青光眼影像人工智能深度学习研究现状与展望

青光眼是一组异质性神经退行性疾病，其特征是视网膜神经节细胞及其轴突逐渐消失，现已成为全球不可逆性失明的主要原因。人工智能(AI)是由机器展示的智能，而深度学习(DL)是其中一个基于深度神经网络的分支，在医学成像领域取得了重大突破。在青光眼影像方面，已有越来越多的研究将DL应用于眼底图像以及光学相干断层扫描(OCT)，以检测青光眼性视神经病变。有很好的结果显示，将DL技术整合到影像中进行青光眼评估是高效、准确的，这可能会解决当前实践和临床工作流程中的一些难题。但是，未来进一步的研究对于解决现存挑战至关重要，例如为不同研究之间的图像标记建立标准，将“黑匣子”的学习过程进行可视化，提高模型在未知数据集上的泛化能力，开发基于DL的实际应用程序，以及建立合理的临床工作流程，进行前瞻性验证和成本效益分析等。本文总结了AI应用于青光眼影像的最新研究现状，并讨论了对临床的潜在影响和未来的研究方向。

Artificial intelligence deep learning in glaucoma imaging: current progress and future prospect

Glaucoma is a group of heterogeneous neurodegenerative diseases, characterized by the gradual loss of retinal ganglion cells and their axons, and has now become the major reason of irreversible blindness worldwide. Artificial intelligence (AI) is intelligence demonstrated by machines. Deep learning (DL) is a subset of AI based on deep neural networks, and it has made great breakthroughs in medical imaging. In glaucoma imaging, research interests have been increasing on applying DL in fundus photographs and optical coherence tomography (OCT) for glaucomatous optic neuropathy (GON) detection. Promising results show that the incorporation of DL technology in imaging for glaucoma assessment is efficient and accurate, which could potentially address some gaps in the current practice and clinical workflow. However, further research is crucial in tackling some existing challenges, such as setting a standard for ground truth labelling among different studies, visualizing the learning process in the "black box", improving the model generalizability on unseen datasets, developing the DL-powered infrastructure for real-world implementation, establishing a practical clinical workflow, conducting prospective validation and cost-effectiveness analysis. This review summarizes recent studies on the application of AI on glaucoma imaging, discusses the potential clinical impact and future research directions.