ArticleOphthalmology2020
Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps.
Article in Ophthalmology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers, 1 of them a synthesis that pooled it.
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Who cites it
102 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-assisted diagnosis of neuro-ophthalmic disorders: a systematic review from optic neuritis to papilledema.BMC ophthalmology · 2026Pooled it
- Current Trends in AI and Eye Disease Diagnostics.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Review
- Multimodal Deep Learning for Humphrey 24-2 Visual Field Prediction: Evaluating the Contribution of Fellow-Eye Information in Glaucoma and Ocular Hypertension.Journal of clinical medicine · 2026Article
- Fusing Structural Phenotypes with Functional Data for Early Prediction of Primary Angle-Closure Glaucoma Progression.Ophthalmology science · 2026Article
- Comparing the Use of Measured and Smoothed Data in Forecasting Visual Field Tests Using Deep Learning.Ophthalmology science · 2026Article
- Sparse-Observation Multi-Horizon Glaucoma Progression Forecasting with Biologically Constrained Temporal Consistency: A Glaucoma Case Study.Research square · 2026Article
- Predicting Visual Field Loss in Glaucoma Using OCT and Deep Learning: A Comparative Study of U-Net Variants.Ophthalmology science · 2026Article
- Retinal Ganglion Cell Degeneration in Glaucoma: Systematic Review.Bioengineering (Basel, Switzerland) · 2026Review
- Retinal nerve fibre layer optical texture analysis: retinal nerve fibre bundle defect patterns and the extent of macular involvement across different stages of glaucoma.The British journal of ophthalmology · 2026Article
- Review
- Deep Learning-Based Prediction of Visual Field Mean Deviation from Numeric OCT Data in Glaucoma.Journal of imaging informatics in 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
- Automatic Retinal Nerve Fiber Segmentation and the Influence of Intersubject Variability in Ocular Parameters on the Mapping of Retinal Sites to the Pointwise Orientation Angles.Journal of imaging · 2026Article
- Performance of General-Purpose Vision Language Models and Ophthalmology Foundation Models in Glaucoma Detection and Function Prediction.Translational vision science & technology · 2025Article
- Network architecture optimization for ophthalmic ultrasound image detection based on modular ablation of multi-version YOLO.Biomedical engineering online · 2025Article
- Artificial Intelligence Deep Learning Models to Predict Spaceflight Associated Neuro-Ocular Syndrome.American journal of ophthalmology · 2025Article
- Self-AttentionNeXt: Exploring schizophrenic optical coherence tomography image detection investigations.World journal of psychiatry · 2025Article
- Predicting Pattern Standard Deviation in Glaucoma: A Machine Learning Approach Leveraging Clinical Data.Vision (Basel, Switzerland) · 2025Article
- Automatic Algorithm-Aided Segmentation of Retinal Nerve Fibers Using Fundus Photographs.Journal of imaging · 2025Article
- Diagnostic Accuracy of 3D Deep Learning Classifiers for Glaucoma Detection: A Comparison of Cross-Domain and Device-Specific Models.Translational vision science & technology · 2025Article
42 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
purposeTo develop and evaluate a deep learning system for differentiating between eyes with and without glaucomatous visual field damage (GVFD) and predicting the severity of GFVD from spectral domain OCT (SD OCT) optic nerve head images.
designEvaluation of a diagnostic technology.
participantsA total of 9765 visual field (VF) SD OCT pairs collected from 1194 participants with and without GVFD (1909 eyes).
methodsDeep learning models were trained to use SD OCT retinal nerve fiber layer (RNFL) thickness maps, RNFL en face images, and confocal scanning laser ophthalmoscopy (CSLO) images to identify eyes with GVFD and predict quantitative VF mean deviation (MD), pattern standard deviation (PSD), and mean VF sectoral pattern deviation (PD) from SD OCT data.
main outcome measuresDeep learning models were compared with mean RNFL thickness for identifying GVFD using area under the curve (AUC), sensitivity, and specificity. For predicting MD, PSD, and mean sectoral PD, models were evaluated using R
resultsIn the independent test dataset, the deep learning models based on RNFL en face images achieved an AUC of 0.88 for identifying eyes with GVFD and 0.82 for detecting mild GVFD significantly (P < 0.001) better than using mean RNFL thickness measurements (AUC = 0.82 and 0.73, respectively). Deep learning models outperformed standard RNFL thickness measurements in predicting all quantitative VF metrics. In predicting MD, deep learning models based on RNFL en face images achieved an R
conclusionsDeep learning models had high accuracy in identifying eyes with GFVD and predicting the severity of functional loss from SD OCT images. Accurately predicting the severity of GFVD from SD OCT imaging can help clinicians more effectively individualize the frequency of VF testing to the individual patient.
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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.