Biomedical Engineering and Systems, Faculty of Engineering, Cairo University, Giza, Egypt.
Biomedical Engineering Program, Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah, Saudi Arabia.
PLoS One. 2020 Mar 24;15(3):e0230409. doi: 10.1371/journal.pone.0230409. eCollection 2020.
Machine learning algorithms are currently being implemented in an escalating manner to classify and/or predict the onset of some neurodegenerative diseases; including Alzheimer's Disease (AD); this could be attributed to the fact of the abundance of data and powerful computers. The objective of this work was to deliver a robust classification system for AD and Mild Cognitive Impairment (MCI) against healthy controls (HC) in a low-cost network in terms of shallow architecture and processing. In this study, the dataset included was downloaded from the Alzheimer's disease neuroimaging initiative (ADNI). The classification methodology implemented was the convolutional neural network (CNN), where the diffusion maps, and gray-matter (GM) volumes were the input images. The number of scans included was 185, 106, and 115 for HC, MCI and AD respectively. Ten-fold cross-validation scheme was adopted and the stacked mean diffusivity (MD) and GM volume produced an AUC of 0.94 and 0.84, an accuracy of 93.5% and 79.6%, a sensitivity of 92.5% and 62.7%, and a specificity of 93.9% and 89% for AD/HC and MCI/HC classification respectively. This work elucidates the impact of incorporating data from different imaging modalities; i.e. structural Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI), where deep learning was employed for the aim of classification. To the best of our knowledge, this is the first study assessing the impact of having more than one scan per subject and propose the proper maneuver to confirm the robustness of the system. The results were competitive among the existing literature, which paves the way for improving medications that could slow down the progress of the AD or prevent it.
机器学习算法目前正在被大量应用于分类和/或预测一些神经退行性疾病的发生，包括阿尔茨海默病（AD）；这可能归因于大量的数据和强大的计算机。本研究的目的是开发一个针对 AD 和轻度认知障碍（MCI）与健康对照组（HC）的鲁棒分类系统，该系统在浅层架构和处理方面具有成本效益。在这项研究中，使用的数据集是从阿尔茨海默病神经影像学倡议（ADNI）下载的。所实施的分类方法是卷积神经网络（CNN），其中扩散图和灰质（GM）体积是输入图像。纳入的扫描数量分别为 185、106 和 115，用于 HC、MCI 和 AD。采用 10 折交叉验证方案，堆叠平均扩散系数（MD）和 GM 体积的 AUC 分别为 0.94 和 0.84，准确率为 93.5%和 79.6%，敏感度为 92.5%和 62.7%，特异性为 93.9%和 89%，用于 AD/HC 和 MCI/HC 分类。本研究阐明了整合来自不同成像模式的数据的影响，即结构磁共振成像（MRI）和弥散张量成像（DTI），其中深度学习被用于分类目的。据我们所知，这是第一项评估每个被试有多个扫描的影响并提出正确的操作来确认系统的稳健性的研究。结果在现有的文献中具有竞争力，为改善可以减缓 AD 进展或预防 AD 的药物铺平了道路。
