ReviewDiagnostics (Basel, Switzerland)2023
Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
18 citing papers in PubMed, 84 citations in OpenAlex.
- 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
- Deep Feature-Based Normality Modeling for Automated Out-Of-Distribution Detection in Sheep Retinal Fundus Images.Veterinary ophthalmology · 2026Article
- A Symmetric Encoder-Decoder Network with Enhanced Group-Shuffle Modules for Robust Lung Nodule Detection in CT Scans.Biomimetics (Basel, Switzerland) · 2026Article
- Combined diagnostic accuracy of two artificial intelligence systems for glaucoma diagnosis using color fundus photography.PloS one · 2026Article
- Why aren't we using AI in eye clinics? A systematic review of barriers and solutions in AI-based fundus image diagnostics for ocular diseases.International journal of ophthalmology · 2026Review
- GAINSeq: glaucoma pre-symptomatic detection using machine learning models driven by next-generation sequencing data.Scientific reports · 2025Article
- CAD-Skin: A Hybrid Convolutional Neural Network-Autoencoder Framework for Precise Detection and Classification of Skin Lesions and Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- A hybrid multi model artificial intelligence approach for glaucoma screening using fundus images.NPJ digital medicine · 2025Article
- A generalised computer vision model for improved glaucoma screening using fundus images.Eye (London, England) · 2025Article
- Clinical performance of an interactive platform based on artificial intelligence in ophthalmology: experience in a third-level reference center.Frontiers in medicine · 2025Article
- DeSPPNet: A Multiscale Deep Learning Model for Cardiac Segmentation.Diagnostics (Basel, Switzerland) · 2024Article
- Deep Learning Evaluation of Glaucoma Detection Using Fundus Photographs in Highly Myopic Populations.Biomedicines · 2024Article
- Automated Classification of Physiologic, Glaucomatous, and Glaucoma-Suspected Optic Discs Using Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Advancements in Glaucoma Diagnosis: The Role of AI in Medical Imaging.Diagnostics (Basel, Switzerland) · 2024Review
- Computational methods in glaucoma research: Current status and future outlook.Molecular aspects of medicine · 2023Review
- MSRNet: Multiclass Skin Lesion Recognition Using Additional Residual Block Based Fine-Tuned Deep Models Information Fusion and Best Feature Selection.Diagnostics (Basel, Switzerland) · 2023Article
- Agreement between Five Experts and the Laguna ONhE Automatic Colourimetric Application Interpreting the Glaucomatous Aspect of the Optic Nerve.Journal of clinical medicine · 2023Article
- Evaluating a Foundation Artificial Intelligence Model for Glaucoma Detection Using Color Fundus Photographs.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
6 authors at 2 institutions in 1 country.
Funding
Abstract
Glaucoma is a chronic eye disease that may lead to permanent vision loss if it is not diagnosed and treated at an early stage. The disease originates from an irregular behavior in the drainage flow of the eye that eventually leads to an increase in intraocular pressure, which in the severe stage of the disease deteriorates the optic nerve head and leads to vision loss. Medical follow-ups to observe the retinal area are needed periodically by ophthalmologists, who require an extensive degree of skill and experience to interpret the results appropriately. To improve on this issue, algorithms based on deep learning techniques have been designed to screen and diagnose glaucoma based on retinal fundus image input and to analyze images of the optic nerve and retinal structures. Therefore, the objective of this paper is to provide a systematic analysis of 52 state-of-the-art relevant studies on the screening and diagnosis of glaucoma, which include a particular dataset used in the development of the algorithms, performance metrics, and modalities employed in each article. Furthermore, this review analyzes and evaluates the used methods and compares their strengths and weaknesses in an organized manner. It also explored a wide range of diagnostic procedures, such as image pre-processing, localization, classification, and segmentation. In conclusion, automated glaucoma diagnosis has shown considerable promise when deep learning algorithms are applied. Such algorithms could increase the accuracy and efficiency of glaucoma diagnosis in a better and faster manner.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.