Alzheimer's Disease

Plasma protein-based identification of neuroimage-driven subtypes in mild cognitive impairment via protein-protein interaction aware explainable graph propagational network.

As an early indicator of dementia, mild cognitive impairment (MCI) requires specialized treatment according to its subtypes for the effective prevention and management of dementia progression. Based on the neuropathological characteristics, MCI can be classified into Alzheimer's disease (AD)-related cognitive impairment (ADCI) and subcortical vascular cognitive impairment (SVCI), being more likely...

Utilizing graph neural networks for adverse health detection and personalized decision making in sensor-based remote monitoring for dementia care.

BACKGROUND: Sensor-based remote health monitoring is increasingly used to detect adverse health in people living with dementia (PLwD) at home, aiming to prevent hospitalizations and reduce caregiver burden. However, home sensor data is often noisy, overly granular, and suffers from unreliable labeling, data drift and high variability between households. Current anomaly detection methods lack gener...

Deciphering the role of lipid metabolism-related genes in Alzheimer's disease: a machine learning approach integrating Traditional Chinese Medicine.

BACKGROUND: Alzheimer's disease (AD) represents a progressive neurodegenerative disorder characterized by the accumulation of misfolded amyloid beta p...

Tackling APOE's structural challenges via in silico modeling in the era of neural networks: Can AlphaFold II help circumvent the problem of lacking full-length protein structure?

The APOE gene, encoding apolipoprotein E, is a predictor of longevity and age-related diseases. Despite numerous genetic studies, the data on molecula...

Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models.

Computer-vision and machine-learning (ML) approaches are being developed to provide scalable, unbiased, and sensitive methods to assess mouse behavior...

MRI-based mild cognitive impairment and Alzheimer's disease classification using an algorithm of combination of variational autoencoder and other machine learning classifiers.

BACKGROUND: Correctly diagnosing mild cognitive impairment (MCI) and Alzheimer's disease (AD) is important for patient selection in drug discovery. Re...

Alzheimer's disease and other memory disorders in the age of AI: reflection and perspectives on the 120th anniversary of the birth of Dr. John von Neumann.

Two themes are coming to the forefront in this decade: Cognitive impairment of an aging population and the quantum leap in developing artificial intel...

Graph neural networks for electroencephalogram analysis: Alzheimer's disease and epilepsy use cases.

Electroencephalography (EEG) is widely used as a non-invasive technique for the diagnosis of several brain disorders, including Alzheimer's disease an...

A genetic programming Rician noise reduction and explainable deep learning model for Alzheimer's diseases severity prediction.

BACKGROUND: Degradation of magnetic resonance imaging (MRI) remains a challenging issue, with noise being a key damaging component introduced due to a...

An Ensemble Learning Algorithm for Cognitive Evaluation by an Immersive Virtual Reality Supermarket.

Early screening for Mild Cognitive Impairment (MCI) is crucial in delaying cognitive deterioration and treating dementia. Conventional neuropsychologi...

A support vector machine-based approach to guide the selection of a pseudo-reference region for brain PET quantification.

A Support Vector Machine (SVM) based approach was developed to identify a pseudo-reference region for brain PET scans with the aim of reducing intersc...

Deep convolutional neural network-based identification and biological evaluation of MAO-B inhibitors.

Parkinson's disease (PD) is one of the most prominent motor disorder of adult-onset dementia connected to memory and other cognitive abilities. Indivi...

A deep learning approach for non-invasive Alzheimer's monitoring using microwave radar data.

Over 50 million people globally suffer from Alzheimer's disease (AD), emphasizing the need for efficient, early diagnostic tools. Traditional methods ...

Automated Neural Architecture Search for Cardiac Amyloidosis Classification from [18F]-Florbetaben PET Images.

Medical image classification using convolutional neural networks (CNNs) is promising but often requires extensive manual tuning for optimal model defi...

Pain Assessment for Patients with Dementia and Communication Impairment: Feasibility Study of the Usage of Artificial Intelligence-Enabled Wearables.

BACKGROUND: Recent studies on machine learning have shown the potential to provide new methods with which to assess pain through the measurement of si...

Deep learning-based binary classification of beta-amyloid plaques using 18 F florapronol PET.

PURPOSE: This study aimed to investigate a deep learning model to classify amyloid plaque deposition in the brain PET images of patients suspected of ...

Explainability of CNN-based Alzheimer's disease detection from online handwriting.

With over 55 million people globally affected by dementia and nearly 10 million new cases reported annually, Alzheimer's disease is a prevalent and ch...

Can robots lie? A posthumanist approach to robotic animals and deceptive practices in dementia care.

Robotic animals are designed to resemble real, living animals, but at the same time, dementia care guidelines and policies often emphasize the value o...

3-1-3 Weight averaging technique-based performance evaluation of deep neural networks for Alzheimer's disease detection using structural MRI.

Alzheimer's disease (AD) is a progressive neurological disorder. It is identified by the gradual shrinkage of the brain and the loss of brain cells. T...

Early diagnosis of Alzheimer's Disease based on multi-attention mechanism.

Alzheimer's Disease is a neurodegenerative disorder, and one of its common and prominent early symptoms is language impairment. Therefore, early diagn...