ArticleTranslational vision science & technology2019
Comparison of Methods to Detect and Measure Glaucomatous Visual Field Progression.
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 39 papers.
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Who cites it
39 citing papers in PubMed.
- Predicting worsening visual field via masked dual stage attention recurrent neural network.Scientific reports · 2026Article
- Myopic and Glaucomatous Optic Neuropathy in Highly Myopic Eyes: A Practical Framework for Diagnosis, Monitoring, and Management.Journal of clinical medicine · 2026Review
- Labeling matters: A multicenter machine learning study on visual field progression in Glaucoma.PloS one · 2026Article
- Functional generalized estimating equation model to detect glaucomatous visual field progression.International journal of ophthalmology · 2026Article
- Visual field prediction using K-means clustering in patients with primary open angle glaucoma.International journal of ophthalmology · 2026Article
- Comparing the Utility of Retinal Nerve Fiber Layer and Ganglion Cell Inner Plexiform Layer OCT Changes to Detect Glaucoma Progression.Ophthalmology · 2026Article
- Dual-Level Pattern Tree for Visual Field Improves Glaucoma Progression and Polygenic Risk Prediction.Translational vision science & technology · 2025Article
- Comparison of Methods for Visual Field Progression in Eyes With Central Visual Field Defects.Translational vision science & technology · 2025Article
- Detecting glaucoma worsening using optical coherence tomography derived visual field estimates.Scientific reports · 2025Article
- [Visual field prediction based on temporal-spatial feature learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Long-Term Rate of Optic Disc Rim Loss in Glaucoma Patients Measured From Optic Disc Photographs With a Deep Neural Network.Translational vision science & technology · 2024Article
- Detecting Visual Field Worsening From Optic Nerve Head and Macular Optical Coherence Tomography Thickness Measurements.Translational vision science & technology · 2024Article
- Prediction of visual field progression with serial optic disc photographs using deep learning.The British journal of ophthalmology · 2024Article
- Prediction of Visual Field Progression with Baseline and Longitudinal Structural Measurements Using Deep Learning.American journal of ophthalmology · 2024Article
- Utility of targeted mean total deviation trend analysis for detecting progressive visual field changes in early-to-moderate stage glaucoma.Eye (London, England) · 2024Article
- Article
- Foveal avascular zone vessel density is associated with visual field progression in early-stage glaucoma eyes with central visual field damage.Scientific reports · 2023Article
- Validating Trend-Based End Points for Neuroprotection Trials in Glaucoma.Translational vision science & technology · 2023Article
- Comparing Rates of Change in Moderate to Advanced Glaucoma: Retinal Nerve Fiber Layer Versus Bruch Membrane Opening-Minimum Rim Width.American journal of ophthalmology · 2023Article
- Predicting Visual Field Worsening with Longitudinal OCT Data Using a Gated Transformer Network.Ophthalmology · 2023Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo compare methods to assess visual field (VF) progression in glaucoma.
methods4,950 VFs of 253 primary open angle-glaucoma patients were evaluated for progression with the following methods: clinical evaluation, guided progression analysis (GPA), mean deviation (MD), and visual field index (VFI) rates, Advanced Glaucoma Intervention Study (AGIS) and Collaborative Initial Glaucoma Treatment Study (CIGTS) scores, pointwise linear regression (PLR), permutation of PLR (PoPLR), and glaucoma rate index (GRI). A separate simulated series of longitudinal VFs was assessed with all methods except for GPA and clinical evaluation.
resultsThe average (±SD) age of the patients at baseline was 65.4 (±11.5) years. The average (±SD) follow-up was 11.8 (±4.6) years, and the mean (±SD) number of VFs was 16.8 (±7.0). Proportion of series detected as progressing was 65% for PoPLR, 58% for GRI, 41% for GPA, 40% for PLR, 36% for CIGTS, 35% for clinicians, 31% for MD rate, 29% for AGIS, and 22% for VFI rate. Median times to detection of progression were 7.3 years for PoPLR, 7.5 years for GRI, 11 years for clinicians, 14 years for GPA, 16 years for PLR, 17 years for CIGTS, 19 years for AGIS, and more than 20 years for MD and VFI rates. In simulated VF series, GRI had the highest partial area under the receiver operator characteristic curve (0.040) to distinguish between glaucoma progression and aging/cataract decay, followed by VFI rate (0.028), MD rate (0.024), and PoPLR (0.006).
conclusionsGRI and PoPLR showed the highest proportion of series detected as progressing and shortest times to progression detection. GRI exhibited the best ability to detect progression in the simulated VF series. TRANSLATIONAL RELEVANCE: Knowledge of the properties of every method would allow tailoring application in both clinical and research settings.
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