ArticleDiagnostics (Basel, Switzerland)2022
Identifying Those at Risk of Glaucoma: A Deep Learning Approach for Optic Disc and Cup Segmentation and Their Boundary Analysis.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- The impact of eye movements on amblyopic vision: A mini-review.Vision research · 2025Review
- Guidelines for glaucoma imaging classification, annotation, and quality control for artificial intelligence applications.International journal of ophthalmology · 2025Article
- Research on segmentation model of optic disc and optic cup in fundus.BMC ophthalmology · 2024Article
- Automated vertical cup-to-disc ratio determination from fundus images for glaucoma detection.Scientific reports · 2024Article
- Validating the Generalizability of Ophthalmic Artificial Intelligence Models on Real-World Clinical Data.Translational vision science & technology · 2023Article
- Artificial Intelligence in Eye Disease: Recent Developments, Applications, and Surveys.Diagnostics (Basel, Switzerland) · 2022Article
- Highly Accurate and Precise Automated Cup-to-Disc Ratio Quantification for Glaucoma Screening.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Glaucoma is a leading cause of irreversible vision loss that gradually damages the optic nerve. In ophthalmic fundus images, measurements of the cup to optic disc (CD) ratio, CD area ratio, neuroretinal rim to optic disc (RD) area ratio, and rim thickness are key measures to screen for potential glaucomatous damage. We propose an automatic method using deep learning algorithms to segment the optic disc and cup and to estimate the key measures. The proposed method comprises three steps: The Region of Interest (ROI) (location of the optic disc) detection from a fundus image using Mask R-CNN, the optic disc and cup segmentation from the ROI using the proposed Multiscale Average Pooling Net (MAPNet), and the estimation of the key measures. Our segmentation results using 1099 fundus images show 0.9381 Jaccard Index (JI) and 0.9679 Dice Coefficient (DC) for the optic disc and 0.8222 JI and 0.8996 DC for the cup. The average CD, CD area, and RD ratio errors are 0.0451, 0.0376, and 0.0376, respectively. The average disc, cup, and rim radius ratio errors are 0.0500, 0.2257, and 0.2166, respectively. Our method performs well in estimating the key measures and shows potential to work within clinical pathways once fully implemented.
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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.