Latest AI and machine learning research in alzheimer's disease for healthcare professionals.
OBJECTIVES: This study aims to develop a deep learning model to assist physicians in accurately classifying negative, equivocal, and positive β-amyloid (Aβ) deposition stages in Alzheimer's disease (AD). MATERIALS AND METHODS: 1327 subjects from two cohorts underwent [¹⁸F]Florbetapir PET and were grouped by Aβ deposition. A cascaded attention-guided vision transformer (CA-ViT) framework was propos...
UNLABELLED: Alzheimer’s disease (AD) is a neurodegenerative disorder that progressively deteriorates a person’s memory, as well as their ability to think and move. It has been reported to be the most common cause of dementia. Alterations in gene expression have been increasingly recognised as key contributors to the onset and progression of AD, driving interest in transcriptomic approaches to bett...
BACKGROUND: In recent years, the incidence of cognitive diseases has also risen with the significant increase in population aging. Among these disease...
BACKGROUND: Dementia and Parkinson's disease (PD) are among the most prevalent neurological disorders globally. Most previous research has focused on ...
BACKGROUND: Alzheimer's disease (AD) is a prevalent neurodegenerative disorder. This study aims to identify biomarkers associated with glutamine metab...
BACKGROUND: Disrupted sleep and circadian rhythms in dementia affect rest-activity patterns and impact quality of life, safety, and caregiver burden. ...
BACKGROUND: Advances in artificial intelligence (AI) have revolutionized digital wellness by providing innovative solutions for health, social connect...
BACKGROUND: The complex brain changes involved in Alzheimer's disease (AD) development constitute a high-dimensional nonlinear feature space where dee...
Vascular dementia (VaD) is the second most common type of dementia, yet its pathogenesis is not fully understood, and effective diagnostic and therape...
Brain imaging genetics aims to uncover the pathological mechanisms and improve the diagnosis of brain diseases, particularly neurodegenerative disorde...
PURPOSE: There has been significant progress in detecting Alzheimer's disease (AD) using retinal imaging. We developed an ensemble learning-based deep...
BACKGROUND: Alzheimer's disease (AD) is increasingly recognized as a multifactorial network disorder in which amyloid and tau pathology interact with ...
Alzheimer's disease (AD) is characterized by progressive brain network disintegration, yet quantifying this process at an individual level remains cha...
OBJECTIVES: Amyloid-β (Aβ) PET is crucial for diagnosing and monitoring Alzheimer's disease (AD), but its high cost and radiation exposure limit its u...
This study presents Latent Diffusion Autoencoder (LDAE), a novel encoder-decoder diffusion-based framework for efficient and meaningful unsupervised l...
OBJECTIVES: Amyloid-lowering immunotherapies can cause amyloid-related imaging abnormalities (ARIA), requiring brain MRI for detection and monitoring....
Alzheimer's disease (AD) -the most common form of dementia- begins with mild memory loss and gradually progresses, eventually resulting in a generaliz...
Deep learning algorithms optimize data by enhancing resolution and suppressing noise associated with biological knowledge. The root issue is that, for...
PURPOSE OF REVIEW: Hypertension remains a leading modifiable risk factor for cardiovascular and renal conditions and dementia. Given its rising global...
Early identification of mild cognitive impairment (MCI) progressing to Alzheimer's disease (AD) is of paramount importance. Despite the notable advanc...