Latest AI and machine learning research in dementia for healthcare professionals.
The lack of validated stage-specific biomarkers hampers the understanding of Alzheimer's disease (AD) progression and clinical translation. Current transcriptomic methods often produce unstable results with limited stage discrimination. We aimed to develop an explainable machine learning pipeline to identify robust, interpretable gene signatures linked to distinct AD neuropathological stages. We a...
Iron (Fe) and copper (Cu) are vital micronutrients that regulate many critical physiological processes in the human body, with their homeostasis in the central nervous system (CNS) being essential for proper neuronal function. Disruptions in their metabolism and regulatory pathways have been associated with the pathogenesis of various forms of neurodegenerative diseases (NDDs) such as Alzheimer's ...
IMPORTANCE: Disruptions in the sleep-wake cycle have been reported in the preclinical period of dementia; whether they contribute to dementia predicti...
Deep learning (DL) has shown success in predicting Alzheimer's disease (AD) diagnosis, yet continuous measures such as cognitive assessment remain cri...
Dementia research often suffers from methodological pitfalls such as label-information and subject-information leakages. Leveraging the longitudinal O...
INTRODUCTION: Spontaneous speech is commonly disrupted in persons with Alzheimer's disease (AD) and/or Alzheimer's clinical syndrome (ACS). Importantl...
INTRODUCTION: Medical tourism (MT) caregiving companions are often expected to navigate unfamiliar healthcare systems, manage high-stress situations, ...
OBJECTIVE: Family caregivers of persons with dementia experience grief as the care recipients' dementia advances. Here, we explore how various interpe...
The brain age gap (BAG), the difference between magnetic resonance imaging-predicted brain age and chronological age, is a proposed marker of neurobio...
BACKGROUND: Cerebral small vessel disease (CSVD) is a major contributor to vascular dementia. Given the absence of effective treatments, the developme...
Early hospital readmission in multimorbid patients remains a major clinical challenge. Although risk stratification tools are widely used, predictive ...
Dynamic effective connectivity (dEC) analysis provides an approach for revealing the causal mechanism of information transmission in human brain. Howe...
This research letter reports the development and preliminary user testing of MusicAlzheimer, an artificial intelligence-driven digital music therapy p...
Alzheimer's disease (AD) presents considerable heterogeneity in disease risk and outcomes, posing a major challenge for effective therapeutic developm...
Alzheimer's Disease (AD) has traditionally been approached through a biomedical lens, focusing on neurodegenerative markers such as amyloid-β plaques ...
Clinical implementation of neurofilament light chain (NfL), a biomarker of neurodegeneration, remains challenging due to absence of reliable cutoffs a...
Dementia, particularly Alzheimer's disease (AD), is a growing concern in aging populations, with mild cognitive impairment (MCI) frequently progressin...
Accurate detection of Mild Cognitive Impairment (MCI) is critical for timely intervention and for slowing progression to Alzheimer's disease. Electroe...
Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders worldwide, requiring early identification for timely intervention an...
Label-free surface-enhanced Raman spectroscopy (SERS) offers a promising avenue for rapid metabolic phenotyping in complex biofluids, yet its translat...