ArticleOphthalmology science2023
Characteristics of a Large, Labeled Data Set for the Training of Artificial Intelligence for Glaucoma Screening with Fundus Photographs.
Article in Ophthalmology science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07668193 (GlaukomAI), which is not on this map. Cited by 9 papers.
What it found
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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.
GlaukomAI: Clinical Validation of an Artificial Intelligence-Based System for Early Glaucoma Screening and Diagnosis - A Case-Control Study and Referral Accuracy Assessment
Who cites it
9 citing papers in PubMed, 32 citations in OpenAlex.
- Improving Generalization of Deep Learning for Glaucoma Classification Under Real-World Domain Shift via Unsupervised Domain Adaptation.Translational vision science & technology · 2026Article
- JustRAIGS: Justified Referral in AI Glaucoma Screening Challenge.IEEE transactions on medical imaging · 2026Article
- Benchmarking Vision Encoders for Image Classification in Ophthalmology.Computational and structural biotechnology journal · 2026Article
- The Role of Artificial Intelligence in Predicting the Progression of Intraocular Hypertension to Glaucoma.Life (Basel, Switzerland) · 2025Article
- 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
- Assessing the documentation of publicly available medical image and signal datasets and their impact on bias using the BEAMRAD tool.Scientific reports · 2024Article
- Artificial intelligence and glaucoma: a lucid and comprehensive review.Frontiers in medicine · 2024Review
- Highly Accurate and Precise Automated Cup-to-Disc Ratio Quantification for Glaucoma Screening.Ophthalmology scienceArticle
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 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Significant visual impairment due to glaucoma is largely caused by the disease being detected too late. Objective: To build a labeled data set for training artificial intelligence (AI) algorithms for glaucoma screening by fundus photography, to assess the accuracy of the graders, and to characterize the features of all eyes with referable glaucoma (RG). Design: Cross-sectional study. Subjects: Color fundus photographs (CFPs) of 113 893 eyes of 60 357 individuals were obtained from EyePACS, California, United States, from a population screening program for diabetic retinopathy. Methods: Carefully selected graders (ophthalmologists and optometrists) graded the images. To qualify, they had to pass the European Optic Disc Assessment Trial optic disc assessment with ≥ 85% accuracy and 92% specificity. Of 90 candidates, 30 passed. Each image of the EyePACS set was then scored by varying random pairs of graders as "RG," "no referable glaucoma (NRG)," or "ungradable (UG)." In case of disagreement, a glaucoma specialist made the final grading. Referable glaucoma was scored if visual field damage was expected. In case of RG, graders were instructed to mark up to 10 relevant glaucomatous features. Main Outcome Measures: Qualitative features in eyes with RG. Results: The performance of each grader was monitored; if the sensitivity and specificity dropped below 80% and 95%, respectively (the final grade served as reference), they exited the study and their gradings were redone by other graders. In all, 20 graders qualified; their mean sensitivity and specificity (standard deviation [SD]) were 85.6% (5.7) and 96.1% (2.8), respectively. The 2 graders agreed in 92.45% of the images (Gwet's AC2, expressing the inter-rater reliability, was 0.917). Of all gradings, the sensitivity and specificity (95% confidence interval) were 86.0 (85.2-86.7)% and 96.4 (96.3-96.5)%, respectively. Of all gradable eyes ( Conclusions: A large data set of CFPs was put together of sufficient quality to develop AI screening solutions for glaucoma. The most common features of RG were the appearance of the NRR inferiorly and superiorly. Disc hemorrhages were a rare feature of RG. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.
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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.