Trends, Challenges, and Future Directions in Deep Learning for Glaucoma: A Systematic Review
This work provides a comprehensive overview for researchers and clinicians in medical imaging, but it is incremental as it synthesizes existing studies rather than introducing new methods.
This systematic review examines recent advances in deep learning for glaucoma detection, analyzing trends in data modalities, processing strategies, and model architectures, and identifies current challenges and future directions.
Here, we examine the latest advances in glaucoma detection through Deep Learning (DL) algorithms using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This study focuses on three aspects of DL-based glaucoma detection frameworks: input data modalities, processing strategies, and model architectures and applications. Moreover, we analyze trends in employing each aspect since the onset of DL in this field. Finally, we address current challenges and suggest future research directions.