Detection and exploratory severity grading of age-related macular degeneration using machine learning

Abstract

Age-related Macular Degeneration (AMD) is a leading cause of irreversible vision loss among the elderly population worldwide, making early detection and reliable severity assessment essential for timely intervention and prevention of severe visual impairment. This study presents a novel machine-learning–based framework for automated AMD detection and exploratory severity grading using Optical Coherence Tomography (OCT) images. The proposed approach employs deep learning architectures, including ResNet and VGG, trained on publicly available datasets. An unsupervised learning strategy is introduced for dry AMD exploratory severity grading through feature extraction based on drusen size. Advanced AMD manifestations, such as geographic atrophy and choroidal neovascularization, are identified using clinically relevant retinal features derived from OCT images. The proposed detection framework achieves high accuracy (98%) on the primary dataset and demonstrates robust generalization with an accuracy of 83% for cross-dataset evaluation using transfer learning. Furthermore, the unsupervised severity grading model attains a silhouette score close to 0.5, reflecting meaningful cluster structure consistent with the continuous nature of drusen progression. Overall, the proposed framework offers a scalable and automated solution for AMD detection and exploratory severity grading, with the potential to support clinical decision–making and enhance personalized disease management.