ArticleNPJ digital medicine2025
A hybrid multi model artificial intelligence approach for glaucoma screening using fundus images.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The trial behind it
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Who cites it
16 citing papers in PubMed.
- Clinically aligned artificial intelligence for glaucoma diagnosis: enhancing retinal nerve fibre layer interpretation from fundus images.Eye (London, England) · 2026Article
- A Lightweight Vision Transformer and Retinal Biomarker Fusion Framework for Early Glaucoma Detection: Toward Improved Clinical Screening.Journal of clinical medicine · 2026Article
- Hypertuned boosting approach with Local Binary Pattern and Pivot Distribution Count method feature extractor for glaucoma identification.Scientific reports · 2026Article
- Transforming Eye-Care Diagnostics Through Artificial Intelligence, Biometric Evaluation, and Tele-Optometry.Cureus · 2026Review
- Portable AI-powered scanning slit-light device for low-cost eye disease screening.Scientific reports · 2026Article
- Development, validation, and cost-effectiveness analysis of an AI-assisted three-tiered glaucoma screening model in a community-based setting: protocol for a cluster randomized controlled trial.Frontiers in public health · 2026Article
- Beyond the clinic: a community-embedded, multidomain framework for early detection of glaucoma.Frontiers in public health · 2026Article
- Robust benchmarking of DeepLabv3 hybrid models for multiclass fundus screening and referable-disease triage on the FIVES dataset.Frontiers in medicine · 2026Article
- Combined diagnostic accuracy of two artificial intelligence systems for glaucoma diagnosis using color fundus photography.PloS one · 2026Article
- An Exploratory Study of Machine Learning-Based Open-Angle Glaucoma Detection Using Specific Autoantibodies.Biomedicines · 2025Article
- Automated cup-to-disc ratio quantification via color fundus photography for chronic glaucoma screening.BMC medical imaging · 2025Article
- A Comparative Study Between Clinical Optical Coherence Tomography (OCT) Analysis and Artificial Intelligence-Based Quantitative Evaluation in the Diagnosis of Diabetic Macular Edema.Vision (Basel, Switzerland) · 2025Article
- Glaucoma screening in Kazakhstan.Scientific reports · 2025Article
- Artificial intelligence in ophthalmology: opportunities, challenges, and ethical considerations.Medical hypothesis, discovery & innovation ophthalmology journal · 2025Review
- A federal learning-driven artificial intelligence framework for fundus image myopia diagnosis.Digital healthArticle
- Advances and challenges from pathological mechanisms to intelligent quantified diagnosis in diabetic optic neuropathy.Digital healthReview
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Glaucoma, a leading cause of blindness, requires accurate early detection. We present an AI-based Glaucoma Screening (AI-GS) network comprising six lightweight deep learning models (total size: 110 MB) that analyze fundus images to identify early structural signs such as optic disc cupping, hemorrhages, and nerve fiber layer defects. The segmentation of the optic cup and disc closely matches that of expert ophthalmologists. AI-GS achieved a sensitivity of 0.9352 (95% CI 0.9277-0.9435) at 95% specificity. In real-world testing, sensitivity dropped to 0.5652 (95% CI 0.5218-0.6058) at ~0.9376 specificity (95% CI 0.9174-0.9562) for the standalone binary glaucoma classification model, whereas the full AI-GS network maintained higher sensitivity (0.8053, 95% CI 0.7704-0.8382) with good specificity (0.9112, 95% CI 0.8887-0.9356). The sub-models in AI-GS, with enhanced capabilities in detecting early glaucoma-related structural changes, drive these improvements. With low computational demands and tunable detection parameters, AI-GS promises widespread glaucoma screening, portable device integration, and improved understanding of disease progression.
Identifiers
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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.