ArticleOphthalmology2018
Reversal of Glaucoma Hemifield Test Results and Visual Field Features in Glaucoma.
Article in Ophthalmology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Making a Correct Diagnosis of Glaucoma: Data From the EMGT.Journal of glaucoma · 2019Trial
- Dual-Level Pattern Tree for Visual Field Improves Glaucoma Progression and Polygenic Risk Prediction.Translational vision science & technology · 2025Article
- Archetypal Analysis Reveals Consistent Visual Field Patterns for Stimulus Size III and Size Modulation Perimetry in Glaucoma.Translational vision science & technology · 2025Article
- Archetypal Analysis Reveals Consistent Visual Field Patterns for Stimulus Sizes III and V in Glaucoma and NAION.Translational vision science & technology · 2024Article
- Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence.Life (Basel, Switzerland) · 2024Review
- Transformer-Based Deep Learning Prediction of 10-Degree Humphrey Visual Field Tests From 24-Degree Data.Translational vision science & technology · 2024Article
- Contributions of Brain Microstructures and Metabolism to Visual Field Loss Patterns in Glaucoma Using Archetypal and Information Gain Analyses.Investigative ophthalmology & visual science · 2024Article
- Artifact Correction in Retinal Nerve Fiber Layer Thickness Maps Using Deep Learning and Its Clinical Utility in Glaucoma.Translational vision science & technology · 2023Article
- Cohort Study of Race/Ethnicity and Incident Primary Open-Angle Glaucoma Characterized by Autonomously Determined Visual Field Loss Patterns.Translational vision science & technology · 2022Article
- Artificial intelligence in ophthalmology - Machines think!Indian journal of ophthalmology · 2022Article
- Archetypal Analysis Reveals Quantifiable Patterns of Visual Field Loss in Optic Neuritis.Translational vision science & technology · 2022Article
- Unsupervised Machine Learning Identifies Quantifiable Patterns of Visual Field Loss in Idiopathic Intracranial Hypertension.Translational vision science & technology · 2021Article
- Development and Comparison of Machine Learning Algorithms to Determine Visual Field Progression.Translational vision science & technology · 2021Article
- Explainable Machine Learning Model for Glaucoma Diagnosis and Its Interpretation.Diagnostics (Basel, Switzerland) · 2021Article
- Inter-Eye Association of Visual Field Defects in Glaucoma and Its Clinical Utility.Translational vision science & technology · 2020Article
- An artificial intelligence model for the simulation of visual effects in patients with visual field defects.Annals of translational medicine · 2020Article
- Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma.Ophthalmology · 2020Article
- Characterization of Central Visual Field Loss in End-stage Glaucoma by Unsupervised Artificial Intelligence.JAMA ophthalmology · 2020Article
- The impact of artificial intelligence in the diagnosis and management of glaucoma.Eye (London, England) · 2020Review
- Agreement and Predictors of Discordance of 6 Visual Field Progression Algorithms.Ophthalmology · 2019Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
purposeTo develop a visual field (VF) feature model to predict the reversal of glaucoma hemifield test (GHT) results to within normal limits (WNL) after 2 consecutive outside normal limits (ONL) results.
designRetrospective cohort study.
participantsVisual fields of 44 503 eyes from 26 130 participants.
methodsEyes with 3 or more consecutive reliable VFs measured with the Humphrey Field Analyzer (Swedish interactive threshold algorithm standard 24-2) were included. Eyes with ONL GHT results for the 2 baseline VFs were selected. We extracted 3 categories of VF features from the baseline tests: (1) VF global indices (mean deviation [MD] and pattern standard deviation), (2) mismatch between baseline VFs, and (3) VF loss patterns (archetypes). Logistic regression was applied to predict the GHT results reversal. Cross-validation was applied to evaluate the model on testing data by the area under the receiver operating characteristic curve (AUC). We ascertained clinical glaucoma status on a patient subset (n = 97) to determine the usefulness of our model.
main outcome measuresPredictive models for GHT results reversal using VF features.
resultsFor the 16 604 eyes with 2 initial ONL results, the prevalence of a subsequent WNL result increased from 0.1% for MD < -12 dB to 13.8% for MD ≥-3 dB. Compared with models with VF global indices, the AUC of predictive models increased from 0.669 (MD ≥-3 dB) and 0.697 (-6 dB ≤ MD < -3 dB) to 0.770 and 0.820, respectively, by adding VF mismatch features and computationally derived VF archetypes (P < 0.001 for both). The GHT results reversal was associated with a large mismatch between baseline VFs. Moreover, the GHT results reversal was associated more with VF archetypes of nonglaucomatous loss, severe widespread loss, and lens rim artifacts. For a subset of 97 eyes, using our model to predict absence of glaucoma based on clinical evidence after 2 ONL results yielded significantly better prediction accuracy (87.7%; P < 0.001) than predicting GHT results reversal (68.8%) with a prescribed specificity 67.7%.
conclusionsUsing VF features may predict the GHT results reversal to WNL after 2 consecutive ONL results.
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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.