ArticleOphthalmology2020
Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma.
Article in Ophthalmology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- Unsupervised Classification of Wide-Field Swept-Source Optical Coherence Tomography Reveals Patterns Associated With Glaucoma Severity and Progression.Translational vision science & technology · 2026Article
- Long-Term Efficacy of Phacoemulsification Combined With Trabeculectomy for Advanced Chronic Primary Angle-Closure Glaucoma With Cataract: A 3-Year Retrospective Study.Journal of ophthalmology · 2026Article
- Dual-Level Pattern Tree for Visual Field Improves Glaucoma Progression and Polygenic Risk Prediction.Translational vision science & technology · 2025Article
- Incidence and risk factors for early transient intraocular pressure elevation after penetrating canaloplasty in patients with glaucoma.BMC ophthalmology · 2025Article
- Enhancing central visual field loss representation with a hybrid unsupervised approach.International ophthalmology · 2025Article
- A three-tier AI solution for equitable glaucoma diagnosis across China's hierarchical healthcare system.NPJ digital medicine · 2025Article
- Latent retinal structural patterns with aging.NPJ digital medicine · 2025Article
- Glaucoma detection and staging from visual field images using machine learning techniques.PloS one · 2025Article
- Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence.Life (Basel, Switzerland) · 2024Review
- Contributions of Brain Microstructures and Metabolism to Visual Field Loss Patterns in Glaucoma Using Archetypal and Information Gain Analyses.Investigative ophthalmology & visual science · 2024Article
- Machine Learning-Derived Baseline Visual Field Patterns Predict Future Glaucoma Onset in the Ocular Hypertension Treatment Study.Investigative ophthalmology & visual science · 2024Article
- Article
- Uncertainty-inspired open set learning for retinal anomaly identification.Nature communications · 2023Article
- Archetypal analysis of longitudinal visual fields for idiopathic intracranial hypertension patients presenting in a clinic setting.PLOS digital health · 2023Article
- Incidence and risk factors of early transient intraocular pressure elevation after canaloplasty for primary open-angle glaucoma.Journal of Zhejiang University. Science. B · 2023Article
- Machine-Identified Patterns of Visual Field Loss and an Association with Rapid Progression in the Ocular Hypertension Treatment Study.Ophthalmology · 2022Article
- The number of examinations required for the accurate prediction of the progression of the central 10-degree visual field test in glaucoma.Scientific reports · 2022Article
- A Prospective Longitudinal Study to Investigate Corneal Hysteresis as a Risk Factor of Central Visual Field Progression in Glaucoma.American journal of ophthalmology · 2022Article
- Archetypal Analysis Reveals Quantifiable Patterns of Visual Field Loss in Optic Neuritis.Translational vision science & technology · 2022Article
Corrections and comments
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Authors and funding
15 authors at 8 institutions in 3 countries.
Funding
Abstract
purposeTo quantify the central visual field (VF) loss patterns in glaucoma using artificial intelligence.
designRetrospective study.
participantsVFs of 8712 patients with 13 951 Humphrey 10-2 test results from 13 951 eyes for cross-sectional analyses, and 824 patients with at least 5 reliable 10-2 test results at 6-month intervals or more from 1191 eyes for longitudinal analyses.
methodsTotal deviation values were used to determine the central VF patterns using the most recent 10-2 test results. A 24-2 VF within a 3-month window of the 10-2 tests was used to stage eyes into mild, moderate, or severe functional loss using the Hodapp-Anderson-Parrish scale at baseline. Archetypal analysis was applied to determine the central VF patterns. Cross-validation was performed to determine the optimal number of patterns. Stepwise regression was applied to select the optimal feature combination of global indices, average baseline decomposition coefficients from central VFs archetypes, and other factors to predict central VF mean deviation (MD) slope based on the Bayesian information criterion (BIC).
main outcome measuresThe central VF patterns stratified by severity stage based on 24-2 test results and a model to predict the central VF MD change over time using baseline test results.
resultsFrom cross-sectional analysis, 17 distinct central VF patterns were determined for the 13 951 eyes across the spectrum of disease severity. These central VF patterns could be divided into isolated superior loss, isolated inferior loss, diffuse loss, and other loss patterns. Notably, 4 of the 5 patterns of diffuse VF loss preserved the less vulnerable inferotemporal zone, whereas they lost most of the remaining more vulnerable zone described by the Hood model. Inclusion of coefficients from central VF archetypical patterns strongly improved the prediction of central VF MD slope (BIC decrease, 35; BIC decrease of >6 indicating strong prediction improvement) than using only the global indices of 2 baseline VF results. Eyes with baseline VF results with more superonasal and inferonasal loss were more likely to show worsening MD over time.
conclusionsWe quantified central VF patterns in glaucoma, which were used to improve the prediction of central VF worsening compared with using only global indices.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.