Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes

卷:8

Lucia Billeci¹  Lorenzo Bachi¹  Alessandro Tonacci¹  Asia Badolato² 

[1] Institute of Clinical Physiology-National Research Council of Italy (IFC-CNR), Via Moruzzi, 1, 56124 Pisa, Italy;

[2] School of Engineering, University of Pisa, Largo Lucio Lazzarino, 1, 56122 Pisa, Italy;

关键词: Alzheimer’s disease;    mild cognitive impairment;    diffusion tensor imaging;    magnetic resonance imaging;    machine learning;    support vector machine;   

DOI  :  10.3390/pr8091071

来源: DOAJ

【 摘 要 】

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

【 授权许可】

Unknown