The role of standard automated perimetry and newer functional methods for glaucoma diagnosis and follow-up

Perimetric visual field testing reveals deficits in contrast sensitivity in workers exposed to occupational levels of pesticides

PURPOSE

This case-control study investigated whether defects in visual field contrast sensitivity are associated with exposure to occupational levels of pesticides in agricultural workers.

METHODS

Twenty-seven individuals exposed to occupational levels of pesticides (exposed group) from 14 agricultural systems and 27 non-exposed individuals (non-exposed group) were measured using standard automated perimetry (30-2 full threshold). Visual sector analysis, targeting regions of the optic nerve head, was performed to examine for potential region-specific sensitivity differences. Participants also underwent comprehensive assessments, including general optometric examinations and demographic surveys. Symptoms and signs of neurotoxicity were assessed using the modified Q16 neurotoxic symptom questionnaire and biological testing for abnormal levels of Substance P in tears (an inflammatory marker associated with chemical exposure) and cholinesterase (which is inhibited by pesticides) in blood samples.

RESULTS

Signs of chemical exposure were found as indicated by significantly higher levels of neurotoxic symptoms, higher concentrations of Substance P and lower levels of cholinesterase in the exposed group. Visual field global indices showed differences in contrast sensitivity between the exposed and non-exposed groups, with the exposed group demonstrating significantly lower means and larger pattern-standard deviations. Notably, visual field sector analysis revealed comparatively lower contrast sensitivity at nasal locations surrounding the optic nerve head in the exposed group.

CONCLUSION

This study found deficits in visual field contrast sensitivity to be associated with pesticide exposure, and selective loss at nasal locations centred on the optic nerve head may indicate retinal toxicity. These findings suggest the utility of visual field assessment as a potential method to evaluate pesticide-related health implications. The results highlight the need for ongoing monitoring and protective measures for agricultural workers exposed to pesticides to prevent potential visual and neurological damage.

Glaucoma detection and staging from visual field images using machine learning techniques

PURPOSE

In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters.

METHODS

A total of 265 PD plots and 265 numerical datasets of Humphrey 24-2 VF images were collected from 119 normal and 146 glaucomatous eyes to train the DL models to classify the images into four groups: normal, early glaucoma, moderate glaucoma, and advanced glaucoma. The two popular pre-trained DL models: ResNet18 and VGG16, were used to train the PD images using five-fold cross-validation (CV) and observed the performance using balanced, pre-augmented data (n = 476 images), imbalanced original data (n = 265) and feature extraction. The trained images were further investigated using the Grad-CAM visualization technique. Moreover, four ML models were trained from the global indices: mean deviation (MD), pattern standard deviation (PSD) and visual field index (VFI), using five-fold CV to compare the classification performance with the DL model's result.

RESULTS

The DL model, ResNet18 trained from balanced, pre-augmented PD images, achieved high accuracy in classifying the groups with an overall F1-score: 96.8%, precision: 97.0%, recall: 96.9%, and specificity: 99.0%. The highest F1 score was 87.8% for ResNet18 with the original dataset and 88.7% for VGG16 with feature extraction. The DL models successfully localized the affected VF loss in PD plots. Among the ML models, the random forest (RF) classifier performed best with an F1 score of 96%.

CONCLUSION

The DL model trained from PD plots was promising in differentiating normal and glaucomatous groups and performed similarly to conventional global indices. Hence, the evidence-based DL model trained from PD images demonstrated that the DL model could stage glaucoma using only PD plots like Mills criteria. This automated DL model will assist clinicians in precision glaucoma detection and progression management during extensive glaucoma screening.

Reversing Aging and Improving Health Span in Glaucoma Patients: The Next Frontier?

How to cite this article: Dada T, Mahalingam K, Bhartiya S. Reversing Aging and Improving Health Span in Glaucoma Patients: The Next Frontier? J Curr Glaucoma Pract 2024;18(3):87-93.

Visual Field Tests: A Narrative Review of Different Perimetric Methods

Visual field (VF) testing dates back to fifth century B.C. It plays a pivotal role in the diagnosis, management, and prognosis of retinal and neurological diseases. This review summarizes each of the different VF tests and perimetric methods, including the advantages and disadvantages and adherence to the desired standard diagnostic criteria. The review targets beginners and eye care professionals and includes history and evolution, qualitative and quantitative tests, and subjective and objective perimetric methods. VF testing methods have evolved in terms of technique, precision, user-friendliness, and accuracy. Consequently, some earlier perimetric techniques, often still effective, are not used or have been forgotten. Newer technologies may not always be advantageous because of higher costs, and they may not achieve the desired sensitivity and specificity. VF testing is most often used in glaucoma and neurological diseases, but new objective methods that also measure response latencies are emerging for the management of retinal diseases. Given the varied perimetric methods available, clinicians are advised to select appropriate methods to suit their needs and target disease and to decide on applying simple vs. complex tests or between using subjective and objective methods. Newer, rapid, non-contact, objective methods may provide improved patient satisfaction and allow for the testing of children and the infirm.

Discriminating early-stage diabetic retinopathy with subjective and objective perimetry

Introduction

To prevent progression of early-stage diabetic retinopathy, we need functional tests that can distinguish multiple levels of neural damage before classical vasculopathy. To that end, we compared multifocal pupillographic objective perimetry (mfPOP), and two types of subjective automated perimetry (SAP), in persons with type 2 diabetes (PwT2D) with either no retinopathy (noDR) or mild to-moderate non-proliferative retinopathy (mmDR).

Methods

Both eyes were assessed by two mfPOP test methods that present stimuli within either the central ±15° (OFA15) or ±30° (OFA30), each producing per-region sensitivities and response delays. The SAP tests were 24-2 Short Wavelength Automated Perimetry and 24-2 Matrix perimetry.

Results

Five of eight mfPOP global indices were significantly different between noDR and mmDR eyes, but none of the equivalent measures differed for SAP. Per-region mfPOP identified significant hypersensitivity and longer delays in the peripheral visual field, verifying earlier findings. Diagnostic power for discrimination of noDR vs. mmDR, and normal controls vs. PwT2D, was much higher for mfPOP than SAP. The mfPOP per-region delays provided the best discrimination. The presence of localized rather than global changes in delay ruled out iris neuropathy as a major factor.

Discussion

mfPOP response delays may provide new surrogate endpoints for studies of interventions for early-stage diabetic eye damage.

Prospective Comparison of VisuALL Virtual Reality Perimetry and Humphrey Automated Perimetry in Glaucoma

Aim and background

Automated perimetry plays an important role in the diagnosis and monitoring of glaucoma patients. The purpose of this study is to prospectively determine parity between Humphrey visual field analyzer (HVFA) perimetry (the current gold standard) and the VisuALL virtual reality perimeter (VRP).

Materials and methods

In this prospective fully paired diagnostic accuracy study, patients with stable, long-term HVFA visual fields (horizontal dots for ≥4 consecutive visits on progression analysis) with preperimetric, mild, moderate, or severe visual field loss were familiarized with the VRP and then tested using its proprietary software. These results were used for point-by-point comparison with a contemporaneous HVFA test. This study was approved by the Institutional Review Board (IRB) of the University of the Incarnate Word, San Antonio, Texas, United States of America (IRB approval #20-06-002).

Results

The prospective study analyzed 43 eyes of 24 glaucoma patients. Spearman's correlation of mean deviation (MD) revealed a strong correlation between HVFA and VRP with rs(41) = 0.871, p < 0.001. The overall mean difference in locus-locus sensitivity between the devices was -0.4 ± 1.5 dB but varied for different visual field locations and glaucoma severity.

Conclusion

The parity between the VRP and HVFA was remarkably strong for mild and moderate glaucoma. Given its portability, ease of use, space efficiency, and low cost, the VRP presents a viable alternative.

Clinical significance

Automated perimetry, specifically the HVFA, has been the gold standard for visual field assessment since its introduction. The recent COVID-19 pandemic has illuminated the advantages of the VRP, allowing for safer visual assessment for both patient and clinician alike. Our study hopes to establish parity between these systems, allowing for the efficient integration of a novel head-mounted perimetry system that can safely diagnose and monitor glaucomatous progression in clinical practice.

Precis

Investigation of parity between Olleyes VisuALL virtual reality perimetry (VRP) and existing standard HVFA perimetry is essential to the diagnosis and management of glaucoma. Linear correlations between the two were established from 43 glaucomatous eyes. Parity was strong for mild and moderate glaucoma, presenting VRP as a viable alternative.

How to cite this article

Griffin JM, Slagle GT, Vu TA, et al. Prospective Comparison of VisuALL Virtual Reality Perimetry and Humphrey Automated Perimetry in Glaucoma. J Curr Glaucoma Pract 2024;18(1):4-9.

Terrestrial health applications of visual assessment technology and machine learning in spaceflight associated neuro-ocular syndrome

The neuro-ocular effects of long-duration spaceflight have been termed Spaceflight Associated Neuro-Ocular Syndrome (SANS) and are a potential challenge for future, human space exploration. The underlying pathogenesis of SANS remains ill-defined, but several emerging translational applications of terrestrial head-mounted, visual assessment technology and machine learning frameworks are being studied for potential use in SANS. To develop such technology requires close consideration of the spaceflight environment which is limited in medical resources and imaging modalities. This austere environment necessitates the utilization of low mass, low footprint technology to build a visual assessment system that is comprehensive, accessible, and efficient. In this paper, we discuss the unique considerations for developing this technology for SANS and translational applications on Earth. Several key limitations observed in the austere spaceflight environment share similarities to barriers to care for underserved areas on Earth. We discuss common terrestrial ophthalmic diseases and how machine learning and visual assessment technology for SANS can help increase screening for early intervention. The foundational developments with this novel system may help protect the visual health of both astronauts and individuals on Earth.

Diagnostic Performance of the PalmScan VF2000 Virtual Reality Visual Field Analyzer for Identification and Classification of Glaucoma

Purpose

To evaluate the diagnostic test properties of the Palm Scan VF2000Ⓡ Virtual Reality Visual Field Analyzer for diagnosis and classification of the severity of glaucoma.

Methods

This study was a prospective cross-sectional analysis of 166 eyes from 97 participants. All of them were examined by the HumphreyⓇ Field Analyzer (used as the gold standard) and the Palm Scan VF 2000Ⓡ Virtual Reality Visual Field Analyzer on the same day by the same examiner. We estimated the kappa statistic (including 95% confidence interval [CI]) as a measure of agreement between these two methods. The diagnostic test properties were assessed using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Results

The sensitivity, specificity, PPV, and NPV for the Virtual Reality Visual Field Analyzer for the classification of individuals as glaucoma/non-glaucoma was 100%. The general agreement for the classification of glaucoma between these two instruments was 0.63 (95% CI: 0.56-0.78). The agreement for mild glaucoma was 0.76 (95% CI: 0.61-0.92), for moderate glaucoma was 0.37 (0.14-0.60), and for severe glaucoma was 0.70 (95% CI: 0.55-0.85). About 28% of moderate glaucoma cases were misclassified as mild and 17% were misclassified as severe by the virtual reality visual field analyzer. Furthermore, 20% of severe cases were misclassified as moderate by this instrument.

Conclusion

The instrument is 100% sensitive and specific in detection of glaucoma. However, among patients with glaucoma, there is a relatively high proportion of misclassification of severity of glaucoma. Thus, although useful for screening of glaucoma, it cannot replace the HumphreyⓇ Field Analyzer for the clinical management in its current form.

A Comparative Study between Swedish Interactive Thresholding Algorithm Faster and Swedish Interactive Thresholding Algorithm Standard in Glaucoma Patients

Purpose:

To compare the results of the new strategy Swedish Interactive Thresholding Algorithm (SITA) Faster to the results of SITA Standard in patients with glaucoma.

Methods:

This was a cross-sectional study of 49 patients with glaucoma and previous experience with standard automated perimetry. Two consecutive tests were performed in random order, one with SITA Standard and another one with SITA Faster, in the studied eye of each patient. Comparisons were made for test time, mean deviation (MD), visual field index (VFI), and number of depressed points in pattern deviation map and total deviation map for every level of significance.

Results:

The average test time was 56% shorter with SITA Faster (P < 0.001). The intraclass correlation coefficient (ICC) for MD and VFI showed excellent agreement between both strategies, ICC = 0.98 (95% confidence interval [CI]: 0.96, 0.99) and ICC = 0.97 (95% CI: 0.95, 0.99), respectively. For the number of depressed points in total deviation map and pattern deviation map, ICC demonstrated good agreement with values between 0.8 and 0.95.

Conclusions:

Our study shows that SITA Faster is a shorter test with strong agreement with SITA Standard parameters. These results suggest that SITA Faster could replace SITA Standard for glaucoma diagnosis.

Virtual Reality-Based and Conventional Visual Field Examination Comparison in Healthy and Glaucoma Patients

Purpose

Clinically evaluate the noninferiority of a custom virtual reality (VR) perimetry system when compared to a clinically and routinely used perimeter on both healthy subjects and glaucoma patients.

Methods

We use a custom-designed VR perimetry system tailored for visual field testing. The system uses Oculus Quest VR headset (Facebook Technologies, LLC, Bern, Switzerland), that includes a clicker for participant response feedback. A prospective, single center, study was conducted at the Department of Ophthalmology of the Bern University Hospital (Bern, Switzerland) for 12 months. Of the 114 participants recruited 70 subjects (36 healthy and 34 glaucoma patients with early to moderate visual field loss) were included in the study. Participants underwent perimetry tests on an Octopus 900 (Haag-Streit, Köniz, Switzerland) as well as on the custom VR perimeter. In both cases, standard dynamic strategy (DS) was used in conjunction with the G testing pattern. Collected visual fields (VFs) from both devices were then analyzed and compared.

Results

High mean defect (MD) correlations between the two systems (Spearman, ρ ≥ 0.75) were obtained. The VR system was found to slightly underestimate VF defects in glaucoma subjects (1.4 dB). No significant bias was found with respect to eccentricity or subject age. On average, a similar number of stimuli presentations per VF was necessary when measuring glaucoma patients and healthy subjects.

Conclusions

This study demonstrates that a clinically used perimeter and the proposed VR perimetry system have comparable performances with respect to a number of perimetry parameters in healthy and glaucoma patients with early to moderate visual field loss.

Translational Relevance

This suggests that VR perimeters have the potential to assess VFs with high enough confidence, whereby alleviating challenges in current perimetry practices by providing a portable and more accessible visual field test.

Comparison of the Automated Pattern-Noise (PANO) Glaucoma Test with the HFA, an FDT Stimulus, and the Fundus Area Cup-to-disk Ratio

AIM AND OBJECTIVE

To compare the results of a new automated glaucoma test-Pattern-Noise (PANO)-to the Humphrey Visual Field Analyzer-II (HFA), the fundus area cup-to-disk ratio (CDR), and a frequency doubling technology (FDT) stimulus.

MATERIALS AND METHODS

This was a prospective study performed in the West-Region of Cameroon. Two hundred and nineteen eyes of 122 adult patients were included with a clinical suspicion of normal-tension or primary open-angle glaucoma and no other major ocular pathology. Eyes were examined with PANO, HFA (24-2 SITA standard), and FDT-stimulus in a randomized order followed by clinical assessment of the CDR.

RESULTS

Parametric correlation of the mean contrast threshold of PANO with the mean contrast threshold of FDT-stimulus, total deviation of HFA, and area CDR was 0.94, -0.85, and 0.62, respectively (p < 0.001 for all values). Spatial distribution of sensitivity thresholds is highly correlated (p < 0.001) at all points in the visual field between PANO and HFA. With cut-off values of 3 ± 1 dB for HFA mean deviation and 4 ± 1 for PANO mean contrast threshold and after eliminating borderline cases, PANO's sensitivity was 95% and specificity 60%. The mean patient age was 45.2 ± 15.8 years. Mean thresholds of PANO and FDT-stimulus decreased with increasing age. Mean examination time was 7.1 ± 1.8 minutes for PANO, 5.9 ± 1.3 minutes for HFA, and 4.7 ± 1.3 minutes for FDT-stimulus. The mean percentage of false-positives per examination was 4.95% for PANO, 4.62% (p = 0.025) for FDT-stimulus, and 2.10% for HFA.

CONCLUSION

The results showed that PANO was successful in suspecting the presence of glaucoma. Pattern-Noise examination led to findings that were significantly correlated to HFA, FDT stimulus, and area CDR. Some patterns of defect were also correlated. Furthermore, PANO showed a reasonable examination time and error rate.

CLINICAL SIGNIFICANCE

Affordable and robust visual field devices are lacking in large parts of the developing world. Comparing them to established methods is a prerequisite to their clinical use.

HOW TO CITE THIS ARTICLE

Hannen T, El-Khoury S, Patel R, et al. Comparison of the Automated Pattern-Noise (PANO) Glaucoma Test with the HFA, an FDT Stimulus, and the Fundus Area Cup-to-disk Ratio. J Curr Glaucoma Pract 2021;15(3):132-138.

Carotenoids in the Management of Glaucoma: A Systematic Review of the Evidence

Primary open-angle glaucoma (POAG) remains a leading cause of irreversible blindness globally. Recent evidence further substantiates sustained oxidative stress, and compromised antioxidant defenses are key drivers in the onset of glaucomatous neurodegeneration. Overwhelming oxidative injury is likely attributed to compounding mitochondrial dysfunction that worsens with age-related processes, causing aberrant formation of free radical species. Thus, a compromised systemic antioxidant capacity exacerbates further oxidative insult in glaucoma, leading to apoptosis, neuroinflammation, and subsequent tissue injury. The purpose of this systematic review is to investigate the neuroprotective benefits of the macular carotenoids lutein, zeaxanthin, and meso-zeaxanthin on glaucomatous neurodegeneration for the purpose of adjunctive nutraceutical treatment in glaucoma. A comprehensive literature search was conducted in three databases (PubMed, Cochrane Library, and Web of Science) and 20 records were identified for screening. Lutein demonstrated enhanced neuroprotection on retinal ganglion cell survival and preserved synaptic activity. In clinical studies, a protective trend was seen with greater dietary consumption of carotenoids and risk of glaucoma, while greater carotenoid levels in macular pigment were largely associated with improved visual performance in glaucomatous eyes. The data suggest that carotenoid vitamin therapy exerts synergic neuroprotective benefits and has the capacity to serve adjunctive therapy in the management of glaucoma.

Aqueous Humor Proteomic Alterations Associated with Visual Field Index Parameters in Glaucoma Patients: A Pilot Study

PURPOSE

The purpose of this study was to discover the aqueous humor proteomic changes associated with visual field indices in glaucoma patients.

METHODS

Aqueous humor samples were analyzed using Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS). The visual fields were analyzed with the Humphrey Visual Field analyzer. Statistical analyses were performed to discover the relationship between the aqueous humor proteins and visual field parameters including Pattern Standard Deviation (PSD), Visual Field Index (VFI), Mean Deviation (MD) and Glaucoma Hemifield Test (GHT).

RESULTS

In total, 222 proteins were identified in 49 aqueous humor samples. A total of 11, 9, 7, and 6 proteins were significantly correlated with PSD, VFI, MD, and GHT respectively. These proteins include apolipoprotein D, members of complement pathway (C1S, C4A, C4B, C8B, and CD14), and immunoglobulin family (IKHV3-9, IGKV2-28).

CONCLUSION

Several proteins involved in immune responses (immunoglobulins and complement factors) and neurodegeneration (apolipoprotein D) were identified to be associated with abnormal visual field parameters. These findings provide targets for future studies investigating precise molecular mechanisms and new therapies for glaucomatous optic neuropathy.

How Do Patients Feel About Visual Field Testing? Analysis of Subjective Perception of Standard Automated Perimetry

PURPOSE

A high rate of unreliability is an issue in visual field (VF) testing, especially in elderly patients, and warrants patient education. We assessed whether subjective perception of the visual field test (VFT) is a good predictor of its reliability in different age groups and examined age differences in patients' awareness of VF damage.

METHODS

This cross-sectional study investigated 107 VFT results of 54 patients with glaucoma or ocular hypertension. Subjective perceptions were compared to reliability indices for cooperation analysis and to mean deviation results for VF damage analysis, and an age-segregated sub-analysis was performed.

RESULTS

Kappa coefficients showed poor agreement between subjective and objective parameters. Nevertheless, there were age differences. Younger patients had a higher positive predictive value and sensitivity in cooperation analysis and a higher negative predictive value in VF damage analysis.

CONCLUSIONS

Patients' perception of cooperation in VFT is a poor predictor of its reliability. Although young cooperative patients may be aware of their good cooperation, even the youngest are unaware of their poor performance. This emphasizes the importance of giving proper directions to all patients during VFT to obtain better reliability indices. Younger, healthy patients are more aware of their health status, than those with a damaged VF, regardless of age. Therefore, illness education is crucial in all glaucoma patients.

Applications of deep learning in detection of glaucoma: A systematic review

Glaucoma is the leading cause of irreversible blindness and disability worldwide. Nevertheless, the majority of patients do not know they have the disease and detection of glaucoma progression using standard technology remains a challenge in clinical practice. Artificial intelligence (AI) is an expanding field that offers the potential to improve diagnosis and screening for glaucoma with minimal reliance on human input. Deep learning (DL) algorithms have risen to the forefront of AI by providing nearly human-level performance, at times exceeding the performance of humans for detection of glaucoma on structural and functional tests. A succinct summary of present studies and challenges to be addressed in this field is needed. Following PRISMA guidelines, we conducted a systematic review of studies that applied DL methods for detection of glaucoma using color fundus photographs, optical coherence tomography (OCT), or standard automated perimetry (SAP). In this review article we describe recent advances in DL as applied to the diagnosis of glaucoma and glaucoma progression for application in screening and clinical settings, as well as the challenges that remain when applying this novel technique in glaucoma.

Factors Associated with Midterm Visual Field Variability in Patients with Stable Glaucoma

Purpose

To evaluate factors associated with midterm visual field (VF) variability in stable glaucoma patients in Brazil.

Methods

This retrospective observational study included 59 eyes of 39 stable glaucoma patients. Baseline data assessed were age, gender, educational level, intraocular pressure (IOP), central corneal thickness, best-corrected visual acuity, spherical equivalent, number of hypotensive eye drops, type of glaucoma, number of VFs performed, follow-up in years, lens status, visual field index (VFI) values from the last 5 VF (standard automated perimetry (SAP)) tests, the presence or absence of central scotoma in the VF test, and the level of glaucomatous damage according to the VF mean deviation (MD) index of the last VFs. The 5 latest VFI scores were used to calculate the mean, the standard deviation (SD), and the coefficient of variation (CV). We divided the eyes into 2 groups, being group 1 comprised by the 29 eyes presenting the lowest CV values and group 2 comprised by the 30 eyes presenting the highest CV values. GEE models were used to compare the CV and demographic and clinical parameters of all participants.

Results

Mean age of all subjects was 65.8 ± 10.1 years. 54.0% were women. Average SAP MD values for groups 1 and 2 were −2.8 ± 3.1 dB and −6.2 ± 4.1 dB, respectively (P=0.006). Average SAP VFI values for groups 1 and 2 were 95.6 ± 5.9% and 85.9 ± 11.3%, respectively (P=0.002). There was a statistically significant association between CV and SAP MD values (P=0.006). A worse SAP MD and VFI were associated with a higher CV. In addition, even adjusting for potential confounding factors (age and level of education), the association between CV and the SAP MD and between CV and VFI remained significant (P ≤ 0.010).

Conclusion

Glaucomatous patients with worse VF sensitivity scores (both MD and VFI indices) present higher VF test variability.

Evidence for the Detection of Subclinical Retinal Involvement in Systemic Lupus Erythematosus and Sjögren Syndrome: A Potential Association with Therapies

BACKGROUND

Retinal involvement in systemic lupus erythematosus (SLE) and Sjögren syndrome (SS) may be subclinical and thus underdiagnosed.

OBJECTIVES

We aimed at evaluating morphological and functional visual abnormalities in a cohort of SLE and SS patients in the absence of an overt clinical visual impairment. We also investigated potential associations between retinal disorders and disease activity, organ involvement, and treatment with steroid and/or hydroxychloroquine.

METHODS

The study comprised 42 SLE and 36 primary SS patients and 76 healthy controls (HC). Ophthalmological examination, standard automated perimetry, spectral-domain optical coherence tomography, and fundus perimetry were performed.

RESULTS

Retinal thickness of the posterior pole was not different between SLE and HC groups, but it was reduced in the SS group compared with both the HC and the SLE group. In SLE and SS patients, mean defect and pattern standard deviation by standard automated perimetry were higher than in HC. Visual field index values were lower in both SLE and SS patients than in HC. SLE patients with nephritis displayed increased mean defect and pattern standard deviation and reduced visual field index values compared to patients without nephritis. In SLE and SS patients, fundus perimetry differential sensitivity was reduced, and mean defect values were higher than in HC. Disturbances in fundus perimetry in the SLE group were more prevalent in steroid-naïve patients and in SS patients who received a cumulative hydroxychloroquine dose > 1,000 g.

CONCLUSIONS

Functional eye impairment was demonstrated in SLE patients, possibly associated with kidney involvement. In SLE, corticosteroids might exert a protective role. Morphological alterations and functional impairment were detected in SS patients, which may be linked to hydroxychloroquine toxicity.

Non-parametric tests in detecting glaucoma progression

Automated perimetry still represents the gold standard in long term glaucoma monitoring. On a daily practice basis, glaucoma progression analysis could be difficult due to the long time needed to detect, confirm, and quantify the progression rate. Moreover, "trend" and "event" analysis require a good theoretical basis to perform and interpret. Aim of study was to present an alternative method to conventional Glaucoma Progression Analysis (Humphrey Visual Field Analyzer, Carl Zeiss® Inc.) applied for the early detection of glaucoma progression. Such an "event" analysis orients the clinician in a fast manner on the progression profile in glaucoma patients and might adapt the follow up visits accordingly. Method and material: 41 eyes from 41 patients with open angle glaucoma were studied in a longitudinal manner, over a 24 months' time interval from diagnosis. Results: in the GPA analysis, a positive "event" (progression) was detected in 11/ 41 eyes (26.82%). Non-parametric analysis confirmed progression in all GPA cases, and additionally found 8 more eyes with positive progression (46.34% studied eyes). Mc Nemar concordance analysis between tests was good and relevant (kappa index k=0.596, p=0.000), with positive correlation (r=0.652, p=0.008). In conclusion, NPA tends to overestimate the number of progression cases in a cohort, but it can easily orient the clinician on the profile of the followed patients. In the first years, the GPA analysis can be highly inaccurate, but there is a great need to detect which patients are at significant risk for vision loss (fast progressors). Yet, combining the two methods of detection of glaucoma progression, the practitioners might direct their selected interest and attention towards observing a larger than expected number of patients who are at risk for vision loss over time due to glaucoma, but not necessarily in a fast manner.

The SCHEIE Visual Field Grading System

OBJECTIVE

No method of grading visual field (VF) defects has been widely accepted throughout the glaucoma community. The SCHEIE (Systematic Classification of Humphrey visual fields-Easy Interpretation and Evaluation) grading system for glaucomatous visual fields was created to convey qualitative and quantitative information regarding visual field defects in an objective, reproducible, and easily applicable manner for research purposes.

METHODS

The SCHEIE grading system is composed of a qualitative and quantitative score. The qualitative score consists of designation in one or more of the following categories: normal, central scotoma, paracentral scotoma, paracentral crescent, temporal quadrant, nasal quadrant, peripheral arcuate defect, expansive arcuate, or altitudinal defect. The quantitative component incorporates the Humphrey visual field index (VFI), location of visual defects for superior and inferior hemifields, and blind spot involvement. Accuracy and speed at grading using the qualitative and quantitative components was calculated for non-physician graders.

RESULTS

Graders had a median accuracy of 96.67% for their qualitative scores and a median accuracy of 98.75% for their quantitative scores. Graders took a mean of 56 seconds per visual field to assign a qualitative score and 20 seconds per visual field to assign a quantitative score.

CONCLUSION

The SCHEIE grading system is a reproducible tool that combines qualitative and quantitative measurements to grade glaucomatous visual field defects. The system aims to standardize clinical staging and to make specific visual field defects more easily identifiable. Specific patterns of visual field loss may also be associated with genetic variants in future genetic analysis.

Rituximab as a monotherapy or in combination therapy for the treatment of non-paraneoplastic autoimmune retinopathy

PURPOSE

To examine the efficacy of rituximab as a monotherapy or in combination therapy for the treatment of patients with non-paraneoplastic autoimmune retinopathy.

METHODS

Twelve eyes of six patients with non-paraneoplastic autoimmune retinopathy who were treated with rituximab and had at least 6 months of follow-up were included. Demographic data, clinical data, visual field parameters, electroretinography parameters, and anti-retinal and anti-optic nerve autoantibody bands were collected from the Massachusetts Eye Research and Surgery Institution database between September 2010 and January 2015. Changes in visual acuity, visual field parameters, electroretinography parameters, and anti-retinal and anti-optic nerve autoantibody bands from the initial visit to the most recent visit were examined.

RESULTS

From the initial visit to the last visit, visual acuity was stable in eight (66.7%) eyes. Visual field was stable in six (50%) eyes and improved in two (16.7%) eyes. Electroretinography was stable or improved in eight (66.7%) eyes. The average number of anti-retinal and anti-optic nerve antibody bands was reduced.

CONCLUSION

Stabilization and/or improvement of visual acuity, visual field parameters, and electroretinography parameters were observed in a high number of patients (75%) on rituximab, as a monotherapy (one patient) or in combination therapy.

The Use of Spectral-Domain Optical Coherence Tomography to Detect Glaucoma Progression

Detection of progression and measurement of rates of change is at the core of glaucoma management, and the use of Spectral Domain Optical Coherence Tomography (SD-OCT) has significantly improved our ability to evaluate change in the disease. In this review, we critically assess the existing literature on the use of SD-OCT for detecting glaucoma progression and estimating rates of change. We discuss aspects related to the reproducibility of measurements, their accuracy to detect longitudinal change over time, and the effect of aging on the ability to detect progression. In addition, we discuss recent studies evaluating the use of combined structure and function approaches to improve detection of glaucoma progression.

Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points

Machine learning classifiers were employed to detect glaucomatous progression using longitudinal series of structural data extracted from retinal nerve fiber layer thickness measurements and visual functional data recorded from standard automated perimetry tests. Using the collected data, a longitudinal feature vector was created for each patient's eye by computing the norm 1 difference vector of the data at the baseline and at each follow-up visit. The longitudinal features from each patient's eye were then fed to the machine learning classifier to classify each eye as stable or progressed over time. This study was performed using several machine learning classifiers including Bayesian, Lazy, Meta, and Tree, composing different families. Combinations of structural and functional features were selected and ranked to determine the relative effectiveness of each feature. Finally, the outcomes of the classifiers were assessed by several performance metrics and the effectiveness of structural and functional features were analyzed.

Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements

A hierarchical approach to learn from visual field data was adopted to identify glaucomatous visual field defect patterns and to detect glaucomatous progression. The analysis pipeline included three stages, namely, clustering, glaucoma boundary limit detection, and glaucoma progression detection testing. First, cross-sectional visual field tests collected from each subject were clustered using a mixture of Gaussians and model parameters were estimated using expectation maximization. The visual field clusters were further estimated to recognize glaucomatous visual field defect patterns by decomposing each cluster into several axes. The glaucoma visual field defect patterns along each axis then were identified. To derive a definition of progression, the longitudinal visual fields of stable glaucoma eyes on the abnormal cluster axes were projected and the slope was approximated using linear regression (LR) to determine the confidence limit of each axis. For glaucoma progression detection, the longitudinal visual fields of each eye on the abnormal cluster axes were projected and the slope was approximated by LR. Progression was assigned if the progression rate was greater than the boundary limit of the stable eyes; otherwise, stability was assumed. The proposed method was compared to a recently developed progression detection method and to clinically available glaucoma progression detection software. The clinical accuracy of the proposed pipeline was as good as or better than the currently available methods.

Recognizing patterns of visual field loss using unsupervised machine learning

Glaucoma is a potentially blinding optic neuropathy that results in a decrease in visual sensitivity. Visual field abnormalities (decreased visual sensitivity on psychophysical tests) are the primary means of glaucoma diagnosis. One form of visual field testing is Frequency Doubling Technology (FDT) that tests sensitivity at 52 points within the visual field. Like other psychophysical tests used in clinical practice, FDT results yield specific patterns of defect indicative of the disease. We used Gaussian Mixture Model with Expectation Maximization (GEM), (EM is used to estimate the model parameters) to automatically separate FDT data into clusters of normal and abnormal eyes. Principal component analysis (PCA) was used to decompose each cluster into different axes (patterns). FDT measurements were obtained from 1,190 eyes with normal FDT results and 786 eyes with abnormal (i.e., glaucomatous) FDT results, recruited from a university-based, longitudinal, multi-center, clinical study on glaucoma. The GEM input was the 52-point FDT threshold sensitivities for all eyes. The optimal GEM model separated the FDT fields into 3 clusters. Cluster 1 contained 94% normal fields (94% specificity) and clusters 2 and 3 combined, contained 77% abnormal fields (77% sensitivity). For clusters 1, 2 and 3 the optimal number of PCA-identified axes were 2, 2 and 5, respectively. GEM with PCA successfully separated FDT fields from healthy and glaucoma eyes and identified familiar glaucomatous patterns of loss.