AIMC Topic: Alzheimer Disease

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Identification of novel DYRK1A inhibitors as treatment options for alzheimer's disease through comprehensive in silico approaches.

Scientific reports
This study aims to identify potential DYRK1A inhibitors from a curated database and utilize a QSAR model to predict the bioactivity of drug compounds in inhibiting the enzyme involved in tau protein oligomerization, a key process in AD pathology. 192...

Automated Speech Markers of Alzheimer Dementia: Test of Cross-Linguistic Generalizability.

Journal of medical Internet research
BACKGROUND: Automated speech and language analysis (ASLA) is gaining momentum as a noninvasive, affordable, and scalable approach for the early detection of Alzheimer disease (AD). Nevertheless, the literature presents 2 notable limitations. First, m...

A novel approach hybrid of ensemble learning and 3-D CNN mechanism: early-stage diagnosis of Alzheimer's disease using EEG signals.

Scientific reports
Alzheimer's disease (AD) is a progressive neurological disorder that causes brain cell degeneration and leads to dementia. Early and accurate detection of AD is crucial, as it allows timely treatment before the brain suffers permanent damage. In rece...

A radiomics model predicts progression from mild cognitive impairment to alzheimer's disease using structural MRI.

Scientific reports
The aim of this study is to build and validate a model based on structural magnetic resonance imaging (sMRI) to predict the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD). A total of 343 patients with MCI were selected fro...

IoMT driven Alzheimer's prediction model empowered with transfer learning and explainable AI approach in healthcare 5.0.

Scientific reports
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the primary cause of dementia, responsible for 60-70% of global cases. It severely affects memory, cognitive function, and daily independence, placing a substantial emotional an...

Comparative Diagnostic Accuracy of AI-Assisted Fluorine-18 Fluorodeoxyglucose Positron Emission Tomography Versus Structural Magnetic Resonance Imaging in Alzheimer Disease: Systematic Review and Meta-Analysis.

JMIR aging
BACKGROUND: Neuroimaging is crucial in the diagnosis of Alzheimer disease (AD). In recent years, artificial intelligence (AI)-based neuroimaging technology has rapidly developed, providing new methods for accurate diagnosis of AD, but its performance...

Robust detection of femtogram-level Alzheimer's biomarkers using machine learning-enhanced graphene biosensors.

Biosensors & bioelectronics
Early diagnosis of Alzheimer's disease (AD) requires blood biomarker tests sensitive to femtogram/mL concentrations. Graphene field-effect transistors (GFETs) are promising for this application, but suffer from device-to-device variability and requir...

An ensemble model based on transfer learning for the early detection of Alzheimer's disease.

Scientific reports
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by the gradual decline in cognitive functions, particularly memory and reasoning. Early detection, especially during cognitive impairment (MCI) stage, is crucial for t...

Centiloid values from deep learning-based CT parcellation: a valid alternative to freesurfer.

Alzheimer's research & therapy
BACKGROUND: Amyloid PET/CT is essential for quantifying amyloid-beta (Aβ) deposition in Alzheimer's disease (AD), with the Centiloid (CL) scale standardizing measurements across imaging centers. However, MRI-based CL pipelines face challenges: high c...

petBrain: a new pipeline for amyloid, Tau tangles and neurodegeneration quantification using PET and MRI.

Alzheimer's research & therapy
INTRODUCTION: Quantification of amyloid plaques (A), neurofibrillary tangles (T), and neurodegeneration (N) using PET and MRI is critical for Alzheimer's disease (AD) diagnosis and prognosis. Existing pipelines face limitations regarding processing t...