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Diagnostics
Volume 15
Issue 20
10.3390/diagnostics15202644
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

An Ensemble Model for Fundus Images to Aid in Age-Related Macular Degeneration Grading

by
Roberto Romero-Oraá
1,2,*,
María Herrero-Tudela
1,2,
María Isabel López
1,2,
Roberto Hornero
1,2,
Pere Romero-Aroca
3 and
María García
1,2
1
Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain
2
Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
3
Servei d’Oftalmologia, Hospital Universitari Sant Joan de Reus, Institut d’Investigaci’o Sanitària Pere Virgili (IISPV), Universitat Rovira i Virgili, 43204 Tarragona, Spain
*
Author to whom correspondence should be addressed.
Diagnostics 2025, 15(20), 2644; https://doi.org/10.3390/diagnostics15202644
Submission received: 1 September 2025 / Revised: 9 October 2025 / Accepted: 16 October 2025 / Published: 20 October 2025
(This article belongs to the Special Issue Updates on the Diagnosis and Management of Retinal Diseases—2nd Edition)
Versions Notes

Abstract

Background: Age-related macular degeneration (AMD) is a leading cause of visual impairment in the elderly population. Periodic examinations through fundus image analysis are paramount for early diagnosis and adequate treatment. Automatic artificial intelligence algorithms have proven useful for AMD grading, with the ensemble strategies recently gaining special attention. Methods: This study presents an ensemble model that combines 2 individual models of a different nature. The first model was based on the ResNetRS architecture and supervised learning. The second model, known as RETFound, was based on a visual transformer architecture and self-supervised learning. Results: Our experiments were conducted using 149,819 fundus images from the Age-Related Eye Disease Study (AREDS) public dataset. An additional private dataset of 1679 images was used to validate our approach. The results on AREDS achieved a quadratic weighted kappa of 0.7364 and an accuracy of 66.03%, which outperforms the previous methods in the literature. Conclusions: The ensemble strategy presented in this study could be useful for the screening of AMD in a clinical setting. Consequently, eye care for AMD patients would be improved while clinical costs and workload would be reduced.
Keywords: age-related macular degeneration; medical diagnosis; fundus images; deep learning; ensemble model age-related macular degeneration; medical diagnosis; fundus images; deep learning; ensemble model

Share and Cite

MDPI and ACS Style

Romero-Oraá, R.; Herrero-Tudela, M.; López, M.I.; Hornero, R.; Romero-Aroca, P.; García, M. An Ensemble Model for Fundus Images to Aid in Age-Related Macular Degeneration Grading. Diagnostics 2025, 15, 2644. https://doi.org/10.3390/diagnostics15202644

AMA Style

Romero-Oraá R, Herrero-Tudela M, López MI, Hornero R, Romero-Aroca P, García M. An Ensemble Model for Fundus Images to Aid in Age-Related Macular Degeneration Grading. Diagnostics. 2025; 15(20):2644. https://doi.org/10.3390/diagnostics15202644

Chicago/Turabian Style

Romero-Oraá, Roberto, María Herrero-Tudela, María Isabel López, Roberto Hornero, Pere Romero-Aroca, and María García. 2025. "An Ensemble Model for Fundus Images to Aid in Age-Related Macular Degeneration Grading" Diagnostics 15, no. 20: 2644. https://doi.org/10.3390/diagnostics15202644

APA Style

Romero-Oraá, R., Herrero-Tudela, M., López, M. I., Hornero, R., Romero-Aroca, P., & García, M. (2025). An Ensemble Model for Fundus Images to Aid in Age-Related Macular Degeneration Grading. Diagnostics, 15(20), 2644. https://doi.org/10.3390/diagnostics15202644

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