Institute of Biomedical Engineering, Bogaziçi University, Istanbul, Turkey.
IQS, Department of Industrial Engineering, Universitat Ramon Llull, Barcelona, Spain.
Comput Methods Programs Biomed. 2022 Jun;221:106825. doi: 10.1016/j.cmpb.2022.106825. Epub 2022 Apr 20.
Dementia refers to the loss of memory and other cognitive abilities. Alzheimer's disease (AD), which patients eventually die from, is the most common cause of dementia. In USA, %60 to %80 of dementia cases, are caused by AD. An estimate of 5.2 million people from all age groups have been diagnosed with AD in 2014. Mild cognitive impairment (MCI) is a preliminary stage of dementia with noticeable changes in patient's cognitive abilities. Individuals, who bear MCI symptoms, are prone to developing AD. Therefore, identification of MCI patients is very critical for a plausible treatment before it reaches to AD, the irreversible stage of this neurodegenerative disease.
Development of machine learning algorithms have recently gained a significant pace in early diagnosis of Alzheimer's disease (AD). In this study, a (2+1)D convolutional neural network (CNN) architecture has been proposed to distinguish mild cognitive impairment (MCI) from AD, based on structural magnetic resonance imaging (MRI). MRI scans of AD and MCI subjects were procured from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. 507 scans of 223 AD patients and 507 scans of 204 MCI patients were obtained for the computational experiments.
The outcome and robustness of 2D convolutions, 3D convolutions and (2+1)D convolutions were compared. The CNN algorithms incorporated 2 to 6 convolutional layers, depending on the architecture, followed by 4 pooling layers and 3 fully connected layers. (2+1)D convolutional neural network model resulted in the best classification performance with 85% auc score, in addition to an almost two times faster convergence compared to classical 3D CNN methods.
Application of (2+1)D CNN algorithm to large datasets and deeper neural network models can provide a significant advantage in speed, due to its architecture handling images in spatial and temporal dimensions separately.
痴呆是指记忆力和其他认知能力的丧失。阿尔茨海默病（AD）是最常见的痴呆症病因，患者最终会因此死亡。在美国，60%到 80%的痴呆病例是由 AD 引起的。2014 年，估计有 520 万来自所有年龄段的人被诊断患有 AD。轻度认知障碍（MCI）是痴呆的早期阶段，患者的认知能力有明显变化。患有 MCI 症状的个体易发展为 AD。因此，在 AD 不可逆阶段之前，对 MCI 患者进行识别非常关键，可以进行合理的治疗。
机器学习算法的发展最近在阿尔茨海默病（AD）的早期诊断方面取得了显著进展。在这项研究中，提出了一种基于结构磁共振成像（MRI）的（2+1）D 卷积神经网络（CNN）架构，用于区分轻度认知障碍（MCI）和 AD。AD 和 MCI 受试者的 MRI 扫描从阿尔茨海默病神经影像学倡议（ADNI）数据库中获得。进行了计算实验，获得了 223 名 AD 患者的 507 个扫描和 204 名 MCI 患者的 507 个扫描。
比较了 2D 卷积、3D 卷积和（2+1）D 卷积的结果和鲁棒性。CNN 算法包含 2 到 6 个卷积层，具体取决于架构，然后是 4 个池化层和 3 个全连接层。（2+1）D 卷积神经网络模型的分类性能最好，AUC 评分为 85%，与经典的 3D CNN 方法相比，收敛速度快近两倍。
（2+1）D CNN 算法在大型数据集和更深的神经网络模型中的应用可以提供显著的速度优势，因为其架构分别处理空间和时间维度的图像。
