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Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer's disease

Published:January 10, 2018DOI:https://doi.org/10.1016/j.trsl.2018.01.001
      Alzheimer's disease (AD) is a major neurodegenerative disease and the most common cause of dementia. Currently, no treatment exists to slow down or stop the progression of AD. There is converging belief that disease-modifying treatments should focus on early stages of the disease, that is, the mild cognitive impairment (MCI) and preclinical stages. Making a diagnosis of AD and offering a prognosis (likelihood of converting to AD) at these early stages are challenging tasks but possible with the help of multimodality imaging, such as magnetic resonance imaging (MRI), fluorodeoxyglucose (FDG)-positron emission topography (PET), amyloid-PET, and recently introduced tau-PET, which provides different but complementary information. This article is a focused review of existing research in the recent decade that used statistical machine learning and artificial intelligence methods to perform quantitative analysis of multimodality image data for diagnosis and prognosis of AD at the MCI or preclinical stages. We review the existing work in 3 subareas: diagnosis, prognosis, and methods for handling modality-wise missing data—a commonly encountered problem when using multimodality imaging for prediction or classification. Factors contributing to missing data include lack of imaging equipment, cost, difficulty of obtaining patient consent, and patient drop-off (in longitudinal studies). Finally, we summarize our major findings and provide some recommendations for potential future research directions.

      Abbreviations:

      AD (Alzheimer's disease), MCI (mild cognitive impairment), MRI (magnetic resonance imaging), FDG (fluorodeoxyglucose), PET (positron emission topography), FDA (Food and Drug Administration), NIA (National Institute of Aging), AA (Alzheimer's Association), IWG (International Working Group), ML (machine learning), AI (artificial intelligence), NC (normal control), CSF (cerebrospinal fluid), APOE (apolipoprotein E), SNAP (suspected non-Alzheimer pathology), SVDs (singular-vector decompositions), SUVR (standard uptake value ratio), SVM (support vector machine), ICA (independent component analysis), MKL (multiple kernel learning), ROI (region of interest), ADNI (Alzheimer's Disease Neuroimaging Initiative), MMSE (Mini-Mental State Examination), ADAS-Cog (Alzheimer's Disease Assessment Scale-Cognitive subscale), GP (Gaussian process), NPSEs (Neuropsychological Status Exam Score), VBM (voxel-based morphometry), TBM (tensor-based morphometry), AUC (area under the curve), PLS (partial least square), CDR (clinical dementia rating), CNN (convolutional neural network), DL (deep learning)
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