ArticlePloS one2019
Forecasting future Humphrey Visual Fields using deep learning.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 1 of them a synthesis that pooled it.
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Who cites it
86 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application.Current diabetes reports · 2019Pooled it
- Artificial intelligence in ophthalmology: From diagnostic accuracy to clinical application.World journal of methodology · 2026Review
- Comparing the Use of Measured and Smoothed Data in Forecasting Visual Field Tests Using Deep Learning.Ophthalmology science · 2026Article
- Toward Long-Term Visual Field Appearance Forecasting Using Artificial Intelligence for Ophthalmic Education and Diagnosis.Ophthalmology science · 2026Article
- Accurate Prediction of Humphrey 10-2 Visual Fields in Glaucoma from a Single Rapid IMOvifa 24plus(1-2).Ophthalmology science · 2026Article
- Predicting worsening visual field via masked dual stage attention recurrent neural network.Scientific reports · 2026Article
- A systematic review of AI for predicting glaucoma progression: challenges and recommendations towards clinical implementation.NPJ digital medicine · 2026Article
- Predicting HFA 30-2 Visual Fields with Deep Learning from Multimodal OCT-Fundus Feature Fusion and Structure-Function Discordance Analysis.Journal of imaging informatics in 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
- Interpretable longitudinal glaucoma visual field estimation deep learning system from fundus images and clinical narratives.NPJ digital medicine · 2025Article
- The Role of Artificial Intelligence in Predicting the Progression of Intraocular Hypertension to Glaucoma.Life (Basel, Switzerland) · 2025Article
- Lights and Shadows on Artificial Intelligence in Glaucoma: Transforming Screening, Monitoring, and Prognosis.Journal of clinical medicine · 2025Review
- Current perspectives in tackling glaucoma blindness.Indian journal of ophthalmology · 2025Review
- Bibliometric analysis of research on the application of deep learning to ophthalmology.Quantitative imaging in medicine and surgery · 2025Article
- Predicting progressive vision loss in glaucoma patients using functional principal component analysis and electronic health records.Frontiers in ophthalmology · 2025Article
- Tackling visual impairment: emerging avenues in ophthalmology.Frontiers in medicine · 2025Review
- Article
- Diagnostic Capability of Pattern Electroretinogram and Three Circumpapillary Retinal Nerve Fiber Layer Thickness Circle Diameter Scans in Glaucoma Suspects.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Dual U-Net with multi-task attention for automated eyelid curvature quantification.Frontiers in medicine · 2025Article
- Applications of Artificial Intelligence in Ophthalmology: Glaucoma, Cornea, and Oculoplastics.Cureus · 2024Review
26 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
Abstract
purposeTo determine if deep learning networks could be trained to forecast future 24-2 Humphrey Visual Fields (HVFs).
methodsAll data points from consecutive 24-2 HVFs from 1998 to 2018 were extracted from a university database. Ten-fold cross validation with a held out test set was used to develop the three main phases of model development: model architecture selection, dataset combination selection, and time-interval model training with transfer learning, to train a deep learning artificial neural network capable of generating a point-wise visual field prediction. The point-wise mean absolute error (PMAE) and difference in Mean Deviation (MD) between predicted and actual future HVF were calculated.
resultsMore than 1.7 million perimetry points were extracted to the hundredth decibel from 32,443 24-2 HVFs. The best performing model with 20 million trainable parameters, CascadeNet-5, was selected. The overall point-wise PMAE for the test set was 2.47 dB (95% CI: 2.45 dB to 2.48 dB), and deep learning showed a statistically significant improvement over linear models. The 100 fully trained models successfully predicted future HVFs in glaucomatous eyes up to 5.5 years in the future with a correlation of 0.92 between the MD of predicted and actual future HVF and an average difference of 0.41 dB.
conclusionsUsing unfiltered real-world datasets, deep learning networks show the ability to not only learn spatio-temporal HVF changes but also to generate predictions for future HVFs up to 5.5 years, given only a single HVF.
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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.