Interocular Symmetry Analysis of Corneal Elevation Using the Fellow Eye as the Reference Surface and Machine Learning.
Interocular Symmetry Analysis of Corneal Elevation Using the Fellow Eye as the Reference Surface and Machine Learning.
复制标题
以同眼为参考面和机器学习的角膜抬高眼间对称性分析。
DOI：
10.3390/healthcare9121738
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发表时间：
2021-12-16
期刊：
影响因子：
--
通讯作者：
Rahman MM
中科院分区：
文献类型：
--
作者：
Mehravaran S;Dehzangi I;Rahman MM
单侧角膜指数和地形图在实践中是常规使用的，然而，尽管人们一致认为同眼不对称在临床上具有重要意义，但对称性研究仅限于局部曲率和单点厚度或高度测量。为了改进目前的实践，有必要设计生成对称色图的算法，研究和分类它们的模式，并为识别异常角膜的新的全球鉴别指数开发参考范围。在这项工作中，我们使用来自4613名中年人群的9230个原始的Pentacam文件，测试了使用同眼作为参考表面研究整个角膜表面高度对称性的可行性。这些文件中的140×140个前高程数据矩阵使用Python进行处理，以减去矩阵，创建颜色编码地图，并设计用于机器学习的特征。最常见的图案是一个单色圆圈(“扁平”)，表示良好的镜面对称性。其他可辨认的花纹被命名为“倾斜”、“锥形”和“四叶”。使用怀卡托知识分析环境(WEKA)对不同的特征组合和不同的算法进行了聚类。我们提出的方法可以单独识别每只眼睛看起来正常但需要进一步测试的病例。这项工作将通过包括后仰角、厚度和常见诊断指数的数据来加强。
Unilateral corneal indices and topography maps are routinely used in practice, however, although there is consensus that fellow-eye asymmetry can be clinically significant, symmetry studies are limited to local curvature and single-point thickness or elevation measures. To improve our current practices, there is a need to devise algorithms for generating symmetry colormaps, study and categorize their patterns, and develop reference ranges for new global discriminative indices for identifying abnormal corneas. In this work, we test the feasibility of using the fellow eye as the reference surface for studying elevation symmetry throughout the entire corneal surface using 9230 raw Pentacam files from a population-based cohort of 4613 middle-aged adults. The 140 × 140 matrix of anterior elevation data in these files were handled with Python to subtract matrices, create color-coded maps, and engineer features for machine learning. The most common pattern was a monochrome circle (“flat”) denoting excellent mirror symmetry. Other discernible patterns were named “tilt”, “cone”, and “four-leaf”. Clustering was done with different combinations of features and various algorithms using Waikato Environment for Knowledge Analysis (WEKA). Our proposed approach can identify cases that may appear normal in each eye individually but need further testing. This work will be enhanced by including data of posterior elevation, thickness, and common diagnostic indices.
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