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Cognitive Dysfunction

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Machine-Learning Algorithms Based on Screening Tests for Mild Cognitive Impairment.

American journal of Alzheimer's disease and other dementias
BACKGROUND: The mobile screening test system for mild cognitive impairment (mSTS-MCI) was developed and validated to address the low sensitivity and specificity of the Montreal Cognitive Assessment (MoCA) widely used clinically.

Evaluation and Prediction of Early Alzheimer's Disease Using a Machine Learning-based Optimized Combination-Feature Set on Gray Matter Volume and Quantitative Susceptibility Mapping.

Current Alzheimer research
BACKGROUND: Because Alzheimer's Disease (AD) has very complicated pattern changes, it is difficult to evaluate it with a specific factor. Recently, novel machine learning methods have been applied to solve limitations.

Addressing the Ethics of Telepresence Applications Through End-User Engagement.

Journal of Alzheimer's disease : JAD
Portacolone et al.'s Ethics Review highlights the ethical challenges associated with the implementation of telepresence devices and applications in the context of aging and dementia. In this response, we review ethical considerations as they relate t...

Multivariate Data Analysis and Machine Learning for Prediction of MCI-to-AD Conversion.

Advances in experimental medicine and biology
There has always been a need for discovering efficient and dependable Alzheimer's disease (AD) diagnostic biomarkers. Like the majority of diseases, the earlier the diagnosis, the most effective the treatment. (Semi)-automated structural magnetic res...

Integrating Convolutional Neural Networks and Multi-Task Dictionary Learning for Cognitive Decline Prediction with Longitudinal Images.

Journal of Alzheimer's disease : JAD
BACKGROUND: Disease progression prediction based on neuroimaging biomarkers is vital in Alzheimer's disease (AD) research. Convolutional neural networks (CNN) have been proved to be powerful for various computer vision research by refining reliable a...

Predicting Cognitive Impairment and Dementia: A Machine Learning Approach.

Journal of Alzheimer's disease : JAD
BACKGROUND: Efforts to identify important risk factors for cognitive impairment and dementia have to date mostly relied on meta-analytic strategies. A comprehensive empirical evaluation of these risk factors within a single study is currently lacking...

Ethical Issues Raised by the Introduction of Artificial Companions to Older Adults with Cognitive Impairment: A Call for Interdisciplinary Collaborations.

Journal of Alzheimer's disease : JAD
Due to the high costs of providing long-term care to older adults with cognitive impairment, artificial companions are increasingly considered as a cost-efficient way to provide support. Artificial companions can comfort, entertain, and inform, and e...

A Machine Learning Framework for Assessment of Cognitive and Functional Impairments in Alzheimer's Disease: Data Preprocessing and Analysis.

The journal of prevention of Alzheimer's disease
The neuropsychological scores and Functional Activities Questionnaire (FAQ) are significant to measure the cognitive and functional domain of the patients affected by the Alzheimer's Disease. Further, there are standardized dataset available today th...

Convolutional Neural Network-based MR Image Analysis for Alzheimer's Disease Classification.

Current medical imaging reviews
BACKGROUND: In this study, we used a convolutional neural network (CNN) to classify Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal control (NC) subjects based on images of the hippocampus region extracted from magnetic resonanc...