Machine Learning Techniques for AD/MCI Diagnosis and Prognosis
نویسندگان
چکیده
In the past two decades, machine learning tools have been extensively applied for the detection of neurologic or neuropsychiatric disorders, especially Alzheimer’s disease (AD) and its prodrome, mild cognitive impairment (MCI). This chapter presents some latest developments in application of machine learning tools to AD and MCI diagnosis and prognosis. We will divide our discussion into single modality and multimodality approaches. We will discuss how various biomarkers as well as connectivity networks can be extracted from the individual modalities, i.e., structural T1-weighted imaging, diffusion-tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), for effective diagnosis and prognosis. We will further demonstrate how these modalities can be fused for further performance improvement.
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