scholarly journals Comparison of Threshold Saccadic Vector Optokinetic Perimetry (SVOP) and Standard Automated Perimetry (SAP) in Glaucoma. Part II: Patterns of Visual Field Loss and Acceptability

2017 ◽  
Vol 6 (5) ◽  
pp. 4 ◽  
Author(s):  
Alice D. McTrusty ◽  
Lorraine A. Cameron ◽  
Antonios Perperidis ◽  
Harry M. Brash ◽  
Andrew J. Tatham ◽  
...  
Keyword(s):  
Visual Field ◽  
Visual Field Loss ◽  
Automated Perimetry ◽  
Standard Automated Perimetry

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

2019 ◽  
Vol 9 (1) ◽  
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.

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

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.

SITA-Standard perimetry has better performance than FDT2 matrix perimetry for detecting glaucomatous progression

2018 ◽  
Vol 102 (10) ◽  
pp. 1396-1401
Author(s):  
Michael Wall ◽  
Chris A Johnson ◽  
K D Zamba
Keyword(s):  
Visual Field ◽  
Visual Field Loss ◽  
Detection Algorithm ◽  
Data Series ◽  
Automated Perimetry ◽  
Standard Automated Perimetry ◽  
Full Data ◽  
Visual Field Progression ◽  
Standard Testing ◽  
Humphrey Field Analyzer

PurposeThe Humphrey Matrix (FDT2) may be more sensitive in detecting glaucomatous visual field loss than SITA standard automated perimetry (SAP) performed on the Humphrey Field Analyzer (HFA). Therefore, FDT may be a good candidate to determine disease progression in patients with glaucoma. Our aim was to test the hypothesis that automated perimetry using the FDT2 would be equal to, or more effective than, HFA SITA-Standard, in identifying glaucomatous progression.MethodsOne hundred and twenty patients with glaucoma were tested twice at baseline and every 6 months for 4 years with HFA SITA-Standard and FDT2. FDT2 values were standardised to HFA SAP values. We used pointwise linear regression (PLR) over the full data series to identify glaucomatous progression and generated an array of results using three different criteria: (1) three or more clustered test locations progressing, (2) three or more non-clustered test locations progressing and (3) total number of progressing test locations. We compared HFA SAP and FDT2 for the number of locations signalled by the PLR detection algorithm.ResultsRegardless of the criteria, HFA SAP with SITA-Standard testing detected visual field progression at a higher rate than the FDT2 overall (P<0.001).ConclusionHFA SAP identifies glaucomatous visual field progression at a rate at least as high if not higher than FDT2.

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

2019 ◽  
Author(s):  
Samuel I. Berchuck ◽  
Sayan Mukherjee ◽  
Felipe A. Medeiros
Keyword(s):  
Visual Field ◽  
Learning Algorithm ◽  
Visual Fields ◽  
Visual Field Loss ◽  
Ordinary Least Squares ◽  
Rate Of Change ◽  
Feature Representation ◽  
Mean Deviation ◽  
Automated Perimetry ◽  
Standard Automated Perimetry

ABSTRACTPurposeTo develop a novel deep learning algorithm to improve estimation of rates of progression and prediction of future patterns of visual field loss in glaucoma.DesignProspective observational cohort.MethodsA variational auto-encoder (VAE) was trained to learn a low-dimensional feature 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. From the VAE, rates were calculated using the average of slopes across latent features from ordinary least squares (OLS) regression and trajectories of the features were used to generate predictions.ResultsThe 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 (19% vs. 6%) and four (40% vs 14%) 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: 4.06 dB vs. PW: 6.06 dB; P<0.001).ConclusionA 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 in the disease.

scholarly journals Association between visual field damage and corneal structural parameters

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.

Essentials of Automated Perimetry

Visual Fields ◽  
2010 ◽  
Author(s):  
George Shafranov
Keyword(s):  
Visual Field ◽  
Digital Data ◽  
Automated Perimetry ◽  
Standard Automated Perimetry ◽  
Central Visual Field ◽  
Kinetic Perimetry ◽  
Field Of Vision ◽  
Stationary Test ◽  
Variable Light ◽  
Defects Analysis

Standard automated perimetry is a standard method of measuring peripheral visual function. Automated static perimetry gained wide acceptance among clinicians due to the test’s high reproducibility and standardization and ability to store, exchange, and statistically analyze digital data. Advances in the computerized visual field assessment have contributed to our understanding of the role that field of vision plays in clinical evaluation and management of patients. The Humphrey Visual Field Analyzer/HFA II-i is the most commonly used automated perimeter in the United States, and the examples in this chapter have been obtained with this instrument. Aubert and Förster in the 1860s developed the arc perimeter, which led to the mapping of peripheral neurologic visual field abnormalities and advanced glaucomatous field defects. Analysis of the central visual field was not seen as clinically important by most clinicians until 1889, when Bjerrum described a detected arcuate paracentral scotoma. Later, Traquair further contributed to kinetic perimetry on the tangent screen. In 1893, Groenouw proposed the term “isopter” for lines with the same sensitivity on a perimetry chart. Rønne further developed kinetic isopter perimetry in 1909 and described the nasal step in glaucoma. Although the first bowl perimeter was introduced in 1872 by Scherk, due to problems with achieving even illumination on the screen, it did not become popular. The version of the bowl perimeter introduced by Goldmann in 1945 became widely accepted and is a significant contribution to clinical perimetry. The Goldmann perimeter incorporated a projected stimulus on an illuminated bowl, with standardization of background illumination as well as size and intensity of the stimulus, and allowed effective use of both static and kinetic techniques. For these reasons, the Goldmann instrument has remained the clinical standard throughout the world until widespread acceptance of automated perimetry. Harms and Aulhorn later designed the Tübingen perimeter with a bowl-type screen exclusively for the measurement of static threshold fields, using stationary test objects with variable light intensity. While excellent threshold measurements were possible with this instrument, the time and effort involved in such measurements prevented this perimeter from becoming widely used.

scholarly journals Risk of newly developing visual field defect and neurodegeneration after pars plana vitrectomy for idiopathic epiretinal membrane

2020 ◽  
pp. bjophthalmol-2020-317478
Author(s):  
Kunihiko Akino ◽  
Norihiro Nagai ◽  
Kazuhiro Watanabe ◽  
Norimitsu Ban ◽  
Toshihide Kurihara ◽  
...  
Keyword(s):  
Visual Field ◽  
Epiretinal Membrane ◽  
Pars Plana Vitrectomy ◽  
Internal Limiting Membrane ◽  
Visual Field Defects ◽  
Automated Perimetry ◽  
Standard Automated Perimetry ◽  
Ilm Peeling ◽  
Idiopathic Epiretinal Membrane ◽  
Pars Plana

Background/AimsPars plana vitrectomy (PPV) is widely performed in patients with idiopathic epiretinal membrane (iERM) to improve vision. Postoperative visual field defects (VFDs) have been previously reported. However, whether they occur when using the most recent PPV system, and the frequency of VFDs as measured by standard automated perimetry, remain poorly documented and were examined in this study.MethodsData of 30 eyes (30 patients; mean age, 66.1 years; 15 men) who underwent PPV for iERM during February 2016–June 2019 and had preoperative and postoperative visual field measurements using standard automated perimetry (Humphrey visual field analyser 30-2 program) were retrospectively analysed. Eyes with diseases other than iERM, including moderate-to-severe cataract or preoperative VFDs were excluded.ResultsVFD, defined by the Anderson and Patella’s criteria, was found in 73.3% of the eyes 1 month after PPV. After age adjustment, internal limiting membrane (ILM) peeling was identified as a risk factor for postoperative VFD (p=0.035; 95% CI 1.173 to 92.8). Postoperative VFD was frequently observed nasally (86.4%, p=0.002), and on optical coherence tomography measurements, ganglion cell layer (GCL) thinning was found temporal to the fovea (p=0.008). Thinning of the superior and inferior retinal nerve fibre layers and of the GCL temporal to the fovea were significant in eyes after ILM peeling (all p<0.05).ConclusionILM peeling may cause inner retinal degeneration and lead to the development of VFDs after PPV, which should be further examined.

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