ArticleTranslational vision science & technology2016
Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields.
Article in Translational vision science & technology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00221897 (Diagnostic Innovations in Glaucoma Study), which is not on this map. Cited by 34 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Diagnostic Innovations in Glaucoma Study
Who cites it
34 citing papers in PubMed, 62 citations in OpenAlex.
- Dual-Level Pattern Tree for Visual Field Improves Glaucoma Progression and Polygenic Risk Prediction.Translational vision science & technology · 2025Article
- Identification of a Novel Subtype of Ocular Hypertensive Eyes with the Lowest Risk of Progression Under Treatment.Ophthalmology and therapy · 2025Article
- Detection of glaucoma progression on longitudinal series of en-face macular optical coherence tomography angiography images with a deep learning model.The British journal of ophthalmology · 2024Observational
- Artificial Intelligence and Advanced Technology in Glaucoma: A Review.Journal of personalized medicine · 2024Review
- Machine Learning-Derived Baseline Visual Field Patterns Predict Future Glaucoma Onset in the Ocular Hypertension Treatment Study.Investigative ophthalmology & visual science · 2024Article
- Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection.Bioengineering (Basel, Switzerland) · 2024Review
- The utilization of artificial intelligence in glaucoma: diagnosis versus screening.Frontiers in ophthalmology · 2024Review
- Article
- A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach.Scientific reports · 2023Article
- Machine-Identified Patterns of Visual Field Loss and an Association with Rapid Progression in the Ocular Hypertension Treatment Study.Ophthalmology · 2022Article
- Development of glaucoma predictive model and risk factors assessment based on supervised models.BioData mining · 2021Article
- Development and Comparison of Machine Learning Algorithms to Determine Visual Field Progression.Translational vision science & technology · 2021Article
- Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective.Progress in retinal and eye research · 2021Review
- Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice.Translational vision science & technology · 2020Article
- Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard.Ophthalmology · 2020Article
- An Artificial Intelligence Approach to Assess Spatial Patterns of Retinal Nerve Fiber Layer Thickness Maps in Glaucoma.Translational vision science & technology · 2020Article
- A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression.Translational vision science & technology · 2020Review
- 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
- Convex Representations Using Deep Archetypal Analysis for Predicting Glaucoma.IEEE journal of translational engineering in health and medicine · 2020Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
Abstract
purposeTo validate Gaussian mixture-model with expectation maximization (GEM) and variational Bayesian independent component analysis mixture-models (VIM) for detecting glaucomatous progression along visual field (VF) defect patterns (GEM-progression of patterns (POP) and VIM-POP). To compare GEM-POP and VIM-POP with other methods.
methodsGEM and VIM models separated cross-sectional abnormal VFs from 859 eyes and normal VFs from 1117 eyes into abnormal and normal clusters. Clusters were decomposed into independent axes. The confidence limit (CL) of stability was established for each axis with a set of 84 stable eyes. Sensitivity for detecting progression was assessed in a sample of 83 eyes with known progressive glaucomatous optic neuropathy (PGON). Eyes were classified as progressed if any defect pattern progressed beyond the CL of stability. Performance of GEM-POP and VIM-POP was compared to point-wise linear regression (PLR), permutation analysis of PLR (PoPLR), and linear regression (LR) of mean deviation (MD), and visual field index (VFI).
resultsSensitivity and specificity for detecting glaucomatous VFs were 89.9% and 93.8%, respectively, for GEM and 93.0% and 97.0%, respectively, for VIM. Receiver operating characteristic (ROC) curve areas for classifying progressed eyes were 0.82 for VIM-POP, 0.86 for GEM-POP, 0.81 for PoPLR, 0.69 for LR of MD, and 0.76 for LR of VFI.
conclusionsGEM-POP was significantly more sensitive to PGON than PoPLR and linear regression of MD and VFI in our sample, while providing localized progression information. TRANSLATIONAL RELEVANCE: Detection of glaucomatous progression can be improved by assessing longitudinal changes in localized patterns of glaucomatous defect identified by unsupervised machine learning.
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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.