ArticleScientific reports2023
Deep learning visual field global index prediction with optical coherence tomography parameters in glaucoma patients.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 12 citations in OpenAlex.
- Predicting Visual Field Loss in Glaucoma Using OCT and Deep Learning: A Comparative Study of U-Net Variants.Ophthalmology science · 2026Article
- Retinal Ganglion Cell Degeneration in Glaucoma: Systematic Review.Bioengineering (Basel, Switzerland) · 2026Review
- Deep Learning-Based Prediction of Visual Field Mean Deviation from Numeric OCT Data in Glaucoma.Journal of imaging informatics in medicine · 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
- Predicting Pattern Standard Deviation in Glaucoma: A Machine Learning Approach Leveraging Clinical Data.Vision (Basel, Switzerland) · 2025Article
- Interpretable Machine Learning Predictions of Bruch's Membrane Opening-Minimum Rim Width Using Retinal Nerve Fiber Layer Values and Visual Field Global Indexes.Bioengineering (Basel, Switzerland) · 2025Article
- Predicting visual field global and local parameters from OCT measurements using explainable machine learning.Scientific reports · 2025Article
- Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis.Biomedicines · 2025Review
- Application of artificial intelligence in glaucoma care: An updated review.Taiwan journal of ophthalmologyReview
- Application of Artificial Intelligence in Ophthalmology: An Updated Comprehensive Review.Journal of ophthalmic & vision researchReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to predict three visual filed (VF) global indexes, mean deviation (MD), pattern standard deviation (PSD), and visual field index (VFI), from optical coherence tomography (OCT) parameters including Bruch's Membrane Opening-Minimum Rim Width (BMO-MRW) and retinal nerve fiber layer (RNFL) based on a deep-learning model. Subjects consisted of 224 eyes with Glaucoma suspects (GS), 245 eyes with early NTG, 58 eyes with moderate stage of NTG, 36 eyes with PACG, 57 eyes with PEXG, and 99 eyes with POAG. A deep neural network (DNN) algorithm was developed to predict values of VF global indexes such as MD, VFI, and PSD. To evaluate performance of the model, mean absolute error (MAE) was determined. The MAE range of the DNN model on cross validation was 1.9-2.9 (dB) for MD, 1.6-2.0 (dB) for PSD, and 5.0 to 7.0 (%) for VFI. Ranges of Pearson's correlation coefficients were 0.76-0.85, 0.74-0.82, and 0.70-0.81 for MD, PSD, and VFI, respectively. Our deep-learning model might be useful in the management of glaucoma for diagnosis and follow-up, especially in situations when immediate VF results are not available because VF test requires time and space with a subjective nature.
Indexed as
Identifiers
What OpenQuestion holds
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.