ArticlePloS one2021
Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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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
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- Fusing Structural Phenotypes with Functional Data for Early Prediction of Primary Angle-Closure Glaucoma Progression.Ophthalmology science · 2026Article
- A systematic review of AI for predicting glaucoma progression: challenges and recommendations towards clinical implementation.NPJ digital medicine · 2026Article
- Visual field prediction using K-means clustering in patients with primary open angle glaucoma.International journal of ophthalmology · 2026Article
- Multimodal AI-based modeling of glaucoma progression: a 3PM-guided approach integrating structural, functional, and vascular patterns.The EPMA journal · 2025Article
- Dual-Level Pattern Tree for Visual Field Improves Glaucoma Progression and Polygenic Risk Prediction.Translational vision science & technology · 2025Article
- An extensive analysis of machine learning techniques for identifying glaucoma.International ophthalmology · 2025Review
- The Impact of Myopia on Regional Visual Field Loss and Progression in Glaucoma.Translational vision science & technology · 2025Article
- Predicting Pattern Standard Deviation in Glaucoma: A Machine Learning Approach Leveraging Clinical Data.Vision (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Glaucoma: Advances in Diagnosis, Progression Forecasting, and Surgical Outcome Prediction.International journal of molecular sciences · 2025Review
- Cognitive visual strategies are associated with delivery accuracy in elite wheelchair curling: insights from eye-tracking and machine learning.Frontiers in psychology · 2025Article
- The use of artificial neural networks in studying the progression of glaucoma.Scientific reports · 2024Article
- Integrating Deep Learning with Electronic Health Records for Early Glaucoma Detection: A Multi-Dimensional Machine Learning Approach.Bioengineering (Basel, Switzerland) · 2024Article
- Retinal Nerve Fiber Layer Damage Assessment in Glaucomatous Eyes Using Retinal Retardance Measured by Polarization-Sensitive Optical Coherence Tomography.Translational vision science & technology · 2024Article
- Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening.Medicina (Kaunas, Lithuania) · 2024Review
- Deep learning-based identification of eyes at risk for glaucoma surgery.Scientific reports · 2024Article
- Predicting glaucoma progression using deep learning framework guided by generative algorithm.Scientific reports · 2023Article
- Detecting disease progression in mild, moderate and severe glaucoma.Current opinion in ophthalmology · 2023Review
- A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach.Scientific reports · 2023Article
- Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications.Frontiers in ophthalmology · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
objectiveTo assess whether machine learning algorithms (MLA) can predict eyes that will undergo rapid glaucoma progression based on an initial visual field (VF) test.
designRetrospective analysis of longitudinal data. SUBJECTS: 175,786 VFs (22,925 initial VFs) from 14,217 patients who completed ≥5 reliable VFs at academic glaucoma centers were included.
methodsSummary measures and reliability metrics from the initial VF and age were used to train MLA designed to predict the likelihood of rapid progression. Additionally, the neural network model was trained with point-wise threshold data in addition to summary measures, reliability metrics and age. 80% of eyes were used for a training set and 20% were used as a test set. MLA test set performance was assessed using the area under the receiver operating curve (AUC). Performance of models trained on initial VF data alone was compared to performance of models trained on data from the first two VFs.
main outcome measuresAccuracy in predicting future rapid progression defined as MD worsening more than 1 dB/year.
results1,968 eyes (8.6%) underwent rapid progression. The support vector machine model (AUC 0.72 [95% CI 0.70-0.75]) most accurately predicted rapid progression when trained on initial VF data. Artificial neural network, random forest, logistic regression and naïve Bayes classifiers produced AUC of 0.72, 0.70, 0.69, 0.68 respectively. Models trained on data from the first two VFs performed no better than top models trained on the initial VF alone. Based on the odds ratio (OR) from logistic regression and variable importance plots from the random forest model, older age (OR: 1.41 per 10 year increment [95% CI: 1.34 to 1.08]) and higher pattern standard deviation (OR: 1.31 per 5-dB increment [95% CI: 1.18 to 1.46]) were the variables in the initial VF most strongly associated with rapid progression.
conclusionsMLA can be used to predict eyes at risk for rapid progression with modest accuracy based on an initial VF test. Incorporating additional clinical data to the current model may offer opportunities to predict patients most likely to rapidly progress with even greater accuracy.
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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.