Latest AI and machine learning research in alzheimer's disease for healthcare professionals.
Both genotype and imaging data carry entangled information about the phenotypes. Associations between genotypes and phenotypes can be manifested or absent on biomedical images. While there are abundant multimodal association studies integrating genotype and imaging data, few of them disentangle their direct and indirect effects in associations. We propose GIF (Genotype-Image-Phenotype), a novel mo...
Plasma proteomics captures a functional snapshot of human physiology; yet, most machine learning models treat protein abundances as independent variables, ignoring the fact that biological systems and proteomic measurements are inherently compositional. Many molecular processes depend not on absolute concentrations but on relative balances: receptor–ligand stoichiometry, enzyme–substrate ratios, a...
Single-cell RNA sequencing (scRNA-seq) has significantly advanced our understanding of Alzheimer’s disease and aging by revealing cellular heterogenei...
Recent advances in high-throughput single-cell technologies have enabled characterization of cellular states across distinct omics layers, yielding co...
Proteostasis dysfunction is a hallmark of frontotemporal dementia (FTD) and Alzheimer’s disease (AD), yet the genetic and molecular pathways that disr...
Alzheimer’s disease (AD) is characterized by progressive cognitive decline and increased seizure susceptibility; yet both the mechanistic and temporal...
Structural dynamics play critical roles for the biological activity of protein molecules. Characterising the inherent conformational landscapes of the...
Leveraging machine learning on electronic health records offers a promising method for early identification of individuals at risk for dementia and ne...
Physical activity is essential for preventing cognitive decline, stroke and dementia in older adults. A new cardiovascular diagnosis offers a critical...
What is the current evidence base for the association between digital biomarkers from wrist-worn wearables, loneliness and social isolation in adults?...
Rapid developments are occurring in artificial intelligence (AI) and machine learning (ML) applied to neuroimaging. To date, advances in this space ha...
Parkinson’s disease (PD) exhibits a variety of symptoms, with approximately 25% of patients experiencing mild cognitive impairment and 45% developing ...
Alzheimer’s disease (AD) presents a critical global health challenge, with current therapies offering limited efficacy and safety in halting disease p...
Sleep is a fundamental biological process with profound implications for physical and mental health, yet our understanding of its complex patterns and...
Characterizing the cardinal neuropathologies in Alzheimer disease (AD) can be laborious, time consuming, and susceptible to intra- and inter-observer ...
INTRODUCTION: Artificial intelligence and neuroimaging enable accurate dementia prediction, but ‘black box’ models can be difficult to trust. Explaina...
Despite the ongoing opioid epidemic, the mortality risk of opioid initiation in patients with dementia or mild cognitive impairment (MCI) remains unde...
The diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM) requires advanced imaging, precluding large-scale pre-clinical testing. Artificial int...
Alzheimer’s Disease (AD) patients at multiple stages of disease progression have a high prevalence of seizures. However, whether AD and epilepsy share...
Artificial intelligence (AI) has been increasingly integrated into imaging genetics to provide intermediate phenotypes (i.e., endophenotypes) that bri...