AIMC Topic: Alzheimer Disease
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3D Multi-feature fusion convolutional network for Alzheimer's disease diagnosis.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
The cognitive decline caused by Alzheimer's disease (AD) is closely related to the structural changes in the hippocampus captured by structural magnetic resonance imaging (sMRI). However, current deep model research on the morphological analysis of h...
Novel Alzheimer's Disease Stating Based on Comorbidities-Informed Graph Neural Networks.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Alzheimer's Disease (AD), the most prevalent form of dementia, requires early prediction for timely intervention. Leveraging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), our study employs Graph Neural Networks (GNNs) for multi-cl...
Dual Attention Graph Convolutional Network Fusing Imaging and Genetic Data for Early Alzheimer's Disease Diagnosis.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Alzheimer's Disease (AD) poses a significant global neurodegenerative challenge, underscoring the urgency of early clinical intervention. Our paper presents a novel approach for early AD diagnosis, focusing on a dual attention graph convolutional net...
Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer's disease or infer dementia severity from T1-weighted brain MRI scans. Here, we examine the value of adding diffusion-weighted MRI (dMRI) as an in...
Graph-based deep learning models in the prediction of early-stage Alzheimers.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Alzheimer's disease is the most common age-related problem and progresses in different stages, from cognitively normal to early mild cognitive impairment, and severe dementia. This study investigates the predictive potential of resting-state function...
TA-RNN: an attention-based time-aware recurrent neural network architecture for electronic health records.
Bioinformatics (Oxford, England)
Jun 28, 2024
MOTIVATION: Electronic health records (EHRs) represent a comprehensive resource of a patient's medical history. EHRs are essential for utilizing advanced technologies such as deep learning (DL), enabling healthcare providers to analyze extensive data...
[An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion].
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Jun 25, 2024
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is challenging, leading to a high rate of misdiagnosis. Current research on early d...
AmyloidPETNet: Classification of Amyloid Positivity in Brain PET Imaging Using End-to-End Deep Learning.
Radiology
Jun 1, 2024
Background Visual assessment of amyloid PET scans relies on the availability of radiologist expertise, whereas quantification of amyloid burden typically involves MRI for processing and analysis, which can be computationally expensive. Purpose To dev...
Disease-driven domain generalization for neuroimaging-based assessment of Alzheimer's disease.
Human brain mapping
Jun 1, 2024
Development of deep learning models to evaluate structural brain changes caused by cognitive impairment in MRI scans holds significant translational value. The efficacy of these models often encounters challenges due to variabilities arising from dif...
AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature.
Briefings in bioinformatics
May 23, 2024
Neurodegenerative diseases, such as Alzheimer's disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimer's Identification Tool (AITeQ) using ribonucleic acid-seq...
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