SynthesisBiomedical engineering online2023
Artificial intelligence in glaucoma: opportunities, challenges, and future directions.
Synthesis in Biomedical engineering online, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis 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
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Multimodal artificial intelligence in retinal vascular and neovascular macular diseases: a systematic review of diagnostic and prognostic applications.BMC ophthalmology · 2025Pooled it
- Machine Learning-Based Prediction of Strabismus with and without Amblyopia from Fixation Eye Movements.Ophthalmology science · 2026Article
- Establishment of the China Elderly Comorbidity Medical Database (CECMed) and its application in machine learning-based prediction.BMC geriatrics · 2026Article
- Automated Report Generation in Ophthalmology: Integrating Artificial Intelligence, Multimodal Imaging, and Clinical Data.Ophthalmology and therapy · 2026Review
- DINO-EYE: self-supervised learning for identification of different optic disc phenotypes in primary open angle glaucoma.Scientific reports · 2026Article
- Deep learning-based joint analysis of diabetic retinopathy and glaucoma in retinal fundus images.Scientific reports · 2025Article
- Disease Diagnosis Using Retinal Vasculature: Insights from Flammer Syndrome and AI.Brain sciences · 2025Review
- Equity-enhanced glaucoma progression prediction from OCT with knowledge distillation.NPJ digital medicine · 2025Article
- Exploring the experiences and perceptions of nursing students in utilizing artificial intelligence: a descriptive phenomenological study.BMC nursing · 2025Article
- Advances in OCT Angiography.Translational vision science & technology · 2025Review
- Artificial Intelligence Applications in Ophthalmology.JMA journal · 2025Review
- Artificial Intelligence for Optical Coherence Tomography in Glaucoma.Translational vision science & technology · 2025Review
- Artificial intelligence in ophthalmology: opportunities, challenges, and ethical considerations.Medical hypothesis, discovery & innovation ophthalmology journal · 2025Review
- Article
- A 20-year research trend analysis of the artificial intelligence on scoliosis using bibliometric methods.Frontiers in pediatrics · 2025Review
- ePWV as a scalable risk factor for large-scale glaucoma screening: evidence from a national Chinese cohort.Frontiers in cell and developmental biology · 2025Article
- Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence.Life (Basel, Switzerland) · 2024Review
- Artificial Intelligence and Advanced Technology in Glaucoma: A Review.Journal of personalized medicine · 2024Review
- Hybrid convolutional neural network optimized with an artificial algae algorithm for glaucoma screening using fundus images.The Journal of international medical research · 2024Article
- Computer-Aided Discrimination of Glaucoma Patients from Healthy Subjects Using the RETeval Portable Device.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 7 institutions in 6 countries.
Funding
Abstract
Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.
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.