ArticleScientific reports2024
A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.
- Artificial intelligence for diagnosing exudative age-related macular degeneration.The Cochrane database of systematic reviews · 2024Pooled it
- AttResAMD: An Attention-Driven Deep Learning Framework for Expert-Level Automated Classification of Age-Related Macular Degeneration from Fundus Photography.Interdisciplinary sciences, computational life sciences · 2026Article
- Insights into the modulation of age-related macular degeneration: Molecular and systems-level perspectives.Acta ophthalmologica · 2026Article
- Advanced computer-aided diagnosis for age-related macular degeneration: integrating segmentation and feature extraction for precise diagnosis.Scientific reports · 2026Article
- Automatic diagnosis of age-related macular degeneration using machine learning and image processing techniques.Scientific reports · 2026Article
- Artificial Intelligence Applications in Sickle Cell Retinopathy Imaging: Current Progress, Challenges, and Future Directions.Journal of ophthalmology · 2026Review
- Clinical-Oriented Hierarchical Machine Learning Framework for Early Kidney Tumor Detection and Malignant Subtype Classification.Tomography (Ann Arbor, Mich.) · 2025Article
- Machine learning framework for predicting susceptibility to obesity.Scientific reports · 2025Article
- AI-driven prognostics in pediatric bone marrow transplantation: a CAD approach with Bayesian and PSO optimization.BMC medical informatics and decision making · 2025Article
- A dual-stream deep learning framework for skin cancer classification using histopathological-inherited and vision-based feature extraction.Scientific reports · 2025Article
- ODDM: Integration of SMOTE Tomek with Deep Learning on Imbalanced Color Fundus Images for Classification of Several Ocular Diseases.Journal of imaging · 2025Article
- Serum RNA Profile Reflects Fluid Status and Atrophic Retinal Changes in Neovascular Age-Related Macular Degeneration.International journal of molecular sciences · 2025Article
- Reinforcement-based leveraging transfer learning for multiclass optical coherence tomography images classification.Scientific reports · 2025Article
- Advancing thyroid diagnosis: integrating AI-driven CAD framework with numerical data and ultrasound images.PeerJ. Computer science · 2025Article
- Recent advances in the application of artificial intelligence in age-related macular degeneration.BMJ open ophthalmology · 2024Review
- Empowering Portable Age-Related Macular Degeneration Screening: Evaluation of a Deep Learning Algorithm for a Smartphone Fundus Camera.BMJ open · 2024Article
- A Comprehensive Review of AI Diagnosis Strategies for Age-Related Macular Degeneration (AMD).Bioengineering (Basel, Switzerland) · 2024Review
- Discriminative, generative artificial intelligence, and foundation models in retina imaging.Taiwan journal of ophthalmologyReview
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increase in eye disorders among older individuals has raised concerns, necessitating early detection through regular eye examinations. Age-related macular degeneration (AMD), a prevalent condition in individuals over 45, is a leading cause of vision impairment in the elderly. This paper presents a comprehensive computer-aided diagnosis (CAD) framework to categorize fundus images into geographic atrophy (GA), intermediate AMD, normal, and wet AMD categories. This is crucial for early detection and precise diagnosis of age-related macular degeneration (AMD), enabling timely intervention and personalized treatment strategies. We have developed a novel system that extracts both local and global appearance markers from fundus images. These markers are obtained from the entire retina and iso-regions aligned with the optical disc. Applying weighted majority voting on the best classifiers improves performance, resulting in an accuracy of 96.85%, sensitivity of 93.72%, specificity of 97.89%, precision of 93.86%, F1 of 93.72%, ROC of 95.85%, balanced accuracy of 95.81%, and weighted sum of 95.38%. This system not only achieves high accuracy but also provides a detailed assessment of the severity of each retinal region. This approach ensures that the final diagnosis aligns with the physician's understanding of AMD, aiding them in ongoing treatment and follow-up for AMD patients.
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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.