AIMC Topic: Alzheimer Disease
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A multimodal machine learning model for predicting dementia conversion in Alzheimer's disease.
Scientific reports
May 29, 2024
Alzheimer's disease (AD) accounts for 60-70% of the population with dementia. Mild cognitive impairment (MCI) is a diagnostic entity defined as an intermediate stage between subjective cognitive decline and dementia, and about 10-15% of people annual...
Alzheimer's disease diagnosis from multi-modal data via feature inductive learning and dual multilevel graph neural network.
Medical image analysis
May 28, 2024
Multi-modal data can provide complementary information of Alzheimer's disease (AD) and its development from different perspectives. Such information is closely related to the diagnosis, prevention, and treatment of AD, and hence it is necessary and c...
Geodesic shape regression based deep learning segmentation for assessing longitudinal hippocampal atrophy in dementia progression.
NeuroImage. Clinical
May 26, 2024
Longitudinal hippocampal atrophy is commonly used as progressive marker assisting clinical diagnose of dementia. However, precise quantification of the atrophy is limited by longitudinal segmentation errors resulting from MRI artifacts across multipl...
Intelligent prediction of Alzheimer's disease via improved multifeature squeeze-and-excitation-dilated residual network.
Scientific reports
May 25, 2024
This study aimed to address the issue of larger prediction errors existing in intelligent predictive tasks related to Alzheimer's disease (AD). A cohort of 487 enrolled participants was categorized into three groups: normal control (138 individuals),...
Detecting Alzheimer's Disease Stages and Frontotemporal Dementia in Time Courses of Resting-State fMRI Data Using a Machine Learning Approach.
Journal of imaging informatics in medicine
May 23, 2024
Early, accurate diagnosis of neurodegenerative dementia subtypes such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) is crucial for the effectiveness of their treatments. However, distinguishing these conditions becomes challenging whe...
Novel drug discovery: Advancing Alzheimer's therapy through machine learning and network pharmacology.
European journal of pharmacology
May 23, 2024
Alzheimer's disease (AD), marked by tau tangles and amyloid-beta plaques, leads to cognitive decline. Despite extensive research, its complex etiology remains elusive, necessitating new treatments. This study utilized machine learning (ML) to analyze...
Medical forecasting.
Science (New York, N.Y.)
May 23, 2024
"AI-Powered Forecasting" was recently on the cover of , highlighting a new deep learning model for much faster and more accurate weather forecasting. Known as GraphCast, it outperformed the gold-standard system and had an accuracy of 99.7% for tropos...
DMA-HPCNet: Dual Multi-Level Attention Hybrid Pyramid Convolution Neural Network for Alzheimer's Disease Classification.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 22, 2024
Computer-aided diagnosis (CAD) plays a crucial role in the clinical application of Alzheimer's disease (AD). In particular, convolutional neural network (CNN)-based methods are highly sensitive to subtle changes caused by brain atrophy in medical ima...
Lipoproteins and metabolites in diagnosing and predicting Alzheimer's disease using machine learning.
Lipids in health and disease
May 21, 2024
BACKGROUND: Alzheimer's disease (AD) is a chronic neurodegenerative disorder that poses a substantial economic burden. The Random forest algorithm is effective in predicting AD; however, the key factors influencing AD onset remain unclear. This study...
Explainability of three-dimensional convolutional neural networks for functional magnetic resonance imaging of Alzheimer's disease classification based on gradient-weighted class activation mapping.
PloS one
May 21, 2024
Currently, numerous studies focus on employing fMRI-based deep neural networks to diagnose neurological disorders such as Alzheimer's Disease (AD), yet only a handful have provided results regarding explainability. We address this gap by applying sev...
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