Latest AI and machine learning research in alzheimer's disease for healthcare professionals.
Functional connectivity (FC) matrices measure the regional interactions in the brain and have been widely used in neurological brain disease classification. A brain network, also named as connectome, could form a graph structure naturally, the nodes of which are brain regions and the edges are interregional connectivity. Thus, in this study, we proposed novel graph convolutional networks (GCNs) to...
Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of Alzheimer's disease. However, classification performance obtained with different approaches is difficult to compare because of variations in components such as input data, participant selection, image preprocessing, feature extraction, f...
With the increasing size of datasets used in medical imaging research, the need for automated data curation is arising. One important data curation ta...
AIM AND OBJECTIVE: Fast and accurate diagnosis of Alzheimer's disease is very important for the care and further treatment of patients. Along with the...
BACKGROUND: In recent years, many convolutional neural networks (CNN) have been proposed for the classification of Alzheimer's disease. Due to memory ...
BACKGROUND: Detailed pathology analysis and morphological quantification is tedious and prone to errors. Automatic image analysis can help to increase...
BACKGROUND: Using non-pharmacological interventions is a current approach in dementia care to manage responsive behaviors, to maintain functional capa...
BACKGROUND: Sociodemographic data indicate the progressive increase in life expectancy and the prevalence of Alzheimer's disease (AD). AD is raised as...
BACKGROUND: Quantitatively predicting the progression of Alzheimer's disease (AD) in an individual on a continuous scale, such as the Alzheimer's Dise...
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 n...
BACKGROUND: Amyloid-β (Aβ) evaluation in amnestic mild cognitive impairment (aMCI) patients is important for predicting conversion to Alzheimer's dise...
BACKGROUND: Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer's disease. Early diagnosis of MCI can allow for treatment to ...
BACKGROUND: Advanced machine learning methods can aid in the identification of dementia risk using neuroimaging-derived features including FDG-PET. Ho...
We applied graph theory analysis on resting-state functional magnetic resonance imaging data to evaluate sex differences of brain functional topograph...
BACKGROUND: Many neurocognitive and neuropsychological tests are used to classify early mild cognitive impairment (EMCI), late mild cognitive impairme...
In this paper, we review state-of-the-art approaches that apply signal processing (SP) and machine learning (ML) to automate the detection of Alzheime...
BACKGROUND: Detecting early-stage Alzheimer's disease in clinical practice is difficult due to a lack of efficient and easily administered cognitive a...
BACKGROUND: Amnestic mild cognitive impairment (aMCI) is the most common preclinical stage of Alzheimer's disease (AD). A strategy to reduce the impac...
BACKGROUND: Olfactory dysfunction (OD) is an early symptom of Alzheimer's disease (AD). However, olfactory testing is not commonly performed to test O...
BACKGROUND: Possible benefits of makeup therapy, in terms of immediate and late effects on cognitive and affective functions, have not been fully prov...