ReviewEye (London, England)2020
The impact of artificial intelligence in the diagnosis and management of glaucoma.
Review in Eye (London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The role of artificial intelligence in analysis of biofluid markers for diagnosis and management of glaucoma: A systematic review.European journal of ophthalmology · 2023Pooled it
- Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis.Journal of medical Internet research · 2021Pooled it
- Data Volume and the Need for Clinical Decision Support in Glaucoma Care.Ophthalmology science · 2026Article
- An extensive analysis of machine learning techniques for identifying glaucoma.International ophthalmology · 2025Review
- [Artificial intelligence in medicine-Opportunities and risks from an ethical perspective].Die Ophthalmologie · 2025Review
- Validation of a Novel Low-Cost Glaucoma Risk Calculator for Community-Based Screening in High-Risk Populations.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Glaucoma detection and staging from visual field images using machine learning techniques.PloS one · 2025Article
- RDLR: A Robust Deep Learning-Based Image Registration Method for Pediatric Retinal Images.Journal of imaging informatics in medicine · 2024Article
- Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence.Life (Basel, Switzerland) · 2024Review
- Article
- Automated analysis for glaucoma screening of retinal videos acquired with smartphone-based ophthalmoscope.Heliyon · 2024Article
- Evaluation of an offline, artificial intelligence system for referable glaucoma screening using a smartphone-based fundus camera: a prospective study.Eye (London, England) · 2024Article
- External validation of a deep learning detection system for glaucomatous optic neuropathy: a real-world multicentre study.Eye (London, England) · 2023Article
- Uncertainty-inspired open set learning for retinal anomaly identification.Nature communications · 2023Article
- Artificial intelligence in ophthalmology.International journal of ophthalmology · 2023Article
- Estimating the Length of the Preclinical Detectable Phase for Open-Angle Glaucoma.JAMA ophthalmology · 2023Article
- Application of diffusion tensor imaging technology in glaucoma diagnosis.Frontiers in neuroscience · 2023Review
- Detection of Glaucoma on Fundus Images Using Deep Learning on a New Image Set Obtained with a Smartphone and Handheld Ophthalmoscope.Healthcare (Basel, Switzerland) · 2022Article
- Measurement of retinal nerve fiber layer thickness with a deep learning algorithm in ischemic optic neuropathy and optic neuritis.Scientific reports · 2022Article
- Glaucoma Detection Using Support Vector Machine Method Based on Spectralis OCT.Diagnostics (Basel, Switzerland) · 2022Article
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.
Funding
Abstract
Deep learning (DL) is a subset of artificial intelligence (AI), which uses multilayer neural networks modelled after the mammalian visual cortex capable of synthesizing images in ways that will transform the field of glaucoma. Autonomous DL algorithms are capable of maximizing information embedded in digital fundus photographs and ocular coherence tomographs to outperform ophthalmologists in disease detection. Other unsupervised algorithms such as principal component analysis (axis learning) and archetypal analysis (corner learning) facilitate visual field interpretation and show great promise to detect functional glaucoma progression and differentiate it from non-glaucomatous changes when compared with conventional software packages. Forecasting tools such as the Kalman filter may revolutionize glaucoma management by accounting for a host of factors to set target intraocular pressure goals that preserve vision. Activation maps generated from DL algorithms that process glaucoma data have the potential to efficiently direct our attention to critical data elements embedded in high throughput data and enhance our understanding of the glaucomatous process. It is hoped that AI will realize more accurate assessment of the copious data encountered in glaucoma management, improving our understanding of the disease, preserving vision, and serving to enhance the deep bonds that patients develop with their treating physicians.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.