Background

Multiple sclerosis (MS) is a neurodegenerative disease that affects the central nervous system, especially the brain, spinal cord, and optic nerve. Diagnosis of this disease is a very complex process and generally requires a lot of time. In addition, treatments are applied without any information on the disability course in each MS patient. For these two reasons, the objective of this study was to improve the MS diagnosis and predict the long-term course of disability in MS patients based on clinical data and retinal nerve fiber layer (RNFL) thickness, measured by optical coherence tomography (OCT).

Material and methods

A total of 104 healthy controls and 108 MS patients, 82 of whom had a 10-year follow-up, were enrolled. Classification algorithms such as multiple linear regression (MLR), support vector machines (SVM), decision tree (DT), k-nearest neighbours (k-NN), Naïve Bayes (NB), ensemble classifier (EC) and long short-term memory (LSTM) recurrent neural network were tested to develop two predictive models: MS diagnosis model and MS disability course prediction model.

Results

For MS diagnosis, the best result was obtained using EC (accuracy: 87.7%; sensitivity: 87.0%; specificity: 88.5%; precision: 88.7%; AUC: 0.8775). In line with this good performance, the accuracy was 85.4% using k-NN and 84.4% using SVM. And, for long-term prediction of MS disability course, LSTM recurrent neural network was the most appropriate classifier (accuracy: 81.7%; sensitivity: 81.1%; specificity: 82.2%; precision: 78.9%; AUC: 0.8165). The use of MLR, SVM and k-NN also showed a good performance (AUC ≥ 0.8).

Conclusions

This study demonstrated that machine learning techniques, using clinical and OCT data, can help establish an early diagnosis and predict the course of MS. This advance could help clinicians select more specific treatments for each MS patient. Therefore, our findings underscore the potential of RNFL thickness as a reliable MS biomarker.

中文翻译：

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光学相干断层扫描在多发性硬化症诊断和残疾预测中的机器学习

背景

多发性硬化症（MS）是一种神经退行性疾病，会影响中枢神经系统，尤其是大脑，脊髓和视神经。这种疾病的诊断是一个非常复杂的过程，通常需要很多时间。此外，在进行治疗时，每位MS患者的残疾过程都没有任何信息。出于这两个原因，本研究的目的是根据临床数据和视网膜神经纤维层（RNFL）厚度（通过光学相干断层扫描（OCT）测量），改善MS患者的MS诊断并预测其长期残疾过程。 。

材料与方法

共有104位健康对照者和108位MS患者入组，其中82位接受了10年的随访。分类算法，例如多元线性回归（MLR），支持向量机（SVM），决策树（DT），k近邻（k-NN），朴素贝叶斯（NB），整体分类器（EC）和长期短期测试了记忆（LSTM）递归神经网络，以开发两个预测模型：MS诊断模型和MS残疾过程预测模型。

结果

对于MS诊断，使用EC可获得最佳结果（准确度：87.7％；灵敏度：87.0％；特异性：88.5％；精确度：88.7％； AUC：0.8775）。与这种良好的性能一致，使用k-NN的精度为85.4％，使用SVM的精度为84.4％。并且，对于MS残障病程的长期预测，LSTM递归神经网络是最合适的分类器（准确性：81.7％；灵敏度：81.1％；特异性：82.2％；精度：78.9％； AUC：0.8165）。使用MLR，SVM和k-NN也显示出良好的性能（AUC≥0.8）。

结论

这项研究表明，使用临床和OCT数据的机器学习技术可以帮助建立早期诊断并预测MS的病程。这一进展可以帮助临床医生为每位MS患者选择更具体的治疗方法。因此，我们的发现强调了RNFL厚度作为可靠的MS生物标志物的潜力。