Winter Karsten, Scheibe Patrick, Köhler Bernd, Allgeier Stephan, Guthoff Rudolf F, Stachs Oliver
a Translational Centre for Regenerative Medicine, University of Leipzig , Leipzig , Germany .
b Department of Ophthalmology , University of Rostock , Rostock , Germany .
Curr Eye Res. 2016;41(2):186-98. doi: 10.3109/02713683.2015.1010686. Epub 2015 Mar 24.
The corneal subbasal nerve plexus (SNP) offers high potential for early diagnosis of diabetic peripheral neuropathy. Changes in subbasal nerve fibers can be assessed in vivo by confocal laser scanning microscopy (CLSM) and quantified using specific parameters. While current study results agree regarding parameter tendency, there are considerable differences in terms of absolute values. The present study set out to identify factors that might account for this high parameter variability.
In three healthy subjects, we used a novel method of software-based large-scale reconstruction that provided SNP images of the central cornea, decomposed the image areas into all possible image sections corresponding to the size of a single conventional CLSM image (0.16 mm2), and calculated a set of parameters for each image section. In order to carry out a large number of virtual examinations within the reconstructed image areas, an extensive simulation procedure (10,000 runs per image) was implemented.
The three analyzed images ranged in size from 3.75 mm2 to 4.27 mm2. The spatial configuration of the subbasal nerve fiber networks varied greatly across the cornea and thus caused heavily location-dependent results as well as wide value ranges for the parameters assessed. Distributions of SNP parameter values varied greatly between the three images and showed significant differences between all images for every parameter calculated (p < 0.001 in each case).
The relatively small size of the conventionally evaluated SNP area is a contributory factor in high SNP parameter variability. Averaging of parameter values based on multiple CLSM frames does not necessarily result in good approximations of the respective reference values of the whole image area. This illustrates the potential for examiner bias when selecting SNP images in the central corneal area.
角膜基底神经丛(SNP)在糖尿病周围神经病变的早期诊断方面具有很大潜力。基底神经纤维的变化可通过共聚焦激光扫描显微镜(CLSM)进行体内评估,并使用特定参数进行量化。虽然目前的研究结果在参数趋势方面是一致的,但在绝对值方面存在相当大的差异。本研究旨在确定可能导致这种高参数变异性的因素。
在三名健康受试者中,我们使用了一种基于软件的大规模重建新方法,该方法提供了中央角膜的SNP图像,将图像区域分解为与单个传统CLSM图像大小(0.16 mm2)相对应的所有可能图像切片,并为每个图像切片计算一组参数。为了在重建的图像区域内进行大量虚拟检查,实施了广泛的模拟程序(每个图像10,000次运行)。
分析的三张图像大小从3.75 mm2到4.27 mm2不等。基底神经纤维网络的空间配置在整个角膜上差异很大,因此导致了高度依赖位置的结果以及所评估参数的广泛值范围。SNP参数值的分布在三张图像之间差异很大,并显示出计算的每个参数在所有图像之间都存在显著差异(每种情况下p < 0.001)。
传统评估的SNP区域相对较小是SNP参数变异性高的一个促成因素。基于多个CLSM帧对参数值进行平均不一定能很好地近似整个图像区域的相应参考值。这说明了在选择中央角膜区域的SNP图像时检查者偏差的可能性。