Advancements in Deep Learning for Automated Diagnosis of Ophthalmic Diseases: A Comprehensive Review

This review paper presents a thorough analysis of 99 recent studies focused on applying deep learning techniques for the automated diagnosis of various eye diseases, including glaucoma, diabetic retinopathy, cataracts, amblyopia, and macular degeneration. The advent of deep learning methodologies has revolutionized the field of ophthalmic diagnostics, offering promising solutions for enhancing the accuracy, efficiency, and accessibility of disease detection. The comprehensive examination encompasses diverse deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models, along with the exploration of diverse imaging modalities, such as fundus photography, optical coherence tomography (OCT), and visual field testing. Each disease category was scrutinized individually, highlighting the unique challenges and opportunities for automated diagnosis. Key findings and advancements in the field are discussed, shedding light on the potential of deep learning algorithms for early disease detection and timely intervention. This review also addresses existing limitations, including data variability, interpretability, and the need for large, diverse datasets. Insights from this literature synthesis aim to guide future research directions, fostering the continued development of reliable and efficient deep learning-based diagnostic tools for eye diseases.