BACKGROUND:Effective detection of Alzheimer’s disease (AD) is still difficult in clinical practice. Therefore, establishment of AD detection model by means of machine learning is of great significance to assist AD diagnosis. OBJECTIVE:To investigate and test a new detection model aiming to help doctors diagnose AD more accurately. METHODS:Diffusion tensor images and the corresponding T1w images acquired from subjects (AD = 98, normal control (NC) = 100) are used to construct brain networks. Then, 9 types features (198×90×9 in total) are extracted from the 3D brain networks by a graph theory method. Features with low correction in both groups are selected through the Pearson correlation analysis. Finally, the selected features (198×33, 198×26, 198×30, 198×42, 198×36, 198×23, 198×29, 198×14, 198×25) are separately used into train 3 machine learning classifier based detection models in which 60% of study subjects are used for training, 20% for validation and 20% for testing. RESULTS:The best detection accuracy levels of 3 models are 90%, 98% and 90% with the corresponding sensitivity of 92%, 96%, and 72% and specificity of 88%, 100% and 94% when using a random forest classifier trained with the Shortest Path Length (SPL) features (198×14), a support vector machine trained with the Degree Centrality features (198×33), and a convolution neural network trained with SPL features, respectively. CONCLUSIONS:This study demonstrates that the new method and models not only improve the accuracy of detecting AD, but also avoid bias caused by the method of direct dimensionality reduction from high dimensional data.

中文翻译：

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基于混合扩散张量成像特征的 AD 分类

背景：阿尔茨海默病（AD）的有效检测在临床实践中仍然很困难。因此，通过机器学习建立AD检测模型对辅助AD诊断具有重要意义。目的：研究和测试一种新的检测模型，旨在帮助医生更准确地诊断AD。方法：使用扩散张量图像和从受试者（AD = 98，正常对照（NC） = 100）获取的相应 T1w 图像构建脑网络。然后，通过图论方法从 3D 大脑网络中提取 9 类特征（共 198×90×9）。通过 Pearson 相关分析选择两组中校正率低的特征。最后选择特征（198×33、198×26、198×30、198×42、198×36、198×23、198×29、198×14、198×25) 分别用于训练 3 个基于机器学习分类器的检测模型，其中 60% 的研究对象用于训练，20% 用于验证，20% 用于测试。结果：使用随机森林分类器时，3种模型的最佳检测准确率分别为90%、98%和90%，相应的灵敏度分别为92%、96%和72%，特异性分别为88%、100%和94%分别用最短路径长度 (SPL) 特征 (198×14) 训练，用度中心性特征 (198×33) 训练的支持向量机，以及用 SPL 特征训练的卷积神经网络。结论：本研究表明，新方法和模型不仅提高了检测AD的准确率，而且避免了高维数据直接降维方法带来的偏差。