AIMC Topic: Alzheimer Disease
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[Accuracy of Classification of Cerebral Blood Flow Reduction Patterns Using Statistical Analysis Images Generated with Simulated SPECT Datasets via Deep Learning].
Nihon Hoshasen Gijutsu Gakkai zasshi
Jan 1, 2021
PURPOSE: The aim of this study was to evaluate the classification accuracy of specific blood flow reduction patterns in clinical images by deep learning using simulation data.
Olfactory Phenotypes Differentiate Cognitively Unimpaired Seniors from Alzheimer's Disease and Mild Cognitive Impairment: A Combined Machine Learning and Traditional Statistical Approach.
Journal of Alzheimer's disease : JAD
Jan 1, 2021
BACKGROUND: Olfactory dysfunction (OD) is an early symptom of Alzheimer's disease (AD). However, olfactory testing is not commonly performed to test OD in the setting of AD.
Short-Term Memory Binding Distinguishing Amnestic Mild Cognitive Impairment from Healthy Aging: A Machine Learning Study.
Journal of Alzheimer's disease : JAD
Jan 1, 2021
BACKGROUND: Amnestic mild cognitive impairment (aMCI) is the most common preclinical stage of Alzheimer's disease (AD). A strategy to reduce the impact of AD is the early aMCI diagnosis and clinical intervention. Neuroimaging, neurobiological, and ge...
Screening for Early-Stage Alzheimer's Disease Using Optimized Feature Sets and Machine Learning.
Journal of Alzheimer's disease : JAD
Jan 1, 2021
BACKGROUND: Detecting early-stage Alzheimer's disease in clinical practice is difficult due to a lack of efficient and easily administered cognitive assessments that are sensitive to very mild impairment, a likely contributor to the high rate of unde...
Brief Survey on Machine Learning in Epistasis.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2021
In biology, the term "epistasis" indicates the effect of the interaction of a gene with another gene. A gene can interact with an independently sorted gene, located far away on the chromosome or on an entirely different chromosome, and this interacti...
A Review of Automated Techniques for Assisting the Early Detection of Alzheimer's Disease with a Focus on EEG.
Journal of Alzheimer's disease : JAD
Jan 1, 2021
In this paper, we review state-of-the-art approaches that apply signal processing (SP) and machine learning (ML) to automate the detection of Alzheimer's disease (AD) and its prodromal stages. In the first part of the document, we describe the econom...
Deep Learning and Risk Score Classification of Mild Cognitive Impairment and Alzheimer's Disease.
Journal of Alzheimer's disease : JAD
Jan 1, 2021
BACKGROUND: Many neurocognitive and neuropsychological tests are used to classify early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer's disease (AD) from cognitive normal (CN). This can make it challenging for...
Sex Differences of Brain Functional Topography Revealed in Normal Aging and Alzheimer's Disease Cohort.
Journal of Alzheimer's disease : JAD
Jan 1, 2021
We applied graph theory analysis on resting-state functional magnetic resonance imaging data to evaluate sex differences of brain functional topography in normal controls (NCs), early mild cognitive impairment (eMCI), and AD patients. These metrics w...
Blinded Clinical Evaluation for Dementia of Alzheimer's Type Classification Using FDG-PET: A Comparison Between Feature-Engineered and Non-Feature-Engineered Machine Learning Methods.
Journal of Alzheimer's disease : JAD
Jan 1, 2021
BACKGROUND: Advanced machine learning methods can aid in the identification of dementia risk using neuroimaging-derived features including FDG-PET. However, to enable the translation of these methods and test their usefulness in clinical practice, it...
Recursive Support Vector Machine Biomarker Selection for Alzheimer's Disease.
Journal of Alzheimer's disease : JAD
Jan 1, 2021
BACKGROUND: There is a need for more reliable diagnostic tools for the early detection of Alzheimer's disease (AD). This can be a challenge due to a number of factors and logistics making machine learning a viable option.
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