AI Medical Compendium Topic:
Alzheimer Disease

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Diagnostic performance of actigraphy in Alzheimer's disease using a machine learning classifier - a cross-sectional memory clinic study.

Alzheimer's research & therapy
BACKGROUND: Movement patterns, activity levels and circadian rhythm are altered in Alzheimer's disease (AD) and can be assessed by actigraphy using wearable sensors. We aimed to determine the diagnostic performance of actigraphy in AD in a memory cli...

MRI-based diagnostic model for Alzheimer's disease using 3D-ResNet.

Biomedical physics & engineering express
Alzheimer's disease (AD), a progressive neurodegenerative disorder, is the leading cause of dementia worldwide and remains incurable once it begins. Therefore, early and accurate diagnosis is essential for effective intervention. Leveraging recent ad...

Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics.

Scientific reports
Dementia spectrum disorders, characterized by progressive cognitive decline, pose a significant global health burden. Early screening and diagnosis are essential for timely and accurate treatment, improving patient outcomes and quality of life. This ...

An integrated deep learning model for early and multi-class diagnosis of Alzheimer's disease from MRI scans.

Scientific reports
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely affects memory, behavior, and cognitive function. Early and accurate diagnosis is crucial for effective intervention, yet detecting subtle changes in the early stages ...

Deep normative modelling reveals insights into early-stage Alzheimer's disease using multi-modal neuroimaging data.

Alzheimer's research & therapy
BACKGROUND: Exploring the early stages of Alzheimer's disease (AD) is crucial for timely intervention to help manage symptoms and set expectations for affected individuals and their families. However, the study of the early stages of AD involves anal...

Effective integration of multi-omics with prior knowledge to identify biomarkers via explainable graph neural networks.

NPJ systems biology and applications
The rapid growth of multi-omics datasets and the wealth of biological knowledge necessitates the development of effective methods for their integration. Such methods are essential for building predictive models and identifying drug targets based on a...

EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models.

Scientific reports
In the context of lifestyle changes, stress and other environmental factors have resulted in the sudden hike in dementia globally. This necessitates investigations with respect to every horizon of the due cause for it; further on, the diagnosis and t...

Ethics From the Outset: Incorporating Ethical Considerations into the Artificial Intelligence and Technology Collaboratories for Aging Research Pilot Projects.

The journals of gerontology. Series A, Biological sciences and medical sciences
There is an urgent need to develop tools to enable older adults to live healthy, independent lives for as long as possible. To address this need, the National Institute on Aging (NIA) Artificial Intelligence and Technology Collaboratories (AITCs) for...

Early detection of Alzheimer's disease using deep learning methods.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Alzheimer's disease (AD), a leading cause of dementia, requires early detection for effective intervention. This study employs AI to analyze multimodal datasets, including clinical, biomarker, and neuroimaging data, using hybrid deep le...

IT: An interpretable transformer model for Alzheimer's disease prediction based on PET/MR images.

NeuroImage
Alzheimer's disease (AD) represents a significant challenge due to its progressive neurodegenerative impact, particularly within an aging global demographic. This underscores the critical need for developing sophisticated diagnostic tools for its ear...