ArticleTranslational vision science & technology2019
Quantification of Visual Field Variability in Glaucoma: Implications for Visual Field Prediction and Modeling.
Article in Translational vision science & technology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Clinical Effective Dynamic Range and the Measurement Floor of SITA-Faster Visual Field Tests.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Article
- Fast Progressors in Primary Open-Angle Glaucoma: A 5-Year Visual Field Index-Based Classification in a Real-World Cohort.Ophthalmology and therapy · 2026Article
- Comparing the Use of Measured and Smoothed Data in Forecasting Visual Field Tests Using Deep Learning.Ophthalmology science · 2026Article
- Visualising treatment effects in low-vision settings: proven and potential endpoints for clinical trials of inherited retinal disease therapies.Gene therapy · 2026Review
- Labeling matters: A multicenter machine learning study on visual field progression in Glaucoma.PloS one · 2026Article
- A Bayesian Hierarchical Longitudinal Model for Estimation of Central Visual Field Rates of Change in Glaucoma.Translational vision science & technology · 2025Article
- Number of macula optical coherence tomography scans needed to detect glaucoma progression.The British journal of ophthalmology · 2025Article
- Detecting glaucoma worsening using optical coherence tomography derived visual field estimates.Scientific reports · 2025Article
- Comparison of Visual Field Fluctuation Between Myopic and Emmetropic Glaucoma Patients.Journal of ophthalmology · 2025Article
- Clinical Outcomes of Excisional Goniotomy with the Kahook Dual Blade: 6-Year Results.Ophthalmology and therapy · 2024Article
- Bidirectional gated recurrent unit network model can generate future visual field with variable number of input elements.PloS one · 2024Article
- Association of macular vessel density and ganglion cell complex thickness with central visual field progression in glaucoma.The British journal of ophthalmology · 2023Article
- Visual Field Prediction: Evaluating the Clinical Relevance of Deep Learning Models.Ophthalmology science · 2023Article
- Hierarchical Censored Bayesian Analysis of Visual Field Progression.Translational vision science & technology · 2021Article
- Inter-Eye Association of Visual Field Defects in Glaucoma and Its Clinical Utility.Translational vision science & technology · 2020Article
- Optical coherence tomography and optical coherence tomography angiography in glaucoma: diagnosis, progression, and correlation with functional tests.Therapeutic advances in ophthalmologyReview
- Application of artificial intelligence in glaucoma care: An updated review.Taiwan journal of ophthalmologyReview
- Use of artificial intelligence in forecasting glaucoma progression.Taiwan journal of ophthalmologyReview
- A Hybrid Deep Learning-Based Approach for Visual Field Test Forecasting.Ophthalmology scienceArticle
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo quantify visual field (VF) variability as a function of threshold sensitivity and location, and to compare weighted pointwise linear regression (PLR) with unweighted PLR and pointwise exponential regression (PER) for data fit and prediction ability.
methodsTwo datasets were used for this retrospective study. The first was used to characterize and estimate VF variability, and included a total of 4,747 eyes of 3,095 glaucoma patients with six or more VFs and 3 years or more of follow-up. After performing PER for each series, standard deviation of residuals was quantified for each decibel of sensitivity as a measure of variability. A separate dataset was used to test and compare unweighted PLR, weighted PLR, and PER for data fit and prediction, and included 261 eyes of 176 primary open-angle glaucoma patients with 10 or more VFs and 6 years or more of follow-up.
resultsThe degree of variability changed as a function of threshold sensitivity with a zenith and a nadir at 33 and 11 dB, respectively. Variability decreased with eccentricity and was higher in the central 10° (
conclusionsVF variability increases with the severity of glaucoma damage and decreases with eccentricity. Weighted linear regression neither improves model fit nor prediction. PER exhibited the best prediction ability, which is likely related to the nonlinear nature of long-term glaucomatous perimetric decay. TRANSLATIONAL RELEVANCE: This study suggests that taking into account heteroscedasticity has no advantage in VF modeling.
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