AI Medical Compendium Topic:
Alzheimer Disease

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Predicting metabolite-disease associations based on dynamic adaptive feature learning architecture.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In recent years, the association between metabolites and complex human diseases has increasingly been recognized as a major research focus. Traditional wet-lab experiments are considered time-consuming and labor-intensive, w...

Relational Bi-level aggregation graph convolutional network with dynamic graph learning and puzzle optimization for Alzheimer's classification.

Computers in biology and medicine
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a progressive cognitive decline, necessitating early diagnosis for effective treatment. This study presents the Relational Bi-level Aggregation Graph Convolutional Network with...

A review of multimodal fusion-based deep learning for Alzheimer's disease.

Neuroscience
Alzheimer's Disease (AD) as one of the most prevalent neurodegenerative disorders worldwide, characterized by significant memory and cognitive decline in its later stages, severely impacting daily lives. Consequently, early diagnosis and accurate ass...

Enhanced EEG-based Alzheimer's disease detection using synchrosqueezing transform and deep transfer learning.

Neuroscience
The most prevalent type of dementia and a progressive neurodegenerative disease, Alzheimer's disease has a major influence on day-to-day functioning due to memory loss, cognitive decline, and behavioral problems. By using synchrosqueezing representat...

An AI-assisted fluorescence microscopic system for screening mitophagy inducers by simultaneous analysis of mitophagic intermediates.

Nature communications
Mitophagy, the selective autophagic elimination of mitochondria, is essential for maintaining mitochondrial quality and cell homeostasis. Impairment of mitophagy flux, a process involving multiple sequential intermediates, is implicated in the onset ...

Deep learning reveals pathology-confirmed neuroimaging signatures in Alzheimer's, vascular and Lewy body dementias.

Brain : a journal of neurology
Concurrent neurodegenerative and vascular pathologies pose a diagnostic challenge in the clinical setting, with histopathology remaining the definitive modality for dementia-type diagnosis. To address this clinical challenge, we introduce a neuropath...

Radiomics of PET Using Neural Networks for Prediction of Alzheimer's Disease Diagnosis.

Statistics in medicine
Positron emission tomography (PET) imaging technology is widely used for diagnosing Alzheimer's disease (AD) in people with dementia. Although various computational methods have been proposed for diagnosis of AD using PET images, prediction of diseas...

ML-Driven Alzheimer's disease prediction: A deep ensemble modeling approach.

SLAS technology
Alzheimer's disease (AD) is a progressive neurological disorder characterized by cognitive decline due to brain cell death, typically manifesting later in life.Early and accurate detection is critical for effective disease management and treatment. T...

Machine learning prediction prior to onset of mild cognitive impairment using T1-weighted magnetic resonance imaging radiomic of the hippocampus.

Asian journal of psychiatry
BACKGROUND: Early identification of individuals who progress from normal cognition (NC) to mild cognitive impairment (MCI) may help prevent cognitive decline. We aimed to build predictive models using radiomic features of the bilateral hippocampus in...

Deep-Diffeomorphic Networks for Conditional Brain Templates.

Human brain mapping
Deformable brain templates are an important tool in many neuroimaging analyses. Conditional templates (e.g., age-specific templates) have advantages over single population templates by enabling improved registration accuracy and capturing common proc...