AIMC Topic: Cognitive Dysfunction
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Proteome profiling of cerebrospinal fluid using machine learning shows a unique protein signature associated with APOE4 genotype.
Aging cell
Dec 25, 2024
Proteome changes associated with APOE4 variant carriage that are independent of Alzheimer's disease (AD) pathology and diagnosis are unknown. This study investigated APOE4 proteome changes in people with AD, mild cognitive impairment, and no impairme...
Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis.
JMIR aging
Dec 23, 2024
BACKGROUND: To diagnose Alzheimer disease (AD), individuals are classified according to the severity of their cognitive impairment. There are currently no specific causes or conditions for this disease.
Classifying Alzheimer's Disease Using a Finite Basis Physics Neural Network.
Microscopy research and technique
Dec 20, 2024
The disease amyloid plaques, neurofibrillary tangles, synaptic dysfunction, and neuronal death gradually accumulate throughout Alzheimer's disease (AD), resulting in cognitive decline and functional disability. The challenges of dataset quality, inte...
Explainable machine learning on clinical features to predict and differentiate Alzheimer's progression by sex: Toward a clinician-tailored web interface.
Journal of the neurological sciences
Dec 19, 2024
Alzheimer's disease (AD), the most common neurodegenerative disorder world-wide, presents sex-specific differences in its manifestation and progression, necessitating personalized diagnostic approaches. Current procedures are often costly and invasiv...
Machine Learning-Based Model for Prediction of Post-Stroke Cognitive Impairment in Acute Ischemic Stroke: A Cross-Sectional Study.
Neurology India
Dec 17, 2024
BACKGROUND AND OBJECTIVE: Early identification of post-stroke cognitive impairment (PSCI) is an important challenge for clinicians. In this study, we aimed to build a machine learning-based prediction model for PSCI and uncover potential risk factors...
DCA-Enhanced Alzheimer's detection with shearlet and deep learning integration.
Computers in biology and medicine
Dec 13, 2024
Alzheimer's dementia (AD) is a neurodegenerative disorder that affects the central nervous system, causing the cells to stop working or die. The quality of life for individuals with AD steadily declines over time. While current treatments can relieve...
A machine learning approach for identifying anatomical biomarkers of early mild cognitive impairment.
PeerJ
Dec 13, 2024
BACKGROUND: Alzheimer's Disease (AD) poses a major challenge as a neurodegenerative disorder, and early detection is critical for effective intervention. Magnetic resonance imaging (MRI) is a critical tool in AD research due to its availability and c...
Random survival forest model for early prediction of Alzheimer's disease conversion in early and late Mild cognitive impairment stages.
PloS one
Dec 13, 2024
With a clinical trial failure rate of 99.6% for Alzheimer's Disease (AD), early diagnosis is critical. Machine learning (ML) models have shown promising results in early AD prediction, with survival ML models outperforming typical classifiers by prov...
Graph Convolutional Network With Self-Supervised Learning for Brain Disease Classification.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
Brain functional network (BFN) analysis has become a popular method for identifying neurological diseases at their early stages and revealing sensitive biomarkers related to these diseases. Due to the fact that BFN is a graph with complex structure, ...
Portable, low-field magnetic resonance imaging for evaluation of Alzheimer's disease.
Nature communications
Dec 2, 2024
Portable, low-field magnetic resonance imaging (LF-MRI) of the brain may facilitate point-of-care assessment of patients with Alzheimer's disease (AD) in settings where conventional MRI cannot. However, image quality is limited by a lower signal-to-n...
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