Discrimination of multiple sclerosis using OCT images from two different centers
Discrimination of multiple sclerosis using OCT images from two different centers
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使用来自两个不同中心的 OCT 图像区分多发性硬化症
DOI：
10.1016/j.msard.2023.104846
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发表时间：
2023
影响因子：
4
通讯作者：
Khodabandeh Z
中科院分区：
文献类型：
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作者：
Khodabandeh Z
多发性硬化（multiple sclerosis，MS）是一种由中枢神经系统脱髓鞘和轴突损伤引起的最常见的慢性炎症性疾病。通过光学相干断层扫描（OCT）的结构视网膜成像显示出作为监测MS的非侵入性生物标志物的前景。有关于人工智能（AI）在眼科疾病的横截面OCT分析中的应用的成功报告。然而，与其他眼科疾病相比，MS中各种视网膜层厚度的改变明显微妙。因此，原始的横截面OCT被替换为多层分割OCT MS和健康对照（HC）的歧视。MethodsTo符合值得信赖的AI的原则，通过可视化区域层的分类性能与建议的遮挡敏感性方法的贡献提供了可解释性。通过在新的独立数据集上测试该算法的有效性，也保证了分类的鲁棒性。通过降维方法从多层分段OCT的不同拓扑结构中选择最具鉴别力的特征。支持向量机（SVM），随机森林（RF）和人工神经网络（ANN）用于分类。病人明智的交叉验证（CV）是用来评估该算法的性能，其中的训练和测试倍包含来自不同subjects.ResultsThe最具歧视性的拓扑结构的记录被确定为正方形的大小为40像素和最有影响力的层是神经节细胞和内丛状层（GCIPL）和内核层（INL）。线性SVM的准确率为88%（以10次执行的标准偏差（std）= 0.49表示重复性），精密度78%（标准差=1.48），63%的回忆（标准差=1.35）结论所提出的分类算法有望帮助神经科医生对MS进行早期诊断。与其他研究不同的是，它采用了两个不同的数据集，与以前缺乏外部验证的研究相比，这增强了其研究结果的稳健性。这项研究旨在避免由于可用数据数量有限而使用深度学习方法，并令人信服地证明可以在不依赖深度学习技术的情况下实现良好的结果。
BackgroundMultiple sclerosis (MS) is one of the most prevalent chronic inflammatory diseases caused by demyelination and axonal damage in the central nervous system. Structural retinal imaging via optical coherence tomography (OCT) shows promise as a noninvasive biomarker for monitoring of MS. There are successful reports regarding the application of Artificial Intelligence (AI) in the analysis of cross-sectional OCTs in ophthalmologic diseases. However, the alteration of thicknesses of various retinal layers in MS is noticeably subtle compared to other ophthalmologic diseases. Therefore, raw cross-sectional OCTs are replaced with multilayer segmented OCTs for discrimination of MS and healthy controls (HCs).MethodsTo conform to the principles of trustworthy AI, interpretability is provided by visualizing the regional layer contribution to classification performance with the proposed occlusion sensitivity approach. The robustness of the classification is also guaranteed by showing the effectiveness of the algorithm while being tested on the new independent dataset. The most discriminative features from different topologies of the multilayer segmented OCTs are selected by the dimension reduction method. Support vector machine (SVM), random forest (RF), and artificial neural network (ANN) are used for classification. Patient-wise cross-validation (CV) is utilized to evaluate the performance of the algorithm, where the training and test folds contain records from different subjects.ResultsThe most discriminative topology is determined to square with a size of 40 pixels and the most influential layers are the ganglion cell and inner plexiform layer (GCIPL) and inner nuclear layer (INL). Linear SVM resulted in 88% Accuracy (with standard deviation (std) = 0.49 in 10 times of execution to indicate the repeatability), 78% precision (std=1.48), and 63% recall (std=1.35) in the discrimination of MS and HCs using macular multilayer segmented OCTs.ConclusionThe proposed classification algorithm is expected to help neurologists in the early diagnosis of MS. This paper distinguishes itself from other studies by employing two distinct datasets, which enhances the robustness of its findings in comparison with previous studies with lack of external validation. This study aims to circumvent the utilization of deep learning methods due to the limited quantity of the available data and convincingly demonstrates that favorable outcomes can be achieved without relying on deep learning techniques.