ArticleTranslational vision science & technology2021
Visual Field Inference From Optical Coherence Tomography Using Deep Learning Algorithms: A Comparison Between Devices.
Article in Translational vision science & technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- 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
- Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis.Biomedicines · 2025Review
- Explainable Deep Learning for Glaucomatous Visual Field Prediction: Artifact Correction Enhances Transformer Models.Translational vision science & technology · 2025Article
- [Visual field prediction based on temporal-spatial feature learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Segmentation-Free OCT-Volume-Based Deep Learning Model Improves Pointwise Visual Field Sensitivity Estimation.Translational vision science & technology · 2023Article
- Deep learning approaches to predict 10-2 visual field from wide-field swept-source optical coherence tomography en face images in glaucoma.Scientific reports · 2022Article
- Pointwise Visual Field Estimation From Optical Coherence Tomography in Glaucoma Using Deep Learning.Translational vision science & technology · 2022Article
- Policy-Driven, Multimodal Deep Learning for Predicting Visual Fields from the Optic Disc and OCT Imaging.Ophthalmology · 2022Article
- Comparison between Deep-Learning-Based Ultra-Wide-Field Fundus Imaging and True-Colour Confocal Scanning for Diagnosing Glaucoma.Journal of clinical medicine · 2022Article
- Emerging imaging developments in experimental vision sciences and ophthalmology.Experimental biology and medicine (Maywood, N.J.) · 2021Article
- Big data in visual field testing for glaucoma.Taiwan journal of ophthalmologyReview
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To develop a deep learning model to estimate the visual field (VF) from spectral-domain optical coherence tomography (SD-OCT) and swept-source OCT (SS-OCT) and to compare the performance between them. Methods: Two deep learning models based on Inception-ResNet-v2 were trained to estimate 24-2 VF from SS-OCT and SD-OCT images. The estimation performance of the two models was evaluated by using the root mean square error between the actual and estimated VF. The performance was also compared among different glaucoma severities, Garway-Heath sectorizations, and central/peripheral regions. Results: The training dataset comprised images of 4391 eyes from 2350 subjects, and the test dataset was obtained from another 243 subjects (243 eyes). In all subjects, the global estimation errors were 5.29 ± 2.68 dB (SD-OCT) and 4.51 ± 2.54 dB (SS-OCT), and the estimation error of SS-OCT was significantly lower than that of SD-OCT (P < 0.001). In the analysis of sectors, SS-OCT showed better performance in all sectors except for the inferonasal sector in normal vision and early glaucoma. In advanced glaucoma, the estimation error of the central region was worsened in both OCTs, but SS-OCT was still significantly better in the peripheral region. Conclusions: Our deep learning model estimated the VF 24-2 better with a wide field image of SS-OCT than did with retinal nerve fiber layer and ganglion cell-inner plexiform layer images of SD-OCT. Translational Relevance: This deep learning method can help clinicians to determine the VF from OCT images. OCT manufacturers can equip this system to provide additional VF data.
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