Improving the Detection of Glaucoma and Its Progression: A Topographical Approach
Improving the Detection of Glaucoma and Its Progression: A Topographical Approach
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DOI：
10.1097/ijg.0000000000001553
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发表时间：
2020-08-01
影响因子：
2
通讯作者：
De Moraes, Carlos Gustavo
中科院分区：
文献类型：
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作者：
Hood, Donald C.;Zemborain, Zane Z.;De Moraes, Carlos Gustavo
青光眼通常被定义为一种进行性视神经病变，其特征是视野(VF)和解剖改变的特殊(弧形)模式。因此，我们应该将VFS上看到的弧形损伤模式与光学相干断层扫描(OCT)图上看到的进行比较。相反，临床医生通常使用VF模式标准差、OCT视网膜神经纤维(RNF)整体厚度等摘要指标。在VF和OCT图上比较损害模式有两个主要障碍。首先，直到最近，要将这些与商业报告进行比较并不容易。虽然最近的报告确实使比较VF和OCT地图变得更容易，但它们也有缺点。特别是，24-2VF覆盖的视网膜区域比商业OCT扫描更大，而且，在当前的OCT报告中，不同地图/地块之间的地形关系不容易理解。在这里，我们展示了RNF捆绑包模型如何克服这些问题。第二个主要障碍是缺乏一种定量的、自动化的方法来比较VF和OCT图上看到的损伤模式。然而，现在可以客观和自动地量化这一协议。总之，RNF束模型和自动结构-功能方法应该会提高地形图方法检测青光眼及其进展的能力。这将在临床研究和试验中被证明是有用的，以及用于培训和验证用于这些目的的人工智能/深度学习方法。
Glaucoma is typically defined as a progressive optic neuropathy characterized by a specific (arcuate) pattern of visual field (VF) and anatomic changes. Therefore, we should be comparing arcuate patterns of damage seen on VFs with those seen on optical coherence tomography (OCT) maps. Instead, clinicians often use summary metrics such as VF pattern standard deviation, OCT retinal nerve fiber (RNF) global thickness, etc. There are 2 major impediments to topographically comparing patterns of damage on VF and OCT maps. First, until recently, it was not easy to make these comparisons with commercial reports. While recent reports do make it easier to compare VF and OCT maps, they have shortcomings. In particular, the 24-2 VF covers a larger retinal region than the commercial OCT scans, and, further, it is not easy to understand the topographical relationship among the different maps/plots within the current OCT reports. Here we show how a model of RNF bundles can overcome these problems. The second major impediment is the lack of a quantitative, and automated, method for comparing patterns of damage seen on VF and OCT maps. However, it is now possible to objectively and automatically quantify this agreement. Together, the RNF bundle model and the automated structure-function method should improve the power of topographical methods for detecting glaucoma and its progression. This should prove useful in clinical studies and trials, as well as for training and validating artificial intelligence/deep learning approaches for these purposes.