Deep Learning and Optical Coherence Tomography: A Review of Emerging Technologies for Early Detection

Abstract

Age-associated macular degeneration (AMD), diabetes-related retinopathy (DR), and glaucoma all can be diagnosed through DL-based OCT analysis of images. We discuss DL architectures employed (e.g., convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformer networks) to examine complicated data sets for early disease markers usually not visible in screening. OCT provides high-resolution images of the retina, ideal for DL algorithms to identify early manifestations of disease patterns. The review identifies improvement in diagnostic efficacy, efficiency, and public screening using these technologies. We also touch upon implementation challenges such as availability of data, model interpretability, and reproducibility across heterogeneous populations and imaging platforms. Finally, we discuss promising future studies to take these technologies from the bench to the bedside and beyond, providing effective tools for timely intervention and personalized ophthalmic care with vision preservation.