ArticleTranslational vision science & technology2023
Segmentation-Free OCT-Volume-Based Deep Learning Model Improves Pointwise Visual Field Sensitivity Estimation.
Article in Translational vision science & technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
14 citing papers in PubMed.
- 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
- Multicenter OCT-Based Visual Field Representations via Segmentation-Free 3D CNNs: Forecasting, Longitudinal Variability, and Progression Detection.Translational vision science & technology · 2026Article
- Effect of Signal-Strength Filtering on 3D Convolutional Neural Network-Based Visual Field Estimation From Macular OCT.Translational vision science & technology · 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
- Optic Nerve Atrophy Conditions Associated With 3D Unsegmented Optical Coherence Tomography Volumes Using Deep Learning.JAMA ophthalmology · 2025Article
- Automatic Algorithm-Aided Segmentation of Retinal Nerve Fibers Using Fundus Photographs.Journal of imaging · 2025Article
- Automated learning of glaucomatous visual fields from OCT images using a comprehensive, segmentation-free 3D convolutional neural network model.Scientific reports · 2025Article
- Explainable Deep Learning for Glaucomatous Visual Field Prediction: Artifact Correction Enhances Transformer Models.Translational vision science & technology · 2025Article
- Optical Coherence Tomography Versus Optic Disc Photo Assessment in Glaucoma Screening.Journal of glaucoma · 2024Review
- A Review of Gene-Property Mapping of Cementitious Materials from the Perspective of Material Genome Approach.Materials (Basel, Switzerland) · 2024Review
- Deep-Learning-Based Group Pointwise Spatial Mapping of Structure to Function in Glaucoma.Ophthalmology scienceArticle
- Applications of deep learning in the analysis of optical coherence tomography images for glaucoma-related diagnostics.Taiwan journal of ophthalmologyReview
- A Hybrid Deep Learning-Based Approach for Visual Field Test Forecasting.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Purpose: The structural changes measured by optical coherence tomography (OCT) are related to functional changes in visual fields (VFs). This study aims to accurately assess the structure-function relationship and overcome the challenges brought by the minimal measurable level (floor effect) of segmentation-dependent OCT measurements commonly used in prior studies. Methods: We developed a deep learning model to estimate the functional performance directly from three-dimensional (3D) OCT volumes and compared it to the model trained with segmentation-dependent two-dimensional (2D) OCT thickness maps. Moreover, we proposed a gradient loss to utilize the spatial information of VFs. Results: Our 3D model was significantly better than the 2D model both globally and pointwise regarding both mean absolute error (MAE = 3.11 + 3.54 vs. 3.47 ± 3.75 dB, P < 0.001) and Pearson's correlation coefficient (0.80 vs. 0.75, P < 0.001). On a subset of test data with floor effects, the 3D model showed less influence from floor effects than the 2D model (MAE = 5.24 ± 3.99 vs. 6.34 ± 4.58 dB, P < 0.001, and correlation 0.83 vs. 0.74, P < 0.001). The gradient loss improved the estimation error for low-sensitivity values. Furthermore, our 3D model outperformed all prior studies. Conclusions: By providing a better quantitative model to encapsulate the structure-function relationship more accurately, our method may help deriving VF test surrogates. Translational Relevance: DL-based VF surrogates not only benefit patients by reducing the testing time of VFs but also allow clinicians to make clinical judgments without the inherent limitations of VFs.
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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.