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

TL;DR

This paper introduces an AI model combining two techniques to improve the grading of age-related macular degeneration from eye images.

Contribution

The novel contribution is an ensemble model combining ResNetRS and RETFound for improved AMD grading.

Findings

The model achieved a quadratic weighted kappa of 0.7364 on the AREDS dataset.

It outperformed previous methods with an accuracy of 66.03%.

The approach was validated on a private dataset of 1679 images.

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.

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