ArticleInvestigative ophthalmology & visual science2017
Optical Coherence Tomography Analysis Based Prediction of Humphrey 24-2 Visual Field Thresholds in Patients With Glaucoma.
Article in Investigative ophthalmology & visual science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed, 46 citations in OpenAlex.
- 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
- Interpretable longitudinal glaucoma visual field estimation deep learning system from fundus images and clinical narratives.NPJ digital medicine · 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
- Exploring the relationship between 24-2 visual field and widefield optical coherence tomography data across healthy, glaucoma suspect and glaucoma eyes.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2024Article
- Hybrid deep learning and optimal graph search method for optical coherence tomography layer segmentation in diseases affecting the optic nerve.Biomedical optics express · 2024Article
- Characterisation of the normal human ganglion cell-inner plexiform layer using widefield optical coherence tomography.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2024Article
- Deep Learning Estimation of 10-2 Visual Field Map Based on Macular Optical Coherence Tomography Angiography Measurements.American journal of ophthalmology · 2024Article
- GRAPE: A multi-modal dataset of longitudinal follow-up visual field and fundus images for glaucoma management.Scientific data · 2023Article
- Segmentation-Free OCT-Volume-Based Deep Learning Model Improves Pointwise Visual Field Sensitivity Estimation.Translational vision science & technology · 2023Article
- Deep Learning Estimation of 10-2 Visual Field Map Based on Circumpapillary Retinal Nerve Fiber Layer Thickness Measurements.American journal of ophthalmology · 2023Article
- Prediction of visual field defects from macular optical coherence tomography in glaucoma using cluster analysis.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2022Article
- Widefield OCT Imaging for Quantifying Inner Retinal Thickness in the Nonhuman Primate.Translational vision science & technology · 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
- Visual Field Sensitivity Prediction Using Optical Coherence Tomography Analysis in Hydroxychloroquine Toxicity.Investigative ophthalmology & visual science · 2022Article
- Estimating the Severity of Visual Field Damage From Retinal Nerve Fiber Layer Thickness Measurements With Artificial Intelligence.Translational vision science & technology · 2021Article
- RetiNerveNet: using recursive deep learning to estimate pointwise 24-2 visual field data based on retinal structure.Scientific reports · 2021Article
- Visual Field Inference From Optical Coherence Tomography Using Deep Learning Algorithms: A Comparison Between Devices.Translational vision science & technology · 2021Article
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
Abstract
Purpose: A pilot study showed that prediction of individual Humphrey 24-2 visual field (HVF 24-2) sensitivity thresholds from optical coherence tomography (OCT) image analysis is possible. We evaluate performance of an improved approach as well as 3 other predictive algorithms on a new, fully independent set of glaucoma subjects. Methods: Subjects underwent HVF 24-2 and 9-field OCT (Heidelberg Spectralis) testing. Nerve fiber (NFL), and ganglion cell and inner plexiform (GCL+IPL) layers were cosegmented and partitioned into 52 sectors matching HVF 24-2 test locations. The Wilcoxon rank sum test was applied to test correlation R, root mean square error (RMSE), and limits of agreement (LoA) between actual and predicted thresholds for four prediction models. The training data consisted of the 9-field OCT and HVF 24-2 thresholds of 111 glaucoma patients from our pilot study. Results: We studied 112 subjects (112 eyes) with early, moderate, or advanced primary and secondary open angle glaucoma. Subjects with less than 9 scans (15/112) or insufficient quality segmentations (11/97) were excluded. Retinal ganglion cell axonal complex (RGC-AC) optimized had superior average R = 0.74 (95% confidence interval [CI], 0.67-0.76) and RMSE = 5.42 (95% CI, 5.1-5.7) dB, which was significantly better (P < 0.05/3) than the other three models: Naïve (R = 0.49; 95% CI, 0.44-0.54; RMSE = 7.24 dB; 95% CI, 6.6-7.8 dB), Garway-Heath (R = 0.66; 95% CI, 0.60-0.68; RMSE = 6.07 dB; 95% CI, 5.7-6.5 dB), and Donut (R = 0.67; 95% CI, 0.61-0.69; RMSE = 6.08 dB, 95% CI, 5.8-6.4 dB). Conclusions: The proposed RGC-AC optimized predictive algorithm based on 9-field OCT image analysis and the RGC-AC concept is superior to previous methods and its performance is close to the reproducibility of HVF 24-2.
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