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.

该研究通过调查最近的研究，使用眼底图像、光学相干断层扫描和视野图像等方法基于人工智能的青光眼诊断，特别关注利用深度学习的方法。研究提供了最新的分类法，并包含可用源代码的链接，以提高研究方法的可重复性。通过对广泛使用的公共数据集进行严格的基准测试，揭示了在普适性、不确定性估计和多模态集成方面的性能差距。此外，调查展示了关键数据集和限制，如规模、标签不一致性和偏见。我们描述了未来研究的开放性挑战并详细阐述了有前景的方向。此调查旨在对希望将进展转化为实践的人工智能研究人员以及希望利用最新人工智能成果改善临床工作流程和诊断的眼科医生有所帮助。

深度学习与计算机视觉在青光眼检测中的应用综述

Deep Learning and Computer Vision for Glaucoma Detection: A Review

One principal impediment in the successful deployment of AI-based
Computer-Aided Diagnosis (CAD) systems in clinical workflows is their lack of
transparent decision making. Although commonly used eXplainable AI methods
provide some insight into opaque algorithms, such explanations are usually
convoluted and not readily comprehensible except by highly trained experts. The
explanation of decisions regarding the malignancy of skin lesions from
dermoscopic images demands particular clarity, as the underlying medical
problem definition is itself ambiguous. This work presents ExAID (Explainable
AI for Dermatology), a novel framework for biomedical image analysis, providing
multi-modal concept-based explanations consisting of easy-to-understand textual
explanations supplemented by visual maps justifying the predictions. ExAID
relies on Concept Activation Vectors to map human concepts to those learnt by
arbitrary Deep Learning models in latent space, and Concept Localization Maps
to highlight concepts in the input space. This identification of relevant
concepts is then used to construct fine-grained textual explanations
supplemented by concept-wise location information to provide comprehensive and
coherent multi-modal explanations. All information is comprehensively presented
in a diagnostic interface for use in clinical routines. An educational mode
provides dataset-level explanation statistics and tools for data and model
exploration to aid medical research and education. Through rigorous
quantitative and qualitative evaluation of ExAID, we show the utility of
multi-modal explanations for CAD-assisted scenarios even in case of wrong
predictions. We believe that ExAID will provide dermatologists an effective
screening tool that they both understand and trust. Moreover, it will be the
basis for similar applications in other biomedical imaging fields.

本文介绍了一种基于 AI 的医学图像分析框架 ExAID，利用多模式概念解释提供易于理解的文本解释和可视化地图来验证预测结果，用于皮肤病例中的良恶性、依赖的模糊的问题。该框架不仅在临床工作流程中提供诊断界面，还提供了教育模式，可用于数据和模型探索，用于辅助医学研究和教育。通过定量和定性的评估，表明多模方式的解释对于 CAD 辅助方案的效用甚至在错误的预测情况下也是很有用的。该应用是在其他生物医学成像领域中类似应用的基础。