Givian Helia, Calbimonte Jean-Paul
Institute of Informatics, University of Applied Sciences and Arts Western Switzerland (HES-SO Valais Wallis), TechnoPole 3, 3960 Sierre, Valais Switzerland.
The Sense Innovation and Research Center, Avenue de Provence 82, 1007 Lausanne, Vaud Switzerland.
Discov Appl Sci. 2025;7(1):27. doi: 10.1007/s42452-024-06440-w. Epub 2024 Dec 18.
Early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) is crucial to prevent their progression. In this study, we proposed the analysis of magnetic resonance imaging (MRI) based on features including; hippocampus (HC) area size, HC grayscale statistics and texture features (mean, standard deviation, skewness, kurtosis, contrast, correlation, energy, homogeneity, entropy), lateral ventricle (LV) area size, gray matter area size, white matter area size, cerebrospinal fluid area size, patient age, weight, and cognitive score. Five machine learning classifiers; K-nearest neighborhood (KNN), support vector machine (SVM), random forest (RF), decision tree (DT), and multi-layer perception (MLP) were used to distinguish between groups: cognitively normal (CN) vs AD, early MCI (EMCI) vs late MCI (LMCI), CN vs EMCI, CN vs LMCI, AD vs EMCI, and AD vs LMCI. Additionally, the correlation and dependence were calculated to examine the strength and direction of association between each extracted feature and each classification of the group. The average classification accuracies in 20 trials were 95% (SVM), 71.50% (RF), 82.58% (RF), 84.91% (SVM), 85.83% (RF), and 85.08% (RF), respectively, with the best accuracies being 100% (SVM, RF, and MLP), 83.33% (RF), 91.66% (RF), 95% (SVM, and MLP), 96.66% (RF), and 93.33% (DT). Cognitive scores, HC and LV area sizes, and HC texture features demonstrated significant potential for diagnosing AD and its subtypes for all groups. RF and SVM showed better performance in distinguishing between groups. These findings highlight the importance of using 2D-MRI to identify key features containing critical information for early diagnosis of AD.
The online version contains supplementary material available at 10.1007/s42452-024-06440-w.
阿尔茨海默病（AD）和轻度认知障碍（MCI）的早期诊断对于预防其进展至关重要。在本研究中，我们提出基于以下特征对磁共振成像（MRI）进行分析：海马体（HC）面积大小、HC灰度统计和纹理特征（均值、标准差、偏度、峰度、对比度、相关性、能量、同质性、熵）、侧脑室（LV）面积大小、灰质面积大小、白质面积大小、脑脊液面积大小、患者年龄、体重和认知评分。使用五种机器学习分类器：K近邻（KNN）、支持向量机（SVM）、随机森林（RF）、决策树（DT）和多层感知器（MLP）来区分以下几组：认知正常（CN）与AD、早期MCI（EMCI）与晚期MCI（LMCI）、CN与EMCI、CN与LMCI、AD与EMCI以及AD与LMCI。此外，计算相关性和依赖性以检查每个提取特征与每组分类之间关联的强度和方向。20次试验中的平均分类准确率分别为95%（SVM）、71.50%（RF）、82.58%（RF）、84.91%（SVM）、85.83%（RF）和85.08%（RF），最佳准确率分别为100%（SVM、RF和MLP）、83.33%（RF）、91.66%（RF）、95%（SVM和MLP）、96.66%（RF）和93.33%（DT）。认知评分、HC和LV面积大小以及HC纹理特征对所有组的AD及其亚型诊断均显示出显著潜力。RF和SVM在区分各组方面表现更好。这些发现突出了使用二维MRI识别包含AD早期诊断关键信息的关键特征的重要性。
在线版本包含可在10.1007/s42452-024-06440-w获取的补充材料。
