Machine Learning-Based Framework for Differential Diagnosis Between Vascular Dementia and Alzheimer's Disease Using Structural MRI Features

Yineng Zheng; Haoming Guo; Lijuan Zhang; Jiahui Wu; Qi Li; Fajin Lv

doi:10.3389/fneur.2019.01097

Machine Learning-Based Framework for Differential Diagnosis Between Vascular Dementia and Alzheimer's Disease Using Structural MRI Features

Front Neurol. 2019 Oct 25:10:1097. doi: 10.3389/fneur.2019.01097. eCollection 2019.

Authors

Yineng Zheng¹, Haoming Guo¹, Lijuan Zhang¹, Jiahui Wu¹, Qi Li², Fajin Lv¹

Affiliations

¹ Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
² Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Abstract

Background and Objective: Vascular dementia (VaD) and Alzheimer's disease (AD) could be characterized by the same syndrome of dementia. This study aims to assess whether multi-parameter features derived from structural MRI can serve as the informative biomarker for differential diagnosis between VaD and AD using machine learning. Methods: A total of 93 patients imaged with brain MRI including 58 AD and 35 VaD confirmed by two chief physicians were recruited in this study from June 2013 to July 2019. Automated brain tissue segmentation was performed by the AccuBrain tool to extract multi-parameter volumetric measurements from different brain regions. Firstly, a total of 62 structural MRI biomarkers were addressed to select significantly different features between VaD and AD for dimensionality reduction. Then, the least absolute shrinkage and selection operator (LASSO) was further used to construct a feature set that is fed into a support vector machine (SVM) classifier. To ensure the unbiased evaluation of model performance, a comparative study of classification models was implemented by using different machine learning algorithms in order to determine which performs best in the application of differential diagnosis between VaD and AD. The diagnostic performance of the classification models was evaluated by the quantitative metrics derived from the receiver operating characteristic curve (ROC). Results: The experimental results demonstrate that the SVM with RBF achieved an encouraging performance with sensitivity (SEN), specificity (SPE), and accuracy (ACC) values of 82.65%, 87.17%, and 84.35%, respectively (AUC = 0.861, 95% CI = 0.820-0.902), for the differential diagnosis between VaD and AD. Conclusions: The proposed computer-aided diagnosis method highlights the potential of combining structural MRI and machine learning to support clinical decision making in distinction of VaD vs. AD.

Keywords: SVM; VaD and AD; computer-aided diagnosis; machine learning; structural MRI.