L. V. Prasad Eye Institute, Hyderabad, Telangana, India.
EEE Department, BITS Pilani Hyderabad Campus, Hyderabad, Telangana, India.
PLoS One. 2023 Oct 18;18(10):e0292915. doi: 10.1371/journal.pone.0292915. eCollection 2023.
We generated Optical Coherence Tomography (OCT) data of much higher resolution than usual on retinal nerve fiber layer (RNFL) thickness of a given eye. These consist of measurements made at hundreds of angular-points defined on a circular coordinate system. Traditional analysis of OCT RNFL data does not utilize insightful characteristics such as its circularity and granularity for common downstream applications. To address this, we present a new circular statistical framework that defines an Angular Decay function and thereby provides a directionally precise representation of an eye with attention to patterns of focused RNFL loss. By applying to a clinical cohort of Asian Indian eyes, the generated circular data were modeled with a finite mixture of von Mises distributions, which led to an unsupervised identification in different age-groups of recurrent clusters of glaucomatous eyes with distinct directional signatures of RNFL decay. New indices of global and local RNFL loss were computed for comparing the structural differences between these glaucoma clusters across the age-groups and improving classification. Further, we built a catalog of directionally precise statistical distributions of RNFL thickness for the said population of normal eyes as stratified by their age and optic disc size.
我们生成了视网膜神经纤维层(RNFL)厚度的比通常更高分辨率的光学相干断层扫描(OCT)数据。这些数据由在以圆形坐标系定义的数百个角点处进行的测量组成。传统的 OCT RNFL 数据分析没有利用其圆性和粒度等有洞察力的特征,这些特征对于常见的下游应用很重要。为了解决这个问题,我们提出了一个新的圆形统计框架,该框架定义了一个角度衰减函数,从而为眼睛提供了一个具有方向性的精确表示,并关注聚焦的 RNFL 损失模式。通过应用于亚洲印第安人眼睛的临床队列,生成的圆形数据使用 von Mises 分布的有限混合进行建模,从而在不同的年龄组中对具有不同 RNFL 衰减方向特征的青光眼眼睛进行了无监督识别。为了比较不同年龄组的青光眼簇之间的结构差异并提高分类效果,我们计算了全局和局部 RNFL 损失的新指数。此外,我们为该人群的正常眼睛构建了一个方向精确的 RNFL 厚度统计分布目录,这些正常眼睛按其年龄和视盘大小分层。