Trustworthy artificial intelligence in Alzheimer's disease: state of the art, opportunities, and challenges

S El-Sappagh, JM Alonso-Moral, T Abuhmed… - Artificial Intelligence …, 2023 - Springer
Abstract Medical applications of Artificial Intelligence (AI) have consistently shown
remarkable performance in providing medical professionals and patients with support for …

Multimodal attention-based deep learning for Alzheimer's disease diagnosis

M Golovanevsky, C Eickhoff… - Journal of the American …, 2022 - academic.oup.com
Objective Alzheimer's disease (AD) is the most common neurodegenerative disorder with
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

S El-Sappagh, H Saleh, F Ali, E Amer… - Neural Computing and …, 2022 - Springer
Alzheimer's disease (AD) is an irreversible neurodegenerative disease characterized by
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

M Odusami, R Maskeliūnas, R Damaševičius - Electronics, 2023 - mdpi.com
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 …

Robust hybrid deep learning models for Alzheimer's progression detection

T Abuhmed, S El-Sappagh, JM Alonso - Knowledge-Based Systems, 2021 - Elsevier
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 …

[HTML][HTML] A hierarchical attention-based multimodal fusion framework for predicting the progression of Alzheimer's disease

P Lu, L Hu, A Mitelpunkt, S Bhatnagar, L Lu… - … Signal Processing and …, 2024 - Elsevier
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 …

Spatiotemporal feature extraction and classification of Alzheimer's disease using deep learning 3D-CNN for fMRI data

H Parmar, B Nutter, R Long, S Antani… - Journal of Medical …, 2020 - spiedigitallibrary.org
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 …

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 …

Broad learning for early diagnosis of Alzheimer's disease using FDG-PET of the brain

J Duan, Y Liu, H Wu, J Wang, L Chen… - Frontiers in …, 2023 - frontiersin.org
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 …

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 …