L. V. Prasad Eye Institute, Hyderabad, Telangana, India.
EEE Department, BITS Pilani, Hyderabad Campus, Hyderabad, Telangana, India.
Sci Rep. 2021 Dec 2;11(1):23336. doi: 10.1038/s41598-021-02025-4.
Progressive optic neuropathies such as glaucoma are major causes of blindness globally. Multiple sources of subjectivity and analytical challenges are often encountered by clinicians in the process of early diagnosis and clinical management of these diseases. In glaucoma, the structural damage is often characterized by neuroretinal rim (NRR) thinning of the optic nerve head, and other clinical parameters. Baseline structural heterogeneity in the eyes can play a key role in the progression of optic neuropathies, and present challenges to clinical decision-making. We generated a dataset of Optical Coherence Tomography (OCT) based high-resolution circular measurements on NRR phenotypes, along with other clinical covariates, of 3973 healthy eyes as part of an established clinical cohort of Asian Indian participants. We introduced CIFU, a new computational pipeline for CIrcular FUnctional data modeling and analysis. We demonstrated CIFU by unsupervised circular functional clustering of the OCT NRR data, followed by meta-clustering to characterize the clusters using clinical covariates, and presented a circular visualization of the results. Upon stratification by age, we identified a healthy NRR phenotype cluster in the age group 40-49 years with predictive potential for glaucoma. Our dataset also addresses the disparity of representation of this particular population in normative OCT databases.
进行性视神经病变，如青光眼，是全球范围内导致失明的主要原因。在这些疾病的早期诊断和临床管理过程中，临床医生经常会遇到多种主观性和分析性挑战来源。在青光眼，结构损伤通常表现为视神经头神经视网膜边缘（NRR）变薄，以及其他临床参数。眼睛的基线结构异质性在视神经病变的进展中起着关键作用，并对临床决策提出挑战。我们生成了一个数据集，其中包含了 3973 只健康眼睛的基于光学相干断层扫描（OCT）的 NRR 表型的高分辨率圆形测量值，以及其他临床协变量。我们引入了 CIFU，这是一种用于圆形功能数据建模和分析的新计算管道。我们通过 OCT NRR 数据的无监督圆形功能聚类来展示 CIFU，然后进行元聚类以使用临床协变量来描述聚类，并以圆形可视化的形式呈现结果。通过按年龄分层，我们在 40-49 岁年龄组中确定了一个具有青光眼预测潜力的健康 NRR 表型聚类。我们的数据集还解决了特定人群在正常 OCT 数据库中的代表性差异问题。
