AIMC Topic: Alzheimer Disease

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Future of Alzheimer's detection: Advancing diagnostic accuracy through the integration of qEEG and artificial intelligence.

NeuroImage
This comprehensive review examines the integration of Quantitative Electroencephalography (qEEG) and Artificial Intelligence (AI) in the detection and diagnosis of Alzheimer's Disease (AD). Through systematic analysis of 11 key studies across multipl...

A hybrid learning approach for MRI-based detection of alzheimer's disease stages using dual CNNs and ensemble classifier.

Scientific reports
Alzheimer's Disease (AD) and related dementias are significant global health issues characterized by progressive cognitive decline and memory loss. Computer-aided systems can help physicians in the early and accurate detection of AD, enabling timely ...

Rapid Fluorescence Lifetime Imaging through One-Dimensional Deep Learning Optimization.

Analytical chemistry
Traditional fluorescence lifetime imaging (FLIM) provides valuable quantitative insights for biomedical and molecular biology research, but often relies on computationally intensive datafitting methods to extract meaningful metrics. To address this l...

MAMSI: Integration of Multiassay Liquid Chromatography-Mass Spectrometry Metabolomics Data Using Multiview Machine Learning.

Analytical chemistry
Liquid chromatography-mass spectrometry (LC-MS) is a commonly used analytical technique in untargeted metabolomics. However, the diverse chemical and physical properties of metabolites often require the use of several different analytical assays for ...

Deep ensemble learning with transformer models for enhanced Alzheimer's disease detection.

Scientific reports
The progression of Alzheimer's disease is relentless, leading to a worsening of mental faculties over time. Currently, there is no remedy for this illness. Accurate detection and prompt intervention are pivotal in mitigating the progression of the di...

Enhanced particle swarm optimization for feature selection in SVM-based Alzheimer's disease diagnosis.

Scientific reports
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder marked by neuronal loss, leading to cognitive and behavioral decline. With the aging global population, AD incidence and its socioeconomic burden are increasing. Developing effectiv...

Machine-learning based strategy identifies a robust protein biomarker panel for Alzheimer's disease in cerebrospinal fluid.

Alzheimer's research & therapy
BACKGROUND: The complex pathogenesis of Alzheimer's disease (AD) has resulted in limited current biomarkers for its classification and diagnosis, necessitating further investigation into reliable universal biomarkers or combinations.

A deep learning model for early diagnosis of alzheimer's disease combined with 3D CNN and video Swin transformer.

Scientific reports
Alzheimer's disease (AD) constitutes a neurodegenerative disorder predominantly observed in the geriatric population. If AD can be diagnosed early, both in terms of prevention and treatment, it is very beneficial to patients. Therefore, our team prop...

A novel neuroimaging based early detection framework for alzheimer disease using deep learning.

Scientific reports
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive function, posing a major global health challenge. Despite its rising prevalence, particularly in low and middle-income countries, early diagnosi...

Classifying and diagnosing Alzheimer's disease with deep learning using 6735 brain MRI images.

Scientific reports
Traditional diagnostic methods for Alzheimer's disease often suffer from low accuracy and lengthy processing times, delaying crucial interventions and patient care. Deep convolutional neural networks trained on MRI data can enhance diagnostic precisi...