ArticleScientific reports2019
Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma Using a Deep Variational Autoencoder.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Effect of Visual Crowding on Quality of Life in Glaucoma Patients.Investigative ophthalmology & visual science · 2026Article
- Artificial Intelligence in Ophthalmology: Current Status, Challenges, and Future Perspectives.Health care science · 2026Review
- Review
- A systematic review of AI for predicting glaucoma progression: challenges and recommendations towards clinical implementation.NPJ digital medicine · 2026Article
- Reliable and Interpretable Visual Field Progression Prediction with Diffusion Models and Conformal Risk Control.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- A complete spatial map of mouse retinal ganglion cells reveals density and gene expression specializations.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Correlation of autoantibody profiles with clinical parameters in exfoliative glaucoma patients.International ophthalmology · 2025Article
- Lights and Shadows on Artificial Intelligence in Glaucoma: Transforming Screening, Monitoring, and Prognosis.Journal of clinical medicine · 2025Review
- Generative AI Models in Time-Varying Biomedical Data: Scoping Review.Journal of medical Internet research · 2025Article
- Former smoking as a risk factor for visual field progression in exfoliation glaucoma patients in Sweden.European journal of ophthalmology · 2024Article
- Estimating the Distribution of True Rates of Visual Field Progression in Glaucoma.Translational vision science & technology · 2024Article
- Ocular Pathology and Genetics: Transformative Role of Artificial Intelligence (AI) in Anterior Segment Diseases.Cureus · 2024Review
- Visualization of Optic Nerve Structural Patterns in Papilledema Using Deep Learning Variational Autoencoders.Translational vision science & technology · 2024Article
- Quantifying the spatial patterns of retinal ganglion cell loss and progression in optic neuropathy by applying a deep learning variational autoencoder approach to optical coherence tomography.Frontiers in ophthalmology · 2024Article
- Bidirectional gated recurrent unit network model can generate future visual field with variable number of input elements.PloS one · 2024Article
- Predicting glaucoma progression using deep learning framework guided by generative algorithm.Scientific reports · 2023Article
- Validating Trend-Based End Points for Neuroprotection Trials in Glaucoma.Translational vision science & technology · 2023Article
- Prediction of visual field progression in glaucoma: existing methods and artificial intelligence.Japanese journal of ophthalmology · 2023Review
- Visual field prediction using a deep bidirectional gated recurrent unit network model.Scientific reports · 2023Article
- Bidirectional Neural Network Model for Glaucoma Progression Prediction.Journal of personalized medicine · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In this manuscript we develop a deep learning algorithm to improve estimation of rates of progression and prediction of future patterns of visual field loss in glaucoma. A generalized variational auto-encoder (VAE) was trained to learn a low-dimensional representation of standard automated perimetry (SAP) visual fields using 29,161 fields from 3,832 patients. The VAE was trained on a 90% sample of the data, with randomization at the patient level. Using the remaining 10%, rates of progression and predictions were generated, with comparisons to SAP mean deviation (MD) rates and point-wise (PW) regression predictions, respectively. The longitudinal rate of change through the VAE latent space (e.g., with eight dimensions) detected a significantly higher proportion of progression than MD at two (25% vs. 9%) and four (35% vs 15%) years from baseline. Early on, VAE improved prediction over PW, with significantly smaller mean absolute error in predicting the 4
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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.