Deep Learning and Computer Vision for Glaucoma Detection: A Review
Mona Ashtari-Majlan Mohammad Mahdi Dehshibi David Masip
Glaucoma is the leading cause of irreversible blindness worldwide and poses
significant diagnostic challenges due to its reliance on subjective evaluation.
However, recent advances in computer vision and deep learning have demonstrated
the potential for automated assessment. In this paper, we survey recent studies
on AI-based glaucoma diagnosis using fundus, optical coherence tomography, and
visual field images, with a particular emphasis on deep learning-based methods.
We provide an updated taxonomy that organizes methods into architectural
paradigms and includes links to available source code to enhance the
reproducibility of the methods. Through rigorous benchmarking on widely-used
public datasets, we reveal performance gaps in generalizability, uncertainty
estimation, and multimodal integration. Additionally, our survey curates key
datasets while highlighting limitations such as scale, labeling
inconsistencies, and bias. We outline open research challenges and detail
promising directions for future studies. This survey is expected to be useful
for both AI researchers seeking to translate advances into practice and
ophthalmologists aiming to improve clinical workflows and diagnosis using the
latest AI outcomes.