Alzheimer's Disease

Early Diagnosis of Alzheimer's Disease Based on Resting-State Brain Networks and Deep Learning.

Computerized healthcare has undergone rapid development thanks to the advances in medical imaging and machine learning technologies. Especially, recent progress on deep learning opens a new era for multimedia based clinical decision support. In this paper, we use deep learning with brain network and clinical relevant text information to make early diagnosis of Alzheimer's Disease (AD). The clinica...

Looking for Alzheimer's Disease morphometric signatures using machine learning techniques.

BACKGROUND: We present our results in the International challenge for automated prediction of MCI from MRI data. We evaluate the performance of MRI-based neuromorphometrics features (nMF) in the classification of Healthy Controls (HC), Mild Cognitive Impairment (MCI), converters MCI (cMCI) and Alzheimer's Disease (AD) patients.

Ensemble based on static classifier selection for automated diagnosis of Mild Cognitive Impairment.

BACKGROUND: Alzheimer's disease (AD) is the most common cause of neurodegenerative dementia in the elderly population. Scientific research is very act...

Comparative molecular docking studies of lupeol and lupenone isolated from Pueraria lobata that inhibits BACE1: Probable remedies for Alzheimer's disease.

OBJECTIVE: To discover lead lupane triterpenoid's potential isolated from Pueraria lobata roots against β-site amyloid precursor protein cleaving enzy...

Nonlinearity-aware based dimensionality reduction and over-sampling for AD/MCI classification from MRI measures.

Alzheimer's disease (AD) has been not only a substantial financial burden to the health care system but also an emotional burden to patients and their...

Alzheimer's disease and driving: review of the literature and consensus guideline from Belgian dementia experts and the Belgian road safety institute endorsed by the Belgian Medical Association.

Alzheimer's disease (AD) is a highly prevalent condition and its prevalence is expected to further increase due to the aging of the general population...

Support vector machine-based classification of neuroimages in Alzheimer's disease: direct comparison of FDG-PET, rCBF-SPECT and MRI data acquired from the same individuals.

OBJECTIVE: To conduct the first support vector machine (SVM)-based study comparing the diagnostic accuracy of T1-weighted magnetic resonance imaging (...

Homecare Robots to Improve Health and Well-Being in Mild Cognitive Impairment and Early Stage Dementia: Results From a Scoping Study.

OBJECTIVES: This scoping study is the first step of a multiphase, international project aimed at designing a homecare robot that can provide functiona...

Ethical Design of Intelligent Assistive Technologies for Dementia: A Descriptive Review.

The use of Intelligent Assistive Technology (IAT) in dementia care opens the prospects of reducing the global burden of dementia and enabling novel op...

Type 2 Diabetes Mellitus Is Associated with Social Recognition Memory Deficit and Altered Dopaminergic Neurotransmission in the Amygdala.

OBJECTIVE: Diabetic neuropathy is a chronic and often disabling condition that affects a significant number of individuals with diabetes mellitus (DM)...

Using artificial neural networks to select the parameters for the prognostic of mild cognitive impairment and dementia in elderly individuals.

BACKGROUND AND OBJECTIVES: A huge number of solutions based on computational systems have been recently developed for the classification of cognitive ...

Potential of telepresence robots to enhance social connectedness in older adults with dementia: an integrative review of feasibility.

BACKGROUND: Socially assistive robots are increasingly used as a therapeutic tool for people with dementia, as a means to improve quality of life thro...

An Alzheimers disease related genes identification method based on multiple classifier integration.

BACKGROUND AND OBJECTIVE: Alzheimers disease (AD) is a fatal neurodegenerative disease and the onset of AD is insidious. Full understanding of the AD-...

Use of a Robotic Seal as a Therapeutic Tool to Improve Dementia Symptoms: A Cluster-Randomized Controlled Trial.

OBJECTIVES: To test the effects of individual, nonfacilitated sessions with PARO (version 9), when compared against a look-alike plush toy and usual c...

AV-1451 PET imaging of tau pathology in preclinical Alzheimer disease: Defining a summary measure.

Utilizing [18F]-AV-1451 tau positron emission tomography (PET) as an Alzheimer disease (AD) biomarker will require identification of brain regions tha...

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows.

BACKGROUND: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepte...

Multi-modal discriminative dictionary learning for Alzheimer's disease and mild cognitive impairment.

BACKGROUND AND OBJECTIVE: The differentiation of mild cognitive impairment (MCI), which is the prodromal stage of Alzheimer's disease (AD), from norma...

Applying an artificial neural network model for developing a severity score for patients with hereditary amyloid polyneuropathy.

Hereditary (familial) amyloid polyneuropathy (FAP) is a systemic disease that includes a sensorimotor polyneuropathy related to transthyretin (TTR) mu...

EEG machine learning for accurate detection of cholinergic intervention and Alzheimer's disease.

Monitoring effects of disease or therapeutic intervention on brain function is increasingly important for clinical trials, albeit hampered by inter-in...

Feature selection before EEG classification supports the diagnosis of Alzheimer's disease.

OBJECTIVE: In many decision support systems, some input features can be marginal or irrelevant to the diagnosis, while others can be redundant among e...