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...
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),...
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...
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...
"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...
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...
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...
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...
Resting-state functional magnetic resonance imaging (rs-fMRI) has increasingly been used to study both Alzheimer's disease (AD) and schizophrenia (SZ). While most rs-fMRI studies being conducted in AD and SZ compare patients to healthy controls, it i...
Alzheimer's disease (AD) stands as the prevalent progressive neurodegenerative disease, precipitating cognitive impairment and even memory loss. Amyloid biomarkers have been extensively used in the diagnosis of AD. However, amyloid proteins offer lim...