Secondary analysis of VF data from a longitudinal cohort study. One thousand eight hundred ninety-three VF tests from 1302 eyes (825 individuals). All participants underwent VF testing (Humphrey 24-2 Swedish interactive threshold algorithm standard program; Carl Zeiss Meditec) and detailed ophthalmic examination. A comprehensive set of VF defect patterns was defined via observation of the 1893 VF reports, literature review, and consensus meetings. The classification system comprised 4 major types of VF patterns, including normal type, glaucoma-like defects (paracentral defect, nasal step, partial arcuate defect, arcuate defect), high myopia-related defects (enlarged blind spot, vertical step, partial peripheral rim, nonspecific defect), and combined defects (nasal step with enlarged blind spot). A subset (n= 1000) of the VFs was used to evaluate the interobserver and intraobserver agreement and weighted κ values of the classification system by 2 trained readers. The prevalence of various VF patterns and their associated factors were determined. The classification of VF in highly myopic eyes and its associated risk factors. We found that normal type, glaucoma-like defects, high myopia-related defects, and combined defects accounted for 74.1%, 10.8%, 15.0%, and 0.1% of all unique VF tests, respectively. The interobserver and intraobserver agreements were > 89%, and the corresponding κ values were 0.86 or more between readers. Both glaucoma-like and high myopia-related VF defects were associated with older age (odds ratios [ORs], 1.07 [95% confidence interval (CI), 1.04-1.10; P < 0.001] and 1.06 [95% CI, 1.04-1.10; P < 0.001]) and longer axial length (ORs, 1.65 [95% CI, 1.32-2.07; P < 0.001] and 1.37 [95% CI, 1.11-1.68; P= 0.003]). Longer axial length showed a stronger effect on the prevalence of glaucoma-like VF defects than on the prevalence of high myopia-related VF defects (P= 0.036). We propose a new and reproducible classification system of VF abnormalities for nonpathologic high myopia. Applying a comprehensive classification system will facilitate communication and comparison of findings among studies.

Contributed Session I: A Multi-Modal Visual Assessment System For Monitoring Spaceflight Associated Neuro-Ocular Syndrome (SANS) During Long Duration Spaceflight

Spaceflight Associated Neuro-Ocular Syndrome (SANS) refers to a unique collection of neuro-ophthalmic clinical and imaging findings that are observed in astronauts during long duration spaceflight. These findings include optic disc edema, posterior globe flattening, retinal nerve layer fiber thickening, optic nerve sheath distension, and hyperopic shift. SANS currently serves as a large barrier to deep space exploration; however, the exact pathophysiology is still being investigated. While in-flight imaging exists on the International Space Station, there are limitations to constant monitoring. To efficiently document the subtle changes that occur in SANS, NASA has funded the development of a head-mounted display, multi-modal visual assessment system; this novel device integrates visual acuity, visual field, contrast sensitivity, and metamorphopsia data (indirect indications) with known terrestrial neuro-ophthalmic imaging (direct indications) from astronauts and terrestrial analogs. By mapping these two indications, this visual assessment device will provide rapid in-flight monitoring of SANS symptoms and provide tools for the terrestrial development of countermeasures. We are in the process of beginning a pilot study of this multi-modal visual assessment device in healthy subjects to investigate the validity and reliability of this novel technology. This foundational study will compare the system with standard vision assessments utilized in clinical practice.

Real-World Analysis of the Aging Effects on Visual Field Reliability Indices in Humans

Relationships between age and visual field (VF) reliability indices were investigated using a large real-world dataset (42,421 VF data points from 11,525 eyes of 5930 subjects). All VFs tested and stored at Shimane University Hospital between 1988 and 2019 were exported. Correlations between age, mean deviation (MD), pattern standard deviation (PSD), and reliability indices including fixation losses (FLs), false negatives (FNs), and false positives (FPs) were analyzed. The mean ± standard deviation age was 65.0 ± 15.1 years; MD—−6.9 ± 8.1 decibels (dB); PSD—6.3 ± 4.6 dB; FL—8.6 ± 11.7%; FN—5.3 ± 8.3%; and FP—2.6 ± 5.0%. Univariate analyses showed strong associations between age and FNs (correlation coefficient, ρ = 0.20, p < 0.0001) and MD (ρ = −0.21, p < 0.0001). All FLs, FNs, and FPs were lowest during the third decade (20–29 years) of life. FLs were elevated consistently after that decade, and FNs were elevated sharply after the seventh decade. FPs were relatively stable after the fourth decade (30–39 years). Mixed-effect regression analyses in subjects 40 years and older showed that older age was associated with worse FLs (p < 0.0001) and FNs (p < 0.0001) but not FPs (p = 0.4126). Aging affects FLs and FNs with different modes but had minimal effects on FPs. Decreased VF sensitivity, deteriorated macular function, and technical difficulties with testing may be mechanisms of age-related changes in FLs and FNs.