Background

The consequences of inflammation, demyelination, axonal degeneration and neuronal loss in the central nervous system, typical of the development of multiple sclerosis (MS), are manifested in thinning of the retina and optic nerve. The purpose of this work is to diagnose early-stage MS patients based on analysis of retinal layer thickness obtained by swept-source optical coherence tomography (SS-OCT).

Method

OCT (Triton® SS-OCT device -Topcon, Tokyo, Japan-) recordings were obtained from 48 control subjects and 48 recently diagnosed MS patients. The following thicknesses were measured on a 45 × 60 grid: retinal nerve fibre layer (RNFL), ganglion cell layer (GCL+), GCL++, retinal thickness and choroid. Using Cohen's d effect size, it was determined the regions and layers with greatest capacity to discriminate between control subjects and patients. Points exceeding the threshold set were used as inputs for an automatic classifier: support vector machine and feed-forward neural network.

Results

In MS at clinical onset the layer with greatest discriminant capacity is GCL++ [AUC = 0.83] which exhibits a horseshoe-like macular topographic distribution. It is followed by retina, GCL+ and RNFL; choroidal thicknesses do not provide discriminatory capacity. Using a neural network as a classifier between controls and MS patients, obtains sensitivity of 0.98 and specificity of 0.98.

Conclusions

This work suggest that OCT may serve as an important complementary role to other clinical tests, particularly regarding neurodegeneration. It is possible to characterise structural alterations in retina and diagnose early-stage MS with high degree of accuracy using OCT and artificial neural networks.

中文翻译：

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使用Cohen d方法和神经网络作为分类器的OCT分析对多发性硬化症的早期诊断

背景

炎症和脱髓鞘，轴突变性和中枢神经系统神经元丧失的后果是多发性硬化症（MS）的典型发展，表现为视网膜和视神经变薄。这项工作的目的是根据通过扫频光学相干断层扫描（SS-OCT）获得的视网膜层厚度分析来诊断早期MS患者。

方法

从48位对照受试者和48位最近诊断出的MS患者中获得了OCT（Triton®SS-OCT设备-Topcon，日本东京，日本）。在45×60的网格上测量以下厚度：视网膜神经纤维层（RNFL），神经节细胞层（GCL +），GCL ++，视网膜厚度和脉络膜。使用Cohen的效应大小，可以确定最能区分对照对象和患者的区域和层。超过阈值集的点用作自动分类器的输入：支持向量机和前馈神经网络。

结果

在临床发作的MS中，具有最大判别能力的层是GCL ++ [AUC = 0.83]，其表现出马蹄状的黄斑形分布。紧随其后的是视网膜，GCL +和RNFL；脉络膜厚度不提供区分能力。使用神经网络作为对照和MS患者之间的分类器，可获得0.98的敏感性和0.98的特异性。

结论

这项工作表明，OCT可以作为其他临床测试的重要补充角色，尤其是在神经变性方面。使用OCT和人工神经网络可以表征视网膜的结构改变并以较高的准确性诊断早期MS。