ReviewDiagnostics (Basel, Switzerland)2022
Review of Machine Learning Applications Using Retinal Fundus Images.
Review 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 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, 48 citations in OpenAlex.
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Machine Learning-Based Classification of Retinitis Pigmentosa from Color Fundus Images: A Reproducible Benchmark and Screening-Oriented Pipeline.Vision (Basel, Switzerland) · 2026Article
- Generative Modeling and Multispectral Imaging for Eye Fundus Classification.Medical sciences (Basel, Switzerland) · 2026Article
- A vessel-guided multi-task deep learning framework with visual interpretability for simultaneous retinal vessel segmentation and multi-disease classification from fundus images.Frontiers in medicine · 2026Article
- Leveraging fundus images for on device eye disease diagnosis with AI powered lightweight software hardware framework.Scientific reports · 2025Article
- ISOSNet: a unified framework for cone photoreceptor detection and inner segment and outer segment length measurement from AO-OCT B-scans.Biomedical optics express · 2025Article
- Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging.Journal of diabetes and metabolic disorders · 2025Review
- Automated segmentation for early detection of uveal melanoma.Canadian journal of ophthalmology. Journal canadien d'ophtalmologie · 2024Article
- State-of-the-Art Review: Ocular Infections.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2024Article
- A Comprehensive Review of AI Diagnosis Strategies for Age-Related Macular Degeneration (AMD).Bioengineering (Basel, Switzerland) · 2024Review
- Autonomous screening for laser photocoagulation in fundus images using deep learning.The British journal of ophthalmology · 2024Article
- Convolutional Neural Network-Based Prediction of Axial Length Using Color Fundus Photography.Translational vision science & technology · 2024Article
- Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening.Medicina (Kaunas, Lithuania) · 2024Review
- Deep learning-based fundus image analysis for cardiovascular disease: a review.Therapeutic advances in chronic disease · 2023Review
- Artificial Intelligence in Eye Disease: Recent Developments, Applications, and Surveys.Diagnostics (Basel, Switzerland) · 2022Article
- Ophthalmology and Artificial Intelligence: Present or Future? A Diabetic Retinopathy Screening Perspective of the Pursuit for Fairness.Frontiers in ophthalmology · 2022Article
- Independent Evaluation of RETFound Foundation Model's Performance on Optic Nerve Analysis Using Fundus Photography.Ophthalmology scienceArticle
- A User-friendly Approach for the Diagnosis of Diabetic Retinopathy Using ChatGPT and Automated Machine Learning.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
3 authors at 2 institutions in 1 country.
Funding
Abstract
Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to rapid advancements in automation. Such efforts have been made in ophthalmology to analyze retinal images and build frameworks based on analysis for the identification of retinopathy and the assessment of its severity. This paper reviews recent state-of-the-art works utilizing the color fundus image taken from one of the imaging modalities used in ophthalmology. Specifically, the deep learning methods of automated screening and diagnosis for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are investigated. In addition, the machine learning techniques applied to the retinal vasculature extraction from the fundus image are covered. The challenges in developing these systems are also discussed.
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.