Standard automated perimetry using size III and size V stimuli in advanced stage glaucoma: an observational cross-sectional comparative study

ObjectivesIn this study, we sought to evaluate the extent of further visual field that could be assessed when using stimulus size V in standard automated perimetry compared with size III in advanced stage glaucoma and whether cut-off values could be determined for when to switch from size III to size V.DesignProspective cross-sectional study.SettingSingle-centre outpatient eye clinic in India (New Delhi).ParticipantsAdvanced stage glaucoma defined as stages 3–4.InterventionCentral static perimetry with Octopus 900 G programme (size III stimulus dynamic strategy) and low vision central programme (size V stimulus dynamic strategy).Primary and secondary outcome measuresVisual field assessment for right and left eyes with both sizes III and V were undertaken within one clinic visit.ResultsWe recruited 126 patients (170 eyes). Mean patient age at assessment was 55.86 years (SD 15.15). Means (SD) for size III versus size V, respectively, were 6.94 dB (5.58) and 12.98 dB (7.77) for mean sensitivity, 20.02 dB (5.67) and 19.22 dB (7.74) for mean deviation, 5.89 dB (2.29) and 7.69 dB (2.78) for standard loss variance and 3.32 min (1.07) and 6.40 min (1.43) for test duration. All except mean deviation were significantly different between size III and V tests.ConclusionUseful visual field information was obtained with size V stimuli which allowed continued monitoring of these patients that was not possible with size III. Increased test duration, standard loss variance and mean sensitivity were found with size V, as expected, given that more visual responses were obtained with the increased target size. A switch from size III to V may be considered when mean sensitivity reaches 10 dB and/or mean deviation reaches 18 dB.

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Glaucoma home-monitoring using a tablet-based visual field test (Eyecatcher): An assessment of accuracy and adherence over six months

10.1101/2020.05.28.20115725 ◽

2020 ◽

Author(s):

Pete R. Jones ◽

Peter Campbell ◽

Tamsin Callaghan ◽

Lee Jones ◽

Daniel S. Asfaw ◽

...

Keyword(s):

Visual Field ◽

Home Monitoring ◽

Absolute Error ◽

Machine Learning Techniques ◽

Mean Deviation ◽

Test Duration ◽

Automated Perimetry ◽

Standard Automated Perimetry ◽

Measurement Variability ◽

Prospective Longitudinal

AbstractPurposeTo assess accuracy and adherence of visual field (VF) home-monitoring in a pilot sample of glaucoma patients.DesignProspective longitudinal observation.MethodsTwenty adults (median 71 years) with an established diagnosis of glaucoma were issued a tablet-perimeter (Eyecatcher), and were asked to perform one VF home-assessment per eye, per month, for 6 months (12 tests total). Before and after home-monitoring, two VF assessments were performed in-clinic using Standard Automated Perimetry (SAP; 4 tests total, per eye).ResultsAll 20 participants could perform monthly home-monitoring, though one participant stopped after 4 months (Adherence: 98%). There was good concordance between VFs measured at home and in the clinic (r = 0.94, P < 0.001). In 21 of 236 tests (9%) Mean Deviation deviated by more than ±3dB from the median. Many of these anomalous tests could be identified by applying machine learning techniques to recordings from the tablets’ front-facing camera (Area Under the ROC Curve = 0.78). Adding home-monitoring data to 2 SAP tests made 6 months apart reduced measurement error (between-test measurement variability) in 97% of eyes, with mean absolute error more than halving in 90% of eyes. Median test duration was 4.5mins (Quartiles: 3.9−5.2mins). Substantial variations in ambient illumination had no observable effect on VF measurements (r = 0.07, P = 0.320).ConclusionsHome-monitoring of VFs is viable for some patients, and may provide clinically useful data.

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Association between visual field damage and corneal structural parameters

Scientific Reports ◽

10.1038/s41598-021-90298-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Alexandru Lavric ◽

Valentin Popa ◽

Hidenori Takahashi ◽

Rossen M. Hazarbassanov ◽

Siamak Yousefi

Keyword(s):

Machine Learning ◽

Visual Field ◽

Structural Parameters ◽

Corneal Thickness ◽

Anterior Segment ◽

Mean Deviation ◽

Automated Perimetry ◽

Standard Automated Perimetry ◽

Visual Field Damage ◽

Corneal Shape

AbstractThe main goal of this study is to identify the association between corneal shape, elevation, and thickness parameters and visual field damage using machine learning. A total of 676 eyes from 568 patients from the Jichi Medical University in Japan were included in this study. Corneal topography, pachymetry, and elevation images were obtained using anterior segment optical coherence tomography (OCT) and visual field tests were collected using standard automated perimetry with 24-2 Swedish Interactive Threshold Algorithm. The association between corneal structural parameters and visual field damage was investigated using machine learning and evaluated through tenfold cross-validation of the area under the receiver operating characteristic curves (AUC). The average mean deviation was − 8.0 dB and the average central corneal thickness (CCT) was 513.1 µm. Using ensemble machine learning bagged trees classifiers, we detected visual field abnormality from corneal parameters with an AUC of 0.83. Using a tree-based machine learning classifier, we detected four visual field severity levels from corneal parameters with an AUC of 0.74. Although CCT and corneal hysteresis have long been accepted as predictors of glaucoma development and future visual field loss, corneal shape and elevation parameters may also predict glaucoma-induced visual functional loss.

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Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma Using a Deep Variational Autoencoder

Scientific Reports ◽

10.1038/s41598-019-54653-6 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 5

Author(s):

Samuel I. Berchuck ◽

Sayan Mukherjee ◽

Felipe A. Medeiros

Keyword(s):

Visual Field ◽

Learning Algorithm ◽

Visual Fields ◽

Visual Field Loss ◽

Absolute Error ◽

Rate Of Change ◽

Mean Deviation ◽

Automated Perimetry ◽

Standard Automated Perimetry ◽

Deep Learning Algorithm

AbstractIn this manuscript we develop a deep learning algorithm to improve estimation of rates of progression and prediction of future patterns of visual field loss in glaucoma. A generalized variational auto-encoder (VAE) was trained to learn a low-dimensional representation of standard automated perimetry (SAP) visual fields using 29,161 fields from 3,832 patients. The VAE was trained on a 90% sample of the data, with randomization at the patient level. Using the remaining 10%, rates of progression and predictions were generated, with comparisons to SAP mean deviation (MD) rates and point-wise (PW) regression predictions, respectively. The longitudinal rate of change through the VAE latent space (e.g., with eight dimensions) detected a significantly higher proportion of progression than MD at two (25% vs. 9%) and four (35% vs 15%) years from baseline. Early on, VAE improved prediction over PW, with significantly smaller mean absolute error in predicting the 4th, 6th and 8th visits from the first three (e.g., visit eight: VAE8: 5.14 dB vs. PW: 8.07 dB; P < 0.001). A deep VAE can be used for assessing both rates and trajectories of progression in glaucoma, with the additional benefit of being a generative technique capable of predicting future patterns of visual field damage.

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Global assessment of arteriolar, venular and capillary changes in normal tension glaucoma

Scientific Reports ◽

10.1038/s41598-020-75784-1 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Timothy P. H. Lin ◽

Yu Meng Wang ◽

Kevin Ho ◽

Cherie Y. K. Wong ◽

Poemen P. Chan ◽

...

Keyword(s):

Normal Tension Glaucoma ◽

Mean Deviation ◽

Computer Assisted ◽

Automated Perimetry ◽

Standard Automated Perimetry ◽

Cross Sectional ◽

Retinal Microvasculature ◽

Branching Angle ◽

Retinal Photographs ◽

Retinal Arteriolar

Abstract Microcirculatory insufficiency has been hypothesized in glaucoma pathogenesis. There is a scarcity of data to comprehensively examine the changes in retinal microvasculature and its role in normal tension glaucoma (NTG). We conducted a cross-sectional case–control study and included 168 eyes from 100 NTG patients and 68 healthy subjects. Quantitative retinal arteriolar and venular metrics were measured from retinal photographs using a computer-assisted program. Radial peripapillary capillary network was imaged with OCT-A and quantitative capillary metrics (circumpapillary vessel density (cpVD) and circumpapillary fractal dimension (cpFD)) were measured with a customized MATLAB program. We found that NTG was associated with decreased arteriolar and venular tortuosity, arteriolar branching angle, cpVD and cpFD. Decreased venular caliber, arteriolar and venular branching angles, cpVD and cpFD were associated with thinner average RNFL thickness. Decreased arteriolar and venular branching angles, cpVD and cpFD were also associated with worse standard automated perimetry measurements (mean deviation and visual field index). Compared with retinal arteriolar and venular metrics, regression models based on OCT-A capillary metrics consistently showed stronger associations with NTG and structural and functional measurements in NTG. We concluded that NTG eyes showed generalized microvascular attenuations, in which OCT-A capillary metrics attenuations were more prominent and strongly associated with NTG.

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Comparison of vascular–function and structure–function correlations in glaucomatous eyes with high myopia

British Journal of Ophthalmology ◽

10.1136/bjophthalmol-2019-314430 ◽

2019 ◽

Vol 104 (6) ◽

pp. 807-812

Author(s):

Seung Hyen Lee ◽

Eun Ji Lee ◽

Tae-Woo Kim

Keyword(s):

Visual Field ◽

Vascular Function ◽

High Myopia ◽

Open Angle Glaucoma ◽

Retinal Vessel ◽

Retinal Nerve Fibre Layer ◽

Automated Perimetry ◽

Standard Automated Perimetry ◽

Cross Sectional ◽

Visual Field Damage

Background/aimsTo determine the usefulness of peripapillary retinal vessel density (VD) measured using optical coherence tomography (OCT) angiography (OCTA) in the evaluation of glaucomatous visual field damage in highly myopic eyes with primary open-angle glaucoma (POAG).MethodsThis cross-sectional observational study enrolled a total of 124 myopic POAG eyes consisting of 40 eyes showing a segmentation error (SE) in OCT scans and 84 eyes without an SE. The peripapillary retinal VD, circumpapillary retinal nerve fibre layer thickness (RNFLT) and visual field sensitivity loss (VFSL) were assessed using OCTA, spectral-domain OCT and standard automated perimetry, respectively. The topographical correlations between the VD and VFSL, and between the RNFLT and VFSL were determined in subgroups divided according to the presence of an SE.ResultsThe peripapillary retinal VD showed significant topographical correlation with VFSL both in the highly myopic POAG eyes without an SE globally (R=0.527, p<0.001), and in temporal (R=0.593), temporal-superior (R=0.543), nasal-inferior (R=0.422) and temporal-inferior sectors (R=0.600, all p<0.001), and in those with an SE globally (R=0.343, p=0.030), and in temporal (R=0.494, p=0.001), temporal-superior (R=0.598, p<0.001), and temporal-inferior sectors (R=0.424, p=0.006). The correlation with VFSL did not differ between the VD and RNFLT in the eyes without an SEConclusionPeripapillary VD as measured with OCTA showed a topographical correlation with VFSL in highly myopic POAG eyes regardless of the presence of an OCT SE OCTA may be a useful adjunct for evaluating glaucomatous visual field damage in high myopia, where the OCT results are frequently confounding.

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Influence of automated visual field testing on intraocular pressure

10.21203/rs.3.rs-21287/v1 ◽

2020 ◽

Author(s):

Samuel Bertaud ◽

Elisabeth Skarbek Borowski ◽

Rachid Abbas ◽

Christophe Baudouin ◽

Antoine Labbé

Keyword(s):

Intraocular Pressure ◽

Visual Field ◽

Open Angle Glaucoma ◽

Corneal Thickness ◽

Angle Closure ◽

Mean Deviation ◽

Test Duration ◽

Automated Perimetry ◽

Significant Difference ◽

The Impact

Abstract Background To evaluate the influence of automated visual field (VF) testing on intraocular pressure (IOP) in patients with ocular hypertension (OHT) or glaucoma. Methods We conducted a prospective observational study from October 2015 to July 2016 at Quinze-Vingts National Ophthalmology Hospital in Paris. Ninety-five right eyes of 95 patients followed for glaucoma or OHT with reliable standard automated perimetry (SAP) were included. IOP was measured three times using a Nidek NT-510 non-contact tonometer within a maximum of 5 minutes before and after VF testing. Subanalyses using logistic regression analysis were performed to evaluate the impact of gender, age, central corneal thickness (CCT), mean deviation (MD) of the VF, VF test duration and filtration surgery on IOP fluctuations. Results There was no significant change in IOP after VF testing, with IOP’s 15.14 ± 4.00 mmHg before and 14.98 ± 3.33 mmHg after the VF (P = 0.4). The average change in IOP was 0.15 ± 1.82 mmHg. Using multivariate analysis, no effect of the VF test on IOP was found (global model fit R²=0.12), whether based on duration of the VF test (P = 0.18) or the MD (P = 0.7) after adjustment for age, gender, CCT and history of glaucoma surgery. Similarly, there was no significant difference within different types of glaucoma, including open-angle glaucoma (p = 0.36), chronic angle closure glaucoma (P = 0.85) and OHT (P = 0.42). The subgroup of patients with an IOP elevation > 2 mmHg had a significantly higher SAP test duration (P = 0.002). Conclusion VF testing by SAP does not influence IOP as measured with a non-contact tonometer.

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Influence of implantations of extended depth-of-focus on standard automated perimetry

Scientific Reports ◽

10.1038/s41598-020-77214-8 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Jinhee Lee ◽

Yosai Mori ◽

Ryohei Nejima ◽

Keiichiro Minami ◽

Kazunori Miyata

Keyword(s):

Visual Field ◽

Cataract Surgery ◽

Mean Deviation ◽

Automated Perimetry ◽

Depth Of Focus ◽

Standard Automated Perimetry ◽

Multifocal Iol ◽

Reliability Indices ◽

Extended Depth Of Focus ◽

Foveal Sensitivity

AbstractThis prospective study aimed to investigate the influence of an extended depth-of-focus intraocular lens (EDOF IOL) on standard automated perimetry. Ninety eyes of 90 patients who had undergone cataract surgery from February 2018 to December 2018 were included. No patients had any diseases that might affect the visual field. ZMB00 (+ 4.00 D add), ZXR00V (+ 1.75 D add), and ZCB00V (Johnson & Johnson Surgical Vision, Santa Ana, CA, USA) were used as multifocal, EDOF, and monofocal IOLs, respectively. Humphrey Visual Field 10–2 testing was performed 2–3 months after cataract surgery, acceptable reliability indices were measured, and mean deviation (MD), pattern standard deviation (PSD), foveal sensitivity and mean sensitivity (MS) were compared. Seventy-one eyes (ZXR00V: 24 eyes, ZMB00: 25 eyes, ZCB00V: 22 eyes) were used for the analyses. The MD and MS of the EDOF and monofocal groups were significantly higher than those of the multifocal group (P < 0.0051). However, the MD and MS of the EDOF and monofocal groups were not different (P > 0.23). The PSD and foveal sensitivity were not different among the groups. In non-glaucomatous patients, the MD and MS of the EDOF IOL were comparable to those of the monofocal IOL and better than those of the multifocal IOL.

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

Journal of Ophthalmology ◽

10.1155/2019/2013160 ◽

2019 ◽

Vol 2019 ◽

pp. 1-7

Author(s):

Maria Emília V. Guimarães ◽

Carolina P. B. Gracitelli ◽

Syril Dorairaj ◽

Fábio N. Kanadani ◽

Tiago S. Prata

Keyword(s):

Visual Field ◽

Corneal Thickness ◽

Mean Deviation ◽

Eye Drops ◽

Automated Perimetry ◽

Standard Automated Perimetry ◽

Factors Associated ◽

Equivalent Number ◽

Group 2 ◽

Test Variability

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.

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Estimating visual field loss from monoscopic optic disc photography using deep learning model

Scientific Reports ◽

10.1038/s41598-020-78144-1 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Jinho Lee ◽

Yong Woo Kim ◽

Ahnul Ha ◽

Young Kook Kim ◽

Ki Ho Park ◽

...

Keyword(s):

Deep Learning ◽

Visual Field ◽

Optic Disc ◽

Correlation Coefficient ◽

Characteristic Curve ◽

Visual Field Loss ◽

Absolute Error ◽

Mean Deviation ◽

Automated Perimetry ◽

Standard Automated Perimetry

AbstractVisual field assessment is recognized as the important criterion of glaucomatous damage judgement; however, it can show large test–retest variability. We developed a deep learning (DL) algorithm that quantitatively predicts mean deviation (MD) of standard automated perimetry (SAP) from monoscopic optic disc photographs (ODPs). A total of 1200 image pairs (ODPs and SAP results) for 563 eyes of 327 participants were enrolled. A DL model was built by combining a pre-trained DL network and subsequently trained fully connected layers. The correlation coefficient and mean absolute error (MAE) between the predicted and measured MDs were calculated. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the detection ability for glaucomatous visual field (VF) loss. The data were split into training/validation (1000 images) and testing (200 images) sets to evaluate the performance of the algorithm. The predicted MD showed a strong correlation and good agreement with the actual MD (correlation coefficient = 0.755; R2 = 57.0%; MAE = 1.94 dB). The model also accurately predicted the presence of glaucomatous VF loss (AUC 0.953). The DL algorithm showed great feasibility for prediction of MD and detection of glaucomatous functional loss from ODPs.

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RetiNerveNet: using recursive deep learning to estimate pointwise 24-2 visual field data based on retinal structure

Scientific Reports ◽

10.1038/s41598-021-91493-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Shounak Datta ◽

Eduardo B. Mariottoni ◽

David Dov ◽

Alessandro A. Jammal ◽

Lawrence Carin ◽

...

Keyword(s):

Visual Field ◽

Optic Disc ◽

Severe Disease ◽

Nerve Fibers ◽

Mean Deviation ◽

Automated Perimetry ◽

Standard Automated Perimetry ◽

Fiber Layer ◽

Floor Effect ◽

Trade Offs

AbstractGlaucoma is the leading cause of irreversible blindness in the world, affecting over 70 million people. The cumbersome Standard Automated Perimetry (SAP) test is most frequently used to detect visual loss due to glaucoma. Due to the SAP test’s innate difficulty and its high test-retest variability, we propose the RetiNerveNet, a deep convolutional recursive neural network for obtaining estimates of the SAP visual field. RetiNerveNet uses information from the more objective Spectral-Domain Optical Coherence Tomography (SDOCT). RetiNerveNet attempts to trace-back the arcuate convergence of the retinal nerve fibers, starting from the Retinal Nerve Fiber Layer (RNFL) thickness around the optic disc, to estimate individual age-corrected 24-2 SAP values. Recursive passes through the proposed network sequentially yield estimates of the visual locations progressively farther from the optic disc. While all the methods used for our experiments exhibit lower performance for the advanced disease group (possibly due to the “floor effect” for the SDOCT test), the proposed network is observed to be more accurate than all the baselines for estimating the individual visual field values. We further augment the proposed network to additionally predict the SAP Mean Deviation values and also facilitate the assignment of higher weightage to the underrepresented groups in the data. We then study the resulting performance trade-offs of the RetiNerveNet on the early, moderate and severe disease groups.

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