Vision, Imaging and Performance Laboratory (VIP), Duke Eye Center, Duke University, Durham, NC, USA.
Transl Vis Sci Technol. 2020 Jul 22;9(2):42. doi: 10.1167/tvst.9.2.42. eCollection 2020 Jul.
Because of recent advances in computing technology and the availability of large datasets, deep learning has risen to the forefront of artificial intelligence, with performances that often equal, or sometimes even exceed, those of human subjects on a variety of tasks, especially those related to image classification and pattern recognition. As one of the medical fields that is highly dependent on ancillary imaging tests, ophthalmology has been in a prime position to witness the application of deep learning algorithms that can help analyze the vast amount of data coming from those tests. In particular, glaucoma stands as one of the conditions where application of deep learning algorithms could potentially lead to better use of the vast amount of information coming from structural and functional tests evaluating the optic nerve and macula. The purpose of this article is to critically review recent applications of deep learning models in glaucoma, discussing their advantages but also focusing on the challenges inherent to the development of such models for screening, diagnosis and detection of progression. After a brief general overview of deep learning and how it compares to traditional machine learning classifiers, we discuss issues related to the training and validation of deep learning models and how they specifically apply to glaucoma. We then discuss specific scenarios where deep learning has been proposed for use in glaucoma, such as screening with fundus photography, and diagnosis and detection of glaucoma progression with optical coherence tomography and standard automated perimetry.
Deep learning algorithms have the potential to significantly improve diagnostic capabilities in glaucoma, but their application in clinical practice requires careful validation, with consideration of the target population, the reference standards used to build the models, and potential sources of bias.
由于计算技术的最新进展和大型数据集的可用性，深度学习已成为人工智能的前沿领域，其性能在各种任务上通常与人类相当，甚至有时甚至超过人类，尤其是在图像分类和模式识别方面。作为高度依赖辅助成像测试的医学领域之一，眼科已经处于应用深度学习算法的有利位置，这些算法可以帮助分析来自这些测试的大量数据。特别是，青光眼是深度学习算法可能有助于更好地利用评估视神经和黄斑的结构和功能测试的大量信息的病症之一。本文的目的是批判性地回顾深度学习模型在青光眼中的最新应用，讨论它们的优势，但也侧重于为筛查、诊断和检测进展开发此类模型所固有的挑战。在简要概述深度学习以及它与传统机器学习分类器的比较之后，我们讨论了与深度学习模型的训练和验证相关的问题，以及它们如何特别适用于青光眼。然后，我们讨论了深度学习在青光眼中应用的具体情况，例如眼底摄影筛查，以及光学相干断层扫描和标准自动视野计的青光眼进展诊断和检测。
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