AIMC Topic: Alzheimer Disease
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An Efficient Combination among sMRI, CSF, Cognitive Score, and 4 Biomarkers for Classification of AD and MCI Using Extreme Learning Machine.
Computational intelligence and neuroscience
Jun 4, 2020
Alzheimer's disease (AD) is the most common cause of dementia and a progressive neurodegenerative condition, characterized by a decline in cognitive function. Symptoms usually appear gradually and worsen over time, becoming severe enough to interfere...
AD-NET: Age-adjust neural network for improved MCI to AD conversion prediction.
NeuroImage. Clinical
Jun 1, 2020
The prediction of Mild Cognitive Impairment (MCI) patients who are at higher risk converting to Alzheimer's Disease (AD) is critical for effective intervention and patient selection in clinical trials. Different biomarkers including neuroimaging have...
Grab-AD: Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer's Disease.
Human brain mapping
May 4, 2020
Alzheimer's disease (AD) is associated with disruptions in brain activity and networks. However, there is substantial inconsistency among studies that have investigated functional brain alterations in AD; such contradictions have hindered efforts to ...
Deep learning based mild cognitive impairment diagnosis using structure MR images.
Neuroscience letters
May 4, 2020
Mild cognitive impairment (MCI) is an early sign of Alzheimer's disease (AD) which is the fourth leading disease mostly found in the aged population. Early intervention of MCI will possibly delay the progress towards AD, and this makes it very import...
Validation of machine learning models to detect amyloid pathologies across institutions.
Acta neuropathologica communications
Apr 28, 2020
Semi-quantitative scoring schemes like the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) are the most commonly used method in Alzheimer's disease (AD) neuropathology practice. Computational approaches based on machine learning ha...
Automatic assessment of Alzheimer's disease diagnosis based on deep learning techniques.
Computers in biology and medicine
Apr 18, 2020
Early detection is crucial to prevent the progression of Alzheimer's disease (AD). Thus, specialists can begin preventive treatment as soon as possible. They demand fast and precise assessment in the diagnosis of AD in the earliest and hardest to det...
Caregiver perspectives on a smart home-based socially assistive robot for individuals with Alzheimer's disease and related dementia.
Disability and rehabilitation. Assistive technology
Apr 17, 2020
: Innovative assistive technology can address aging-in-place and caregiving needs of individuals with Alzheimer's disease and related dementia (ADRD). The purpose of this study was to beta-test a novel socially assistive robot (SAR) with a cohort of ...
Deep learning prediction of falls among nursing home residents with Alzheimer's disease.
Geriatrics & gerontology international
Apr 8, 2020
AIM: This study aimed to use a convolutional neural network (CNN) to investigate the associations between the time of falling and multiple complicating factors, including age, dementia severity, lower extremity strength and physical function, among n...
Application of Generalized Split Linearized Bregman Iteration algorithm for Alzheimer's disease prediction.
Aging
Apr 5, 2020
In this paper, we applied a novel method for the detection of Alzheimer's disease (AD) based on a structural magnetic resonance imaging (sMRI) dataset. Specifically, the method involved a new classification algorithm of machine learning, named Genera...
Multiview learning for understanding functional multiomics.
PLoS computational biology
Apr 2, 2020
The molecular mechanisms and functions in complex biological systems currently remain elusive. Recent high-throughput techniques, such as next-generation sequencing, have generated a wide variety of multiomics datasets that enable the identification ...
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