Alzheimer's Disease

Binary Classification of Alzheimer's Disease Using sMRI Imaging Modality and Deep Learning.

Alzheimer's disease (AD) is an irreversible devastative neurodegenerative disorder associated with p...

Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker.

The aging process is accompanied by changes in the brain's cortex at many levels. There is growing i...

Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging.

A fundamental problem of supervised learning algorithms for brain imaging applications is that the n...

A new method to predict anomaly in brain network based on graph deep learning.

Functional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and ...

Deep learning with long short-term memory networks for classification of dementia related travel patterns.

Wandering pattern classification is important for early recognition of cognitive deterioration and o...

Segmentation of Tau Stained Alzheimers Brain Tissue Using Convolutional Neural Networks.

Alzheimers disease is characterized by complex changes in brain tissue including the accumulation of...

A Two Cascaded Network Integrating Regional-based YOLO and 3D-CNN for Cerebral Microbleeds Detection.

Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagn...

Can robots express facial emotions dominantly enough for use in dementia care?

MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide.

Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ...

Using Unsupervised Learning to Identify Clinical Subtypes of Alzheimer's Disease in Electronic Health Records.

Identifying subtypes of Alzheimer's Disease (AD) can lead towards the creation of personalized inter...

Automatic Extraction of Risk Factors for Dialysis Patients from Clinical Notes Using Natural Language Processing Techniques.

Studies have shown that mental health and comorbidities such as dementia, diabetes and cardiovascula...

Automated MRI-Based Deep Learning Model for Detection of Alzheimer's Disease Process.

In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical ...

The Use of Random Forests to Identify Brain Regions on Amyloid and FDG PET Associated With MoCA Score.

PURPOSE: The aim of this study was to evaluate random forests (RFs) to identify ROIs on F-florbetapi...

Investigating Predictors of Cognitive Decline Using Machine Learning.

OBJECTIVES: Genetic risks for cognitive decline are not modifiable; however their relative importanc...

Neuropsychiatric symptoms as predictors of conversion from MCI to dementia: a machine learning approach.

OBJECTIVES: To use a Machine Learning (ML) approach to compare Neuropsychiatric Symptoms (NPS) in pa...

Vitamin D in mild cognitive impairment and Alzheimer's disease. A study in older Greek adults.

BACKGROUND: In recent years, accumulating evidence has linked vitamin D deficiency to cognitive dysf...

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer's Disease: A Systematic Review.

BACKGROUND: Language is a valuable source of clinical information in Alzheimer's disease, as it decl...

A Community-Based Study Identifying Metabolic Biomarkers of Mild Cognitive Impairment and Alzheimer's Disease Using Artificial Intelligence and Machine Learning.

BACKGROUND: Currently, there is no objective, clinically available tool for the accurate diagnosis o...

Utility of MemTrax and Machine Learning Modeling in Classification of Mild Cognitive Impairment.

BACKGROUND: The widespread incidence and prevalence of Alzheimer's disease and mild cognitive impair...

Validation of Random Forest Machine Learning Models to Predict Dementia-Related Neuropsychiatric Symptoms in Real-World Data.

BACKGROUND: Neuropsychiatric symptoms (NPS) are the leading cause of the social burden of dementia b...

High-accuracy Automated Diagnosis of Parkinson's Disease.

PURPOSE: Parkinson's disease (PD), which is the second most common neurodegenerative disease followi...