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Machine learning algorithms are currently being implemented in an escalating manner to classify and/or predict the onset of some neurodegenerative diseases; including Alzheimer's Disease (AD); this could be attributed to the fact of the abundance of data and powerful computers. The objective of this work was to deliver a robust classification system for AD and Mild Cognitive Impairment (MCI) against healthy controls (HC) in a low-cost network in terms of shallow architecture and processing. In this study, the dataset included was downloaded from the Alzheimer's disease neuroimaging initiative (ADNI). The classification methodology implemented was the convolutional neural network (CNN), where the diffusion maps, and gray-matter (GM) volumes were the input images. The number of scans included was 185, 106, and 115 for HC, MCI and AD respectively. Ten-fold cross-validation scheme was adopted and the stacked mean diffusivity (MD) and GM volume produced an AUC of 0.94 and 0.84, an accuracy of 93.5% and 79.6%, a sensitivity of 92.5% and 62.7%, and a specificity of 93.9% and 89% for AD/HC and MCI/HC classification respectively. This work elucidates the impact of incorporating data from different imaging modalities; i.e. structural Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI), where deep learning was employed for the aim of classification. To the best of our knowledge, this is the first study assessing the impact of having more than one scan per subject and propose the proper maneuver to confirm the robustness of the system. The results were competitive among the existing literature, which paves the way for improving medications that could slow down the progress of the AD or prevent it.
This article was published in the following journal.
Name: PloS one
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The use of diffusion ANISOTROPY data from diffusion magnetic resonance imaging results to construct images based on the direction of the faster diffusing molecules.
Methods developed to aid in the interpretation of ultrasound, radiographic images, etc., for diagnosis of disease.
A diagnostic technique that incorporates the measurement of molecular diffusion (such as water or metabolites) for tissue assessment by MRI. The degree of molecular movement can be measured by changes of apparent diffusion coefficient (ADC) with time, as reflected by tissue microstructure. Diffusion MRI has been used to study BRAIN ISCHEMIA and tumor response to treatment.
Abnormal structures located chiefly in distal dendrites and, along with NEUROFIBRILLARY TANGLES and SENILE PLAQUES, constitute the three morphological hallmarks of ALZHEIMER DISEASE. Neuropil threads are made up of straight and paired helical filaments which consist of abnormally phosphorylated microtubule-associated tau proteins. It has been suggested that the threads have a major role in the cognitive impairment seen in Alzheimer disease.
Vaccines or candidate vaccines used to prevent or treat ALZHEIMER DISEASE.
Neurology - Central Nervous System (CNS)
Alzheimer's Disease Anesthesia Anxiety Disorders Autism Bipolar Disorders Dementia Epilepsy Multiple Sclerosis (MS) Neurology Pain Parkinson's Disease Sleep Disorders Neurology is the branch of me...