ArticleAmerican journal of ophthalmology2019
A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs.
Article in American journal of ophthalmology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis 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
57 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging.Frontiers in ophthalmology · 2022Pooled it
- 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
- Current Applications of Artificial Intelligence in Neuro-Ophthalmic Imaging: A Narrative Approach.Medical sciences (Basel, Switzerland) · 2026Review
- RNFL Thickness in a Population-Based Cohort: The Canadian Longitudinal Study on Aging M2M (Machine-to-Machine) Study.American journal of ophthalmology · 2026Article
- Novel Deep Learning Model for Glaucoma Detection Using Fusion of Fundus and Optical Coherence Tomography Images.Sensors (Basel, Switzerland) · 2025Article
- Prospective pragmatic trial of automated retinal photography and AI glaucoma screening in Australian primary care.NPJ digital medicine · 2025Article
- Evaluating Fundoscopy as a Screening Tool for Optic Nerve Atrophy in Multiple Sclerosis: An Optical Coherence Tomography (OCT) Comparative Study.Journal of clinical medicine · 2025Article
- Lights and Shadows on Artificial Intelligence in Glaucoma: Transforming Screening, Monitoring, and Prognosis.Journal of clinical medicine · 2025Review
- 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
- Artificial intelligence virtual assistants in primary eye care practice.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2025Article
- Artificial intelligence in ophthalmology: opportunities, challenges, and ethical considerations.Medical hypothesis, discovery & innovation ophthalmology journal · 2025Review
- Tackling visual impairment: emerging avenues in ophthalmology.Frontiers in medicine · 2025Review
- Artificial Intelligence and Advanced Technology in Glaucoma: A Review.Journal of personalized medicine · 2024Review
- Optical Coherence Tomography Versus Optic Disc Photo Assessment in Glaucoma Screening.Journal of glaucoma · 2024Review
- Diagnosing glaucoma in primary eye care and the role of Artificial Intelligence applications for reducing the prevalence of undetected glaucoma in Australia.Eye (London, England) · 2024Review
- Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection.Bioengineering (Basel, Switzerland) · 2024Review
- The utilization of artificial intelligence in glaucoma: diagnosis versus screening.Frontiers in ophthalmology · 2024Review
- Multi-Dataset Comparison of Vision Transformers and Convolutional Neural Networks for Detecting Glaucomatous Optic Neuropathy from Fundus Photographs.Bioengineering (Basel, Switzerland) · 2023Article
- Identifying, Understanding, and Addressing Disparities in Glaucoma Care in the United States.Translational vision science & technology · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
purposeTo train a deep learning (DL) algorithm that quantifies glaucomatous neuroretinal damage on fundus photographs using the minimum rim width relative to Bruch membrane opening (BMO-MRW) from spectral-domain optical coherence tomography (SDOCT).
designCross-sectional study.
methodsA total of 9282 pairs of optic disc photographs and SDOCT optic nerve head scans from 927 eyes of 490 subjects were randomly divided into the validation plus training (80%) and test sets (20%). A DL convolutional neural network was trained to predict the SDOCT BMO-MRW global and sector values when evaluating optic disc photographs. The predictions of the DL network were compared to the actual SDOCT measurements. The area under the receiver operating curve (AUC) was used to evaluate the ability of the network to discriminate glaucomatous visual field loss from normal eyes.
resultsThe DL predictions of global BMO-MRW from all optic disc photographs in the test set (mean ± standard deviation [SD]: 228.8 ± 63.1 μm) were highly correlated with the observed values from SDOCT (mean ± SD: 226.0 ± 73.8 μm) (Pearson's r = 0.88; R
conclusionsA DL network can be trained to quantify the amount of neuroretinal damage on optic disc photographs using SDOCT BMO-MRW as a reference. This algorithm showed high accuracy for glaucoma detection, and may potentially eliminate the need for human gradings of disc photographs.
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