ArticleIEEE transactions on bio-medical engineering2014
Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements.
Article in IEEE transactions on bio-medical engineering, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed.
- Article
- A transfer learning-based multimodal neural network combining metadata and multiple medical images for glaucoma type diagnosis.Scientific reports · 2023Article
- Machine-Identified Patterns of Visual Field Loss and an Association with Rapid Progression in the Ocular Hypertension Treatment Study.Ophthalmology · 2022Article
- Prediction of Visual Field Progression from OCT Structural Measures in Moderate to Advanced Glaucoma.American journal of ophthalmology · 2021Article
- Association between visual field damage and corneal structural parameters.Scientific reports · 2021Article
- An enhanced deep image model for glaucoma diagnosis using feature-based detection in retinal fundus.Medical & biological engineering & computing · 2021Article
- Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard.Ophthalmology · 2020Article
- Deep-learning-based Prediction of Late Age-Related Macular Degeneration Progression.Nature machine intelligence · 2020Article
- Convex Representations Using Deep Archetypal Analysis for Predicting Glaucoma.IEEE journal of translational engineering in health and medicine · 2020Article
- Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma Using a Deep Variational Autoencoder.Scientific reports · 2019Article
- Spatial and Temporal Characteristics of Visual Field Progression in Glaucoma Assessed by Parallel Factor Analysis.Korean journal of ophthalmology : KJO · 2019Article
- Dynamic Monitoring and Control of Irreversible Chronic Diseases with Application to Glaucoma.Production and operations management · 2019Article
- Automated Vision-Based High Intraocular Pressure Detection Using Frontal Eye Images.IEEE journal of translational engineering in health and medicine · 2019Article
- Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs.Scientific reports · 2018Article
- Application of Pattern Recognition Analysis to Optimize Hemifield Asymmetry Patterns for Early Detection of Glaucoma.Translational vision science & technology · 2018Article
- Article
- Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields.Translational vision science & technology · 2016Article
- Detecting glaucomatous change in visual fields: Analysis with an optimization framework.Journal of biomedical informatics · 2015Article
- Use of artificial intelligence in forecasting glaucoma progression.Taiwan journal of ophthalmologyReview
- Forecasting Risk of Future Rapid Glaucoma Worsening Using Early Visual Field, OCT, and Clinical Data.Ophthalmology. GlaucomaArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
A hierarchical approach to learn from visual field data was adopted to identify glaucomatous visual field defect patterns and to detect glaucomatous progression. The analysis pipeline included three stages, namely, clustering, glaucoma boundary limit detection, and glaucoma progression detection testing. First, cross-sectional visual field tests collected from each subject were clustered using a mixture of Gaussians and model parameters were estimated using expectation maximization. The visual field clusters were further estimated to recognize glaucomatous visual field defect patterns by decomposing each cluster into several axes. The glaucoma visual field defect patterns along each axis then were identified. To derive a definition of progression, the longitudinal visual fields of stable glaucoma eyes on the abnormal cluster axes were projected and the slope was approximated using linear regression (LR) to determine the confidence limit of each axis. For glaucoma progression detection, the longitudinal visual fields of each eye on the abnormal cluster axes were projected and the slope was approximated by LR. Progression was assigned if the progression rate was greater than the boundary limit of the stable eyes; otherwise, stability was assumed. The proposed method was compared to a recently developed progression detection method and to clinically available glaucoma progression detection software. The clinical accuracy of the proposed pipeline was as good as or better than the currently available methods.
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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.