Alzheimer's Disease

Deep learning-based brain age prediction in normal aging and dementia.

Brain aging is accompanied by patterns of functional and structural change. Alzheimer's disease (AD)...

Deep-Learning-Assisted Stratification of Amyloid Beta Mutants Using Drying Droplet Patterns.

The development of simple and accurate methods to predict mutations in proteins remains an unsolved ...

Brain network connectivity feature extraction using deep learning for Alzheimer's disease classification.

Early diagnosis and therapeutic intervention for Alzheimer's disease (AD) is currently the only viab...

Identifying Blood Biomarkers for Dementia Using Machine Learning Methods in the Framingham Heart Study.

Blood biomarkers for dementia have the potential to identify preclinical disease and improve partici...

Therapeutic use of the humanoid robot, Telenoid, with older adults: A critical interpretive synthesis review.

This review sought to critically evaluate the use of the teleoperated humanoid robotic communication...

Deep learning from multiple experts improves identification of amyloid neuropathologies.

Pathologists can label pathologies differently, making it challenging to yield consistent assessment...

Dietary administration of D-chiro-inositol attenuates sex-specific metabolic imbalances in the 5xFAD mouse model of Alzheimer's disease.

Increasing evidence shows that hypothalamic dysfunction, insulin resistance, and weight loss precede...

Predicting Abnormal Amyloid Burden: Can Machine Learning Become a Future Tool in Preventive Neurology?

Dementia risk predictions from German claims data using methods of machine learning.

INTRODUCTION: We examined whether German claims data are suitable for dementia risk prediction, how ...

Predicting brain structural network using functional connectivity.

Uncovering the non-trivial brain structure-function relationship is fundamentally important for reve...

Socially assistive robots for people with dementia: Systematic review and meta-analysis of feasibility, acceptability and the effect on cognition, neuropsychiatric symptoms and quality of life.

BACKGROUND: There is increasing interest in using robots to support dementia care but little consens...

Predicting Brain Age Using Machine Learning Algorithms: A Comprehensive Evaluation.

Machine learning (ML) algorithms play a vital role in the brain age estimation frameworks. The impac...

Formulation and optimization of silymarin-encapsulated binary micelles for enhanced amyloid disaggregation activity.

Silymarin (SLY) is a natural hydrophobic polyphenol that possesses antioxidant and amyloid fibril (A...

A Single Model Deep Learning Approach for Alzheimer's Disease Diagnosis.

Early and accurate diagnosis of Alzheimer's disease (AD) and its prodromal period mild cognitive imp...

Assessment of the predictive potential of cognitive scores from retinal images and retinal fundus metadata via deep learning using the CLSA database.

Accumulation of beta-amyloid in the brain and cognitive decline are considered hallmarks of Alzheime...

The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects.

Research on segmentation of the hippocampus in magnetic resonance images through deep learning convo...

A deep learning MRI approach outperforms other biomarkers of prodromal Alzheimer's disease.

BACKGROUND: The three core pathologies of Alzheimer's disease (AD) are amyloid pathology, tau pathol...

Analysis of Factors Affecting Cranial Nerve Function of Patients With Vascular Mild Cognitive Impairment Through Functional Magnetic Resonance Imaging Under Artificial Intelligence Environment.

The study aimed to explore the risk factors of effects of patients with vascular mild cognitive impa...

Reduction of multiple-caregiver assistance through the long-term use of a transfer support robot in a nursing facility.

The long-term use of transfer support robots in nursing facilities is an important option for improv...

Single-molecule fluorescence imaging and deep learning reveal highly heterogeneous aggregation of amyloid-β 42.

Polymorphism in the structure of amyloid fibrils suggests the existence of many different assembly p...

EvoRator: Prediction of Residue-level Evolutionary Rates from Protein Structures Using Machine Learning.

Measuring evolutionary rates at the residue level is indispensable for gaining structural and functi...