Lapierre-Landry Maryse, Lu Eric Y, McPheeters Matthew T, Widjaja-Adhi Made Airanthi K, Wilson David L, Sayegh Rony R, Taylor Patricia R, Golczak Marcin, Jenkins Michael W
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
Department of Pharmacology, Case Western Reserve University, Cleveland, OH, USA.
Transl Vis Sci Technol. 2024 Dec 2;13(12):11. doi: 10.1167/tvst.13.12.11.
The corneal nerves within the sub-basal nerve plexus (SBNP) display a distinctive whorl-like pattern, a highly dynamic structure that could be a marker of diseases. Previous studies have reported a decrease in whorl nerve density in patients with diabetes, indicating an avenue for noninvasive monitoring of diabetic neuropathy. However, conflicting results have since been reported, highlighting the need for improved quantitative analysis of the corneal whorl. We present an automated algorithm to characterize the whorl shape and test the hypothesis that the whorl organization is affected by diabetic neuropathy.
The SBNP whorl was analyzed as a vector field, from which seven whorl metrics were calculated. The efficacy of these whorl metrics was demonstrated in synthetic images, ex vivo mouse corneas, and in a publicly available dataset of wide-field in vivo confocal microscopy (IVCM) images of diabetic and control subjects. Linear discriminant analysis and the Peacock test were used to test for statistical differences. Our analysis code is made freely available.
Using our whorl metrics, we were able to quantify different whorl patterns in our patient population and statistically compare cohorts. We determined that whorl patterns tend to present bilaterally in patients (P < 0.001), but there were no significant differences between whorl patterns in patients with diabetes and control subjects, nor between patients with or without neuropathy symptoms.
We present a generalizable framework to statistically compare corneal nerve patterns in cohorts of patients.
SBNP whorl patterns could serve as a noninvasive marker for ocular diseases, whereas few quantitative IVCM endpoints have been identified to date.
角膜基质下神经丛(SBNP)内的角膜神经呈现出独特的螺旋状模式,这是一种高度动态的结构,可能是疾病的标志物。先前的研究报告称,糖尿病患者的螺旋神经密度降低,这为糖尿病神经病变的无创监测提供了一条途径。然而,此后有相互矛盾的结果报道,凸显了改进角膜螺旋定量分析的必要性。我们提出了一种自动算法来表征螺旋形状,并检验螺旋结构受糖尿病神经病变影响的假设。
将SBNP螺旋作为一个向量场进行分析,从中计算出七个螺旋指标。这些螺旋指标的有效性在合成图像、离体小鼠角膜以及一个公开可用的糖尿病和对照受试者的宽视野体内共聚焦显微镜(IVCM)图像数据集中得到了验证。使用线性判别分析和孔雀检验来检验统计学差异。我们的分析代码可免费获取。
使用我们的螺旋指标,我们能够量化患者群体中不同的螺旋模式,并对队列进行统计学比较。我们确定螺旋模式在患者中往往双侧出现(P < 0.001),但糖尿病患者和对照受试者的螺旋模式之间、有或无神经病变症状的患者之间均无显著差异。
我们提出了一个可推广的框架,用于对患者队列中的角膜神经模式进行统计学比较。
SBNP螺旋模式可作为眼部疾病的无创标志物,而迄今为止几乎没有确定的定量IVCM终点指标。