ArticleTranslational vision science & technology2022
Pointwise Visual Field Estimation From Optical Coherence Tomography in Glaucoma Using Deep Learning.
Article in Translational vision science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- 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
- 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
- Artificial Intelligence in Glaucoma Detection and Progression.Advances in ophthalmology and optometry · 2026Article
- Multicenter OCT-Based Visual Field Representations via Segmentation-Free 3D CNNs: Forecasting, Longitudinal Variability, and Progression Detection.Translational vision science & technology · 2026Article
- Deep learning-based prediction of 24-2 visual field from fundus photographs in glaucoma.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Predicting Pattern Standard Deviation in Glaucoma: A Machine Learning Approach Leveraging Clinical Data.Vision (Basel, Switzerland) · 2025Article
- Interpretable longitudinal glaucoma visual field estimation deep learning system from fundus images and clinical narratives.NPJ digital medicine · 2025Article
- Automated learning of glaucomatous visual fields from OCT images using a comprehensive, segmentation-free 3D convolutional neural network model.Scientific reports · 2025Article
- Predicting visual field global and local parameters from OCT measurements using explainable machine learning.Scientific reports · 2025Article
- Detecting glaucoma worsening using optical coherence tomography derived visual field estimates.Scientific reports · 2025Article
- Explainable Deep Learning for Glaucomatous Visual Field Prediction: Artifact Correction Enhances Transformer Models.Translational vision science & technology · 2025Article
- Development and validation of CNN-MLP models for predicting anti-VEGF therapy outcomes in diabetic macular edema.Scientific reports · 2024Article
- [Awareness for glaucoma in the general population].Die Ophthalmologie · 2023Review
- Spatial Summation in the Glaucomatous Macula: A Link With Retinal Ganglion Cell Damage.Investigative ophthalmology & visual science · 2023Article
- Deep learning visual field global index prediction with optical coherence tomography parameters in glaucoma patients.Scientific reports · 2023Article
- Comparison of machine learning approaches for structure-function modeling in glaucoma.Annals of the New York Academy of Sciences · 2022Article
- Big data in visual field testing for glaucoma.Taiwan journal of ophthalmologyReview
- Big data for imaging assessment in glaucoma.Taiwan journal of ophthalmologyReview
- Application of artificial intelligence in glaucoma care: An updated review.Taiwan journal of ophthalmologyReview
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Standard automated perimetry is the gold standard to monitor visual field (VF) loss in glaucoma management, but it is prone to intrasubject variability. We trained and validated a customized deep learning (DL) regression model with Xception backbone that estimates pointwise and overall VF sensitivity from unsegmented optical coherence tomography (OCT) scans. Methods: DL regression models have been trained with four imaging modalities (circumpapillary OCT at 3.5 mm, 4.1 mm, and 4.7 mm diameter) and scanning laser ophthalmoscopy en face images to estimate mean deviation (MD) and 52 threshold values. This retrospective study used data from patients who underwent a complete glaucoma examination, including a reliable Humphrey Field Analyzer (HFA) 24-2 SITA Standard (SS) VF exam and a SPECTRALIS OCT. Results: For MD estimation, weighted prediction averaging of all four individuals yielded a mean absolute error (MAE) of 2.89 dB (2.50-3.30) on 186 test images, reducing the baseline by 54% (MAEdecr%). For 52 VF threshold values' estimation, the weighted ensemble model resulted in an MAE of 4.82 dB (4.45-5.22), representing an MAEdecr% of 38% from baseline when predicting the pointwise mean value. DL managed to explain 75% and 58% of the variance (R2) in MD and pointwise sensitivity estimation, respectively. Conclusions: Deep learning can estimate global and pointwise VF sensitivities that fall almost entirely within the 90% test-retest confidence intervals of the 24-2 SS test. Translational Relevance: Fast and consistent VF prediction from unsegmented OCT scans could become a solution for visual function estimation in patients unable to perform reliable VF exams.
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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.