ArticleOphthalmology2019
From Machine to Machine: An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs.
Article in Ophthalmology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 123 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
123 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI-assisted diagnosis of neuro-ophthalmic disorders: a systematic review from optic neuritis to papilledema.BMC ophthalmology · 2026Pooled it
- Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging.Frontiers in ophthalmology · 2022Pooled it
- Current Trends in AI and Eye Disease Diagnostics.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Review
- Deriving OCT-Equivalent Retinal Nerve Fiber Layer Thickness Maps from Fundus Photographs with Deep Learning Improves Glaucoma Diagnosis.Ophthalmology science · 2026Article
- Diagnostic Likelihood Ratios for Glaucoma Using Deep Learning-Predicted Retinal Nerve Fiber Layer Thickness from Fundus Photographs.Ophthalmology science · 2026Article
- Genetic discovery using deep learning-derived optic nerve integrity phenotypes.medRxiv : the preprint server for health sciences · 2026Article
- Artificial Intelligence in Glaucoma Detection and Progression.Advances in ophthalmology and optometry · 2026Article
- Diagnostic accuracy of image-based deep learning for glaucomatous optic neuropathy detection: a systematic review and meta-analysis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Current Applications of Artificial Intelligence in Neuro-Ophthalmic Imaging: A Narrative Approach.Medical sciences (Basel, Switzerland) · 2026Review
- Optic Disc Fundus Images Retain Biometric Identity Signals Under Deep Learning.Research square · 2026Article
- Deep Learning Prediction of Personalized Peripapillary Retinal Nerve Fiber Layer Thickness Norms from Fundus Images in Glaucoma.medRxiv : the preprint server for health sciences · 2026Article
- Deep Learning-Based Generation of Retinal Nerve Fibre Layer Thickness Maps from Fundus Photographs: A Comparative Analysis of U-Net Architectures for Accessible Glaucoma Assessment.Life (Basel, Switzerland) · 2026Article
- An Artificial Intelligence Method for Phenotyping of OCT-Derived Thickness Maps Using Unsupervised and Self-supervised Deep Learning.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in Neuro-Ophthalmology for Optic Disc Pathologies and Neurodegenerative Disease.Eye and brain · 2026Review
- RNFL Thickness in a Population-Based Cohort: The Canadian Longitudinal Study on Aging M2M (Machine-to-Machine) Study.American journal of ophthalmology · 2026Article
- Artificial intelligence in ophthalmology: from innovation to clinical integration.Frontiers in ophthalmology · 2026Article
- Multifunctional nanoplatforms for optic nerve regeneration integrating anti-inflammatory, epigenetic, and ionic mechanisms with emerging artificial intelligence technologies.Cell biology and toxicology · 2025Review
- Development of a Neural Network to Predict Optimal IOP Reduction in Glaucoma Management.Vision (Basel, Switzerland) · 2025Article
- Network architecture optimization for ophthalmic ultrasound image detection based on modular ablation of multi-version YOLO.Biomedical engineering online · 2025Article
- Augmented Decisions: AI-Enhanced Accuracy in Glaucoma Diagnosis and Treatment.Journal of clinical medicine · 2025Review
63 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
purposePrevious approaches using deep learning (DL) algorithms to classify glaucomatous damage on fundus photographs have been limited by the requirement for human labeling of a reference training set. We propose a new approach using quantitative spectral-domain (SD) OCT data to train a DL algorithm to quantify glaucomatous structural damage on optic disc photographs.
designCross-sectional study.
participantsA total of 32 820 pairs of optic disc photographs and SD OCT retinal nerve fiber layer (RNFL) scans from 2312 eyes of 1198 participants.
methodsThe sample was divided randomly into validation plus training (80%) and test (20%) sets, with randomization performed at the patient level. A DL convolutional neural network was trained to assess optic disc photographs and predict SD OCT average RNFL thickness.
main outcome measuresThe DL algorithm performance was evaluated in the test sample by evaluating correlation and agreement between the predictions and actual SD OCT measurements. We also assessed the ability to discriminate eyes with glaucomatous visual field loss from healthy eyes with area under the receiver operating characteristic (ROC) curves.
resultsThe mean prediction of average RNFL thickness from all 6292 optic disc photographs in the test set was 83.3±14.5 μm, whereas the mean average RNFL thickness from all corresponding SD OCT scans was 82.5±16.8 μm (P = 0.164). There was a very strong correlation between predicted and observed RNFL thickness values (Pearson r = 0.832; R
conclusionsWe introduced a novel DL approach to assess fundus photographs and provide quantitative information about the amount of neural damage that can be used to diagnose and stage glaucoma. In addition, training neural networks to predict SD OCT data objectively represents a new approach that overcomes limitations of human labeling and could be useful in other areas of ophthalmology.
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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.