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Trustworthy artificial intelligence in Alzheimer's disease: state of the art, opportunities, and challenges
Abstract Medical applications of Artificial Intelligence (AI) have consistently shown
remarkable performance in providing medical professionals and patients with support for …
remarkable performance in providing medical professionals and patients with support for …
Multimodal attention-based deep learning for Alzheimer's disease diagnosis
Objective Alzheimer's disease (AD) is the most common neurodegenerative disorder with
one of the most complex pathogeneses, making effective and clinically actionable decision …
one of the most complex pathogeneses, making effective and clinically actionable decision …
Two-stage deep learning model for Alzheimer's disease detection and prediction of the mild cognitive impairment time
Alzheimer's disease (AD) is an irreversible neurodegenerative disease characterized by
thinking, behavioral and memory impairments. Early prediction of conversion from mild …
thinking, behavioral and memory impairments. Early prediction of conversion from mild …
Pixel-level fusion approach with vision transformer for early detection of Alzheimer's disease
Alzheimer's disease (AD) has become a serious hazard to human health in recent years,
and proper screening and diagnosis of AD remain a challenge. Multimodal neuroimaging …
and proper screening and diagnosis of AD remain a challenge. Multimodal neuroimaging …
Robust hybrid deep learning models for Alzheimer's progression detection
The prevalence of Alzheimer's disease (AD) in the growing elderly population makes
accurately predicting AD progression crucial. Due to AD's complex etiology and …
accurately predicting AD progression crucial. Due to AD's complex etiology and …
[HTML][HTML] A hierarchical attention-based multimodal fusion framework for predicting the progression of Alzheimer's disease
Early detection and treatment can slow the progression of Alzheimer's Disease (AD), one of
the most common neurodegenerative diseases. Recent studies have demonstrated the …
the most common neurodegenerative diseases. Recent studies have demonstrated the …
Spatiotemporal feature extraction and classification of Alzheimer's disease using deep learning 3D-CNN for fMRI data
Purpose: Through the last three decades, functional magnetic resonance imaging (fMRI) has
provided immense quantities of information about the dynamics of the brain, functional brain …
provided immense quantities of information about the dynamics of the brain, functional brain …
Identifying early mild cognitive impairment by multi-modality MRI-based deep learning
L Kang, J Jiang, J Huang, T Zhang - Frontiers in aging neuroscience, 2020 - frontiersin.org
Mild cognitive impairment (MCI) is a clinical state with a high risk of conversion to
Alzheimer's Disease (AD). Since there is no effective treatment for AD, it is extremely …
Alzheimer's Disease (AD). Since there is no effective treatment for AD, it is extremely …
Broad learning for early diagnosis of Alzheimer's disease using FDG-PET of the brain
Alzheimer's disease (AD) is a progressive neurodegenerative disease, and the development
of AD is irreversible. However, preventive measures in the presymptomatic stage of AD can …
of AD is irreversible. However, preventive measures in the presymptomatic stage of AD can …
Deep learning based mild cognitive impairment diagnosis using structure MR images
J Jiang, L Kang, J Huang, T Zhang - Neuroscience letters, 2020 - Elsevier
Mild cognitive impairment (MCI) is an early sign of Alzheimer's disease (AD) which is the
fourth leading disease mostly found in the aged population. Early intervention of MCI will …
fourth leading disease mostly found in the aged population. Early intervention of MCI will …