ArticleScientific reports2018
Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 papers, 5 of them syntheses that pooled it.
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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.
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Who cites it
122 citing papers in PubMed, 5 syntheses or guidelines pooled it, 324 citations in OpenAlex.
- Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature.Frontiers in medicine · 2025Pooled it
- Artificial intelligence for X-ray scaphoid fracture detection: a systematic review and diagnostic test accuracy meta-analysis.European radiology · 2024Pooled it
- Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis.Journal of medical Internet research · 2021Pooled it
- Health care cost and benefits of artificial intelligence-assisted population-based glaucoma screening for the elderly in remote areas of China: a cost-offset analysis.BMC public health · 2021Pooled it
- Machine learning applied to retinal image processing for glaucoma detection: review and perspective.Biomedical engineering online · 2020Pooled it
- Self-Supervised Feature Learning and Phenotyping for Assessing Age-Related Macular Degeneration Using Retinal Fundus Images.Ophthalmology. Retina · 2022Trial
- Beyond Accuracy: A Controlled Comparative Glaucoma Screening Benchmark Across Deep Learning and Hybrid Models Under Within- and Cross-Dataset Conditions.Journal of clinical medicine · 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
- A Semi-supervised Reinforcement Learning Framework Incorporating the Multi-scale IncepMambaNet Network for Glaucoma Progression Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Machine Learning for Coronary Heart Disease Prediction: Comparative Analysis of Framingham and Cleveland Subset of the UCI Dataset with SHAP-Based Interpretability.Epidemiologia (Basel, Switzerland) · 2026Article
- Review
- A vision transformer model for the detection of glaucoma from optic disc photographs.Scientific reports · 2026Article
- A Novel Multimodal Implementation of a Foundation Artificial Intelligence Model Using Optic Nerve Head Fundus Photographs and OCT Imaging for Glaucoma Detection.Ophthalmology science · 2026Article
- Multidimensional imaging model for diagnosing primary open-angle glaucoma using fundus photographs and optical coherence tomography images.BMJ open ophthalmology · 2025Article
- Diagnostic Accuracy of 3D Deep Learning Classifiers for Glaucoma Detection: A Comparison of Cross-Domain and Device-Specific Models.Translational vision science & technology · 2025Article
- GAINSeq: glaucoma pre-symptomatic detection using machine learning models driven by next-generation sequencing data.Scientific reports · 2025Article
- Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence.Medicina (Kaunas, Lithuania) · 2025Review
- A hybrid multi model artificial intelligence approach for glaucoma screening using fundus images.NPJ digital medicine · 2025Article
- Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis.Biomedicines · 2025Review
- Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning.Scientific reports · 2025Article
62 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
The ability of deep learning architectures to identify glaucomatous optic neuropathy (GON) in fundus photographs was evaluated. A large database of fundus photographs (n = 14,822) from a racially and ethnically diverse group of individuals (over 33% of African descent) was evaluated by expert reviewers and classified as GON or healthy. Several deep learning architectures and the impact of transfer learning were evaluated. The best performing model achieved an overall area under receiver operating characteristic (AUC) of 0.91 in distinguishing GON eyes from healthy eyes. It also achieved an AUC of 0.97 for identifying GON eyes with moderate-to-severe functional loss and 0.89 for GON eyes with mild functional loss. A sensitivity of 88% at a set 95% specificity was achieved in detecting moderate-to-severe GON. In all cases, transfer improved performance and reduced training time. Model visualizations indicate that these deep learning models relied on, in part, anatomical features in the inferior and superior regions of the optic disc, areas commonly used by clinicians to diagnose GON. The results suggest that deep learning-based assessment of fundus images could be useful in clinical decision support systems and in the automation of large-scale glaucoma detection and screening programs.
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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.