Machine Learning-Based Analysis of Optical Coherence Tomography Angiography Images for Age-Related Macular Degeneration - Alfahaid, Abdullah; Morris, Tim; Cootes, Tim; Keane, Pearse A.; Khalid, Hagar; Pontikos, Nikolas; Alharbi, Fatemah; Alalwany, Easa; Almars, Abdulqader M.; Aldweesh, Amjad; ALMansour, Abdullah G. M.; Sergouniotis, Panagiotis I.; et al. (Biomedicines, 2025)
Background/Objectives: Age-related macular degeneration (AMD) is the leading cause of visual impairment among the elderly. Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that enables detailed visualisation of retinal vascular layers. However, clinical assessment of OCTA images is often challenging due to high data volume, pattern variability, and subtle abnormalities. This study aimed to develop automated algorithms to detect and quantify AMD in OCTA images, thereby reducing ophthalmologists’ workload and enhancing diagnostic accuracy. Methods: Two texture-based algorithms were developed to classify OCTA images without relying on segmentation. The first algorithm used whole local texture features, while the second applied principal component analysis (PCA) to decorrelate and reduce texture features. Local texture descriptors, including rotation-invariant uniform local binary patterns (LBP2riu), local binary patterns (LBP), and binary robust independent elementary features (BRIEF), were combined with machine learning classifiers such as support vector machine (SVM) and K-nearest neighbour (KNN). OCTA datasets from Manchester Royal Eye Hospital and Moorfields Eye Hospital, covering healthy, dry AMD, and wet AMD eyes, were used for evaluation. Results: The first algorithm achieved a mean area under the receiver operating characteristic curve (AUC) of 1.00±0.00 for distinguishing healthy eyes from wet AMD. The second algorithm showed superior performance in differentiating dry AMD from wet AMD (AUC 0.85±0.02). Conclusions: The proposed algorithms demonstrate strong potential for rapid and accurate AMD diagnosis in OCTA workflows. By reducing manual image evaluation and associated variability, they may support improved clinical decision-making and patient care.