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

2021 ◽  
Vol 10 (6) ◽  
pp. 1180
Author(s):  
Sai Karthik Kodeboyina ◽  
Tae Jin Lee ◽  
Kathryn Bollinger ◽  
Lane Ulrich ◽  
David Bogorad ◽  
...  
Keyword(s):  
Visual Field ◽  
Aqueous Humor ◽  
Molecular Mechanisms ◽  
Visual Fields ◽  
Mean Deviation ◽  
Glaucomatous Optic Neuropathy ◽  
Visual Field Index ◽  
Liquid Chromatography Tandem Mass ◽  
Visual Field Indices ◽  
Apolipoprotein D

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.

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.

Role of Central Corneal Thickness on Baseline Parameters and Progression of Visual Fields in Open Angle Glaucoma

2007 ◽  
Vol 17 (4) ◽  
pp. 545-549 ◽  
Author(s):  
H. Shah ◽  
C. Kniestedt ◽  
A. Bostrom ◽  
R. Stamper ◽  
S. Lin
Keyword(s):  
Visual Field ◽  
Central Corneal Thickness ◽  
Open Angle Glaucoma ◽  
Corneal Thickness ◽  
Visual Fields ◽  
Negative Relationship ◽  
Significant Risk ◽  
Mean Deviation ◽  
Open Angle ◽  
Visual Field Progression

Purpose To evaluate the relationship of central corneal thickness (CCT) to baseline visual field parameters and visual field progression in patients with primary open-angle glaucoma (POAG). Methods Charts of consecutive patients with POAG were reviewed to obtain visual field data. Visual field was measured by standard threshold static perimetry. Variables analyzed included mean deviation (MD) and pattern standard deviation (PSD). Results A total of 121 eyes examined over 4 years were evaluated. A significant negative relationship between CCT and PSD (correlation coefficient: −0.02, p<0.05) was found. Analyses comparing CCT to change in PSD and MD (visual field progression) were statistically not significant. Conclusions Patients with thinner corneas initially present with a greater visual field defect, indicating that thin corneas may contribute to advanced glaucomatous damage at the time of diagnosis. However, CCT does not seem to be a significant risk factor for progression of the disease.

Optical Coherent Tomography Predicts Long-Term Visual Outcome of Pituitary Adenoma Surgery: New Perspectives From a 5-Year Follow-up Study

Neurosurgery ◽  
2020 ◽  
Vol 88 (1) ◽  
pp. 106-112 ◽  
Author(s):  
Young Soo Chung ◽  
Minkyun Na ◽  
Jihwan Yoo ◽  
Woohyun Kim ◽  
In-Ho Jung ◽  
...  
Keyword(s):  
Pituitary Adenoma ◽  
Visual Field ◽  
Assessment Tool ◽  
Visual Fields ◽  
Visual Field Defects ◽  
Mean Deviation ◽  
Rnfl Thickness ◽  
Long Term Follow Up

Abstract BACKGROUND Compressive optic neuropathy is the most common indication for transsphenoidal surgery for pituitary adenomas. Optical coherence tomography (OCT) is a useful visual assessment tool for predicting postoperative visual field recovery. OBJECTIVE To analyze visual parameters and their association based on long-term follow-up. METHODS Only pituitary adenoma patients with abnormal visual field defects were selected. A total of 188 eyes from 113 patients assessed by visual field index (VFI) and 262 eyes from 155 patients assessed by mean deviation (MD) were enrolled in this study. Postoperative VFI, MD, and retinal nerve fiber layer (RNFL) thickness were evaluated and followed up. After classifying the patients into normal (&gt;5%) and thin (&lt;5%) RNFL groups, we investigated whether preoperative RNFL could predict visual field outcomes. We also observed how RNFL changes after surgery on a long-term basis. RESULTS Both preoperative VFI and MD had a linear proportional relationship with preoperative RNFL thickness. Sustained improvement of the visual field was observed after surgery in both groups, and the degree of improvement over time in each group was similar. RNFL thickness continued to decrease until 36 mo after surgery (80.2 ± 13.3 μm to 66.6 ± 11.9 μm) while visual field continued to improve (VFI, 61.8 ± 24.5 to 84.3 ± 15.4; MD, −12.9 ± 7.3 dB to −6.3 ± 5.9 dB). CONCLUSION Patients with thin preoperative RNFL may experience visual recovery similar to those with normal preoperative RNFL; however, the probability of normalized visual fields was not comparable. RNFL thickness showed a strong correlation with preoperative visual field defect. Long-term follow-up observation revealed a discrepancy between anatomic and functional recovery.

scholarly journals Bilateral Glaucomatous Optic Neuropathy Caused by Eye Rubbing

2015 ◽  
Vol 6 (3) ◽  
pp. 279-283 ◽  
Author(s):  
Alfonso Savastano ◽  
Maria Cristina Savastano ◽  
Laura Carlomusto ◽  
Silvio Savastano
Keyword(s):  
Intraocular Pressure ◽  
Visual Field ◽  
Optic Disc ◽  
Optic Neuropathy ◽  
Correct Diagnosis ◽  
Visual Fields ◽  
Pattern Electroretinogram ◽  
Glaucomatous Optic Neuropathy ◽  
Fiber Layer ◽  
The Right

In this report, we describe a particular condition of a 52-year-old man who showed advanced bilateral glaucomatous-like optic disc damage, even though the intraocular pressure resulted normal during all examinations performed. Visual field test, steady-state pattern electroretinogram, retinal nerve fiber layer and retinal tomographic evaluations were performed to evaluate the optic disc damage. Over a 4-year observational period, his visual acuity decreased to 12/20 in the right eye and counting fingers in the left eye. Visual fields were severely compromised, and intraocular pressure values were not superior to 14 mm Hg during routine examinations. An accurate anamnesis and the suspicion of this disease represent a crucial aspect to establish the correct diagnosis. In fact, our patient strongly rubbed his eyes for more than 10 h per day. Recurrent and continuous eye rubbing can induce progressive optic neuropathy, causing severe visual field damage similar to the pathology of advanced glaucoma.

scholarly journals Predicting glaucoma prior to its onset using deep learning

2019 ◽  
Author(s):  
Anshul Thakur ◽  
Michael Goldbaum ◽  
Siamak Yousefi
Keyword(s):  
Deep Learning ◽  
Visual Field ◽  
Optic Neuropathy ◽  
Disease Onset ◽  
Visual Fields ◽  
Learning Model ◽  
Glaucomatous Optic Neuropathy ◽  
Learning Models ◽  
Fundus Photographs ◽  
Deep Learning Model

AbstractPurposeTo assess the accuracy of deep learning models to predict glaucoma development from fundus photographs several years prior to disease onset.DesignA deep learning model for prediction of glaucomatous optic neuropathy or visual field abnormality from color fundus photographs.ParticipantsWe retrospectively included 66,721 fundus photographs from 3,272 eyes of 1,636 subjects to develop deep leaning models.MethodFundus photographs and visual fields were carefully examined by two independent readers from the optic disc and visual field reading centers of the ocular hypertension treatment study (OHTS). When an abnormality was detected by the readers, subject was recalled for re-testing to confirm the abnormality and further confirmation by an endpoint committee. Using OHTS data, deep learning models were trained and tested using 85% of the fundus photographs and further validated (re-tested) on the remaining (held-out) 15% of the fundus photographs.Main Outcome MeasuresAccuracy and area under the receiver-operating characteristic curve (AUC).ResultsThe AUC of the deep learning model in predicting glaucoma development 4-7 years prior to disease onset was 0.77 (95% confidence interval 0.75, 0.79). The accuracy of the model in predicting glaucoma development about 1-3 years prior to disease onset was 0.88 (0.86, 0.91). The accuracy of the model in detecting glaucoma after onset was 0.95 (0.94, 0.96).ConclusionsDeep learning models can predict glaucoma development prior to disease onset with reasonable accuracy. Eyes with visual field abnormality but not glaucomatous optic neuropathy had a higher tendency to be missed by deep learning algorithms.

scholarly journals Comparison of Perimetric Outcomes from Melbourne Rapid Fields Tablet Perimeter Software and Humphrey Field Analyzer in Glaucoma Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Harsh Kumar ◽  
Mithun Thulasidas
Keyword(s):  
Standard Deviation ◽  
Visual Field ◽  
Rural Areas ◽  
Visual Fields ◽  
Test Time ◽  
Mean Deviation ◽  
Time Saving ◽  
Humphrey Field Analyzer ◽  
Probability Plot ◽  
The Mean

Purpose. To compare visual field results obtained using Melbourne Rapid Fields (MRF) iPad-based perimeter software and Humphrey Field Analyzer (HFA) 24-2 Swedish Interactive Threshold Algorithm (SITA) standard program in glaucoma patients. Design. A cross-sectional observational study. Methods. In this single-centre study involving patients diagnosed with glaucoma, the perimetric outcomes of MRF were compared against those returned from the HFA 24-2 SITA standard. Outcomes included mean deviation (MD), pattern standard deviation (PSD), visual field index (VFI)/visual capacity (VC), foveal threshold, test time, number of points depressed at P<5% on PSD probability plot, and glaucoma hemifield test/color coded indicator. Results. The study included 28 eyes of 28 glaucoma patients. Mean (standard deviation) test times were 342.07 (56.70) seconds for MRF and 375.11 (88.95) for HFA 24-2 SITA standard P=0.046. Mean MD was significantly lower for MRF (Δ = 3.09, P<0.001), and mean PSD was significantly higher for MRF (Δ = 1.40, P=0.005) compared with HFA. The mean foveal threshold for the MRF was significantly lower than the mean HFA foveal threshold ((Δ = 9.25, P<0.001). The number of points depressed at P<5% on the PSD probability plot was significantly less for MRF P<0.001. Other perimetric outcomes showed no significant differences between both. Bland–Altman plots showed that considerable variability existed between the programs. Conclusion. MRF is a good cost-effective, time-saving, user-friendly tool for monitoring visual fields in settings where access to traditional perimetry is limited. The lack of Internet strength in rural areas and questionable detection of early cases may be two points in MRF fields requiring an upgrade.

Static Threshold Visual Field in Glaucoma with the Fastpac™ Algorithm of the Humphrey Field Analyser is the Gain in Examination Time Offset by Any Loss of Information?

1994 ◽  
Vol 4 (2) ◽  
pp. 105-110 ◽  
Author(s):  
J.P. Nordmann ◽  
PH. Denis ◽  
Y. Nguer ◽  
D. Mouton-Chopin ◽  
H. Saraux
Keyword(s):  
Visual Field ◽  
Visual Fields ◽  
Mean Deviation ◽  
Testing Time ◽  
Global Indices ◽  
Examination Time ◽  
Field Analyser ◽  
Speed Up ◽  
Time Offset ◽  
Test Variability

A new algorithm (Fastpac™) has been designed to speed up full threshold tests with the Humphrey Field Analyser. We studied the utility of this algorithm by comparing central 24-2 threshold programs obtained with standard and Fastpac™ algorithms in 43 hypertensive or glaucomatous eyes. The Fastpac™ reduced testing time by 35% to 45%. In glaucomatous eyes, the time spared was inversely correlated with the degree of visual field impairment. Global indices were used to compare visual fields. Mean absolute differences in indices did not exceed 0.98 dB. Correlations between indices in glaucoma patients with standard and Fastpac™ strategy were respectively 0.99 and 0.98 for mean deviation and pattern standard deviation. The intra-test variability (short-term fluctuation) was slightly higher with Fastpac™ with a lower correlation between algorithms (r=0.62). The use of Fastpac™ achieves a mean time reduction of 40% and does not significantly modify global indices obtained with the Humphrey perimeter in hypertensive and glaucomatous eyes.

scholarly journals Visual field indices and patterns of visual field deficits in mesopic and dark-adapted two-colour fundus-controlled perimetry in macular diseases

2017 ◽  
Vol 102 (8) ◽  
pp. 1054-1059 ◽  
Author(s):  
Maximilian Pfau ◽  
Moritz Lindner ◽  
Julia S Steinberg ◽  
Sarah Thiele ◽  
Christian K Brinkmann ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Visual Field ◽  
Mean Deviation ◽  
Weighted Mean ◽  
Cone Dysfunction ◽  
Macular Diseases ◽  
Visual Field Deficits ◽  
Visual Field Indices ◽  
The Mean ◽  
Loss Variance

Background/AimsTo analyse the retest reliability of visual field indices and to describe patterns of visual field deficits in mesopic and dark-adapted two-colour fundus-controlled perimetry (FCP) in macular diseases.MethodsSeventy-seven eyes (30 eyes with macular diseases and 47 normal eyes) underwent duplicate mesopic and dark-adapted two-colour FCP (Scotopic Macular Integrity Assessment, CenterVue). Non-weighted (mean defect, loss variance), variability-weighted (mean deviation, pattern standard deviation (PSD)) and graphical (cumulative defect (Bebie) curves) indices were computed. Reproducibility (coefficient of repeatability, CoR) of these indices was assessed. Cluster analysis was carried out to identify patterns of visual field deficits.ResultsThe intrasession reproducibility was lower for the mean defect as compared with the mean deviation (CoR (dB) 2.67 vs 2.57 for mesopic, 1.71 vs 1.45 for dark-adapted cyan, 1.94 vs 1.87 for dark-adapted red testing) and lower for the square-root loss variance as compared with the PSD (CoR (dB) 1.48 vs 1.34, 0.77 vs 0.65, 1.23 vs 1.03). Hierarchical cluster analysis of the indices disclosed six patterns of visual field deficits (approximately unbiased P value>0.95) with varying degrees of global versus focal defect and rod versus cone dysfunction. These were also reflected by the cumulative defect curves.ConclusionFCP with mesopic and dark-adapted two-colour testing allows for reproducible assessment of different types of retinal sensitivity, whereby mean deviation and PSD exhibited the better retest reliability of the tested indices. Distinct patterns of retinal dysfunction can be identified using this setup, reflecting variable degrees of rod and cone dysfunction in different macular diseases. Dark-adapted two-colour FCP provides additional diagnostic information and allows for refined structure–function correlation in macular diseases.

scholarly journals Forecasting Future Humphrey Visual Fields Using Deep Learning

2018 ◽  
Author(s):  
Joanne C. Wen ◽  
Cecilia S. Lee ◽  
Pearse A. Keane ◽  
Sa Xiao ◽  
Yue Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Visual Field ◽  
Visual Fields ◽  
Mean Deviation ◽  
Time Interval ◽  
Learning Networks ◽  
University Of Washington ◽  
Test Set ◽  
Interval Model ◽  
Real World Datasets

ABSTRACTPurposeTo determine if deep learning networks could be trained to forecast a future 24-2 Humphrey Visual Field (HVF).DesignRetrospective database study.ParticipantsAll patients who obtained a HVF 24-2 at the University of Washington.MethodsAll datapoints from consecutive 24-2 HVFs from 1998 to 2018 were extracted from a University of Washington database. Ten-fold cross validation with a held out test set was used to develop the three main phases of model development: model architecture selection, dataset combination selection, and time-interval model training with transfer learning, to train a deep learning artificial neural network capable of generating a point-wise visual field prediction.Main outcome measuresMean absolute error (MAE) and difference in Mean Deviation (MD) between predicted and actual future HVF.ResultsMore than 1.7 million perimetry points were extracted to the hundredth decibel from 32,443 24-2 HVFs. The best performing model with 20 million trainable parameters, CascadeNet-5, was selected. The overall MAE for the test set was 2.47 dB (95% CI: 2.45 dB to 2.48 dB). The 100 fully trained models were able to successfully predict progressive field loss in glaucomatous eyes up to 5.5 years in the future with a correlation of 0.92 between the MD of predicted and actual future HVF (p < 2.2 = 10−16) and an average difference of 0.41 dB.ConclusionsUsing unfiltered real-world datasets, deep learning networks show an impressive ability to not only learn spatio-temporal HVF changes but also to generate predictions for future HVFs up to 5.5 years, given only a single HVF.

scholarly journals Macular choroidal thickness: evaluation of variability among measurements and assessment of predictive value of glaucomatous visual field damage

2021 ◽  
Author(s):  
Maria Marenco ◽  
Federico Rissotto ◽  
Andrea Palamini ◽  
Carlo Alberto Cutolo ◽  
Giulia Agosto ◽  
...  
Keyword(s):  
Visual Field ◽  
Predictive Value ◽  
Choroidal Thickness ◽  
Primary Open Angle Glaucoma ◽  
Open Angle Glaucoma ◽  
Mean Deviation ◽  
Choroidal Circulation ◽  
Visual Field Damage ◽  
Visual Field Indices ◽  
The Relationship

Introduction: To investigate the relationship between the choroidal circulation and glaucoma, assessing macular choroidal thickness (MCT) as a predictive value of glaucomatous visual field damage. Methods: Twenty primary open-angle glaucoma patients were recruited. Patients underwent two SS-OCTs scans: one with DRI OCT (Topcon) and the other with PLEX Elite 9000 (Zeiss). Standard OCT parameters were acquired by DRI OCT, while MCT was manually measured in 5 points on Plex ELITE 9000 images. The relationship among MCT, standard OCT parameters and visual field indices were evaluated. Pearson’s r correlation was calculated to evaluate these relationships. Reproducibility of measurements was analyzed. Results: MCT measurements showed a good intra- and inter-observer repeatability. A negative correlation appeared between MCT and body mass index (BMI) (r = -0.518, p=0.023). Mean deviation showed a statistically significant correlation with MCT measured at sub-foveal and at 1000 µm nasally (r = 0.50, p=0.03 and r = 0.52, p=0.023). A correlation was found between the two MCT (Zeiss vs Topcon) measurements and between MCT and peripapillary choroidal thickness (r = 0.944 and r = 0.740, p<0.001, respectively). Conclusions: A good intra- and inter-observer reproducibility was found. MCT showed a weak predictive value of glaucomatous visual field damage. A significant correlation was found between MCT and BMI.

Sign in / Sign up

Export Citation Format

Share Document