ArticleScientific reports2022
Deep learning approaches to predict 10-2 visual field from wide-field swept-source optical coherence tomography en face images in glaucoma.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- Effect of Signal-Strength Filtering on 3D Convolutional Neural Network-Based Visual Field Estimation From Macular OCT.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
- Visual field prediction using K-means clustering in patients with primary open angle glaucoma.International journal of ophthalmology · 2026Article
- 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
- A Practical Framework for the Integration of Structural Data Into Perimetric Examinations.Translational vision science & technology · 2024Article
- Application of artificial intelligence in glaucoma care: An updated review.Taiwan journal of ophthalmologyReview
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Close monitoring of central visual field (VF) defects with 10-2 VF helps prevent blindness in glaucoma. We aimed to develop a deep learning model to predict 10-2 VF from wide-field swept-source optical coherence tomography (SS-OCT) images. Macular ganglion cell/inner plexiform layer thickness maps with either wide-field en face images (en face model) or retinal nerve fiber layer thickness maps (RNFLT model) were extracted, combined, and preprocessed. Inception-ResNet-V2 was trained to predict 10-2 VF from combined images. Estimation performance was evaluated using mean absolute error (MAE) between actual and predicted threshold values, and the two models were compared with different input data. The training dataset comprised paired 10-2 VF and SS-OCT images of 3,025 eyes of 1,612 participants and the test dataset of 337 eyes of 186 participants. Global prediction errors (MAE
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