Glaucoma diagnosis in the era of deep learning: A survey.

Glaucoma, a leading cause of irreversible blindness worldwide, poses significant diagnostic challenges due to its reliance on subjective evaluation. Recent advances in computer vision and deep learning have demonstrated the potential for automated assessment. This paper provides a comprehensive survey of studies on AI-based glaucoma diagnosis using fundus, optical coherence tomography, and visual field images, with a focus on deep learning-based methods. We searched Web of Science, PubMed, IEEE Xplore, and Google Scholar, applying specific selection criteria to identify relevant studies published from 2017 to 2023. Our analysis provides a structured overview of architectural paradigms, including convolutional neural networks, autoencoders, attention networks, generative adversarial networks, and geometric deep learning models. Additionally, we discuss approaches for extracting informative features, such as structural, statistical, and hybrid techniques. Furthermore, we outline key research challenges and future directions, emphasizing the need for larger, more diverse datasets, strategies for early disease detection, multi-modal data integration, model explainability, and clinical translation. This survey is expected to be useful for Artificial Intelligence (AI) researchers seeking to translate advances into practice and ophthalmologists aiming to improve clinical workflows and diagnosis using the latest AI outcomes. • Surveyed deep learning techniques (2017–2023) for glaucoma diagnosis. • Categorized glaucoma diagnosis feature extraction methods. • Studied datasets, architectures, and metrics for glaucoma diagnosis. • Outlined challenges and future directions in glaucoma diagnosis. [ABSTRACT FROM AUTHOR]