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.

翻译：青光眼是全球导致不可逆失明的主要原因，由于其依赖主观评估而面临显著的诊断挑战。然而，计算机视觉和深度学习的最新进展已展现出自动化评估的潜力。本文综述了近期基于人工智能的青光眼诊断研究，涵盖眼底图像、光学相干断层扫描图像和视野图像，并特别聚焦于深度学习方法。我们提出了一种更新的分类体系，将不同方法按架构范式进行组织，并附上可用源代码的链接以增强方法的可复现性。通过在广泛使用的公开数据集上进行严格的基准测试，我们揭示了在泛化能力、不确定性估计和多模态融合方面的性能差距。此外，本综述整理了关键数据集，同时指出其存在的规模、标注不一致性和偏差等局限性。我们概述了开放性的研究挑战，并详细介绍了未来研究的有前景方向。本综述对于寻求将前沿成果转化为临床实践的人工智能研究人员，以及希望借助最新人工智能成果改进临床工作流程和诊断的眼科医生均具有参考价值。