Latest AI and machine learning research in alzheimer's disease for healthcare professionals.
Quantitative Structure-Activity Relationship ($\mathtt{QSAR}$) modeling is a foundational computational methodology in early-stage drug discovery, heavily relied upon for predicting compound toxicity, bioavailability, and therapeutic potential. However, classical methods often struggle to effectively map the highly complex, non-linear, and high-dimensional interactions inherent in molecular data, ...
Parkinson disease (PD) progression is highly heterogeneous. Deeply phenotyped longitudinal research cohorts have enabled characterization of PD progression trajectories. Early prediction of these progression patterns can help us better understand patient disease conditions and manage appropriately. However, the sample sizes of these cohorts are typically too small to build robust early predictors,...
Background. Clostridioides difficile infection (CDI) imposes a burden that extends well beyond the gastrointestinal tract, yet existing outcome measur...
Background: Hypertension is a modifiable risk factor for dementia, yet the comparative effectiveness of angiotensin receptor blockers (ARBs) versus an...
Accurate and scalable assessment of quantitative neuroimaging biomarkers, such as white matter hyperintensities (WMH) and hippocampal (HIP) volumes, i...
Background: Lewy body diseases (LBD) collectively share alpha-synuclein Lewy pathology, yet present wide clinical heterogeneity, with overlapping moto...
Medical imaging pipelines routinely copy single-channel grayscale data into three identical RGB channels before classification, usually without justif...
In longitudinal Alzheimer's disease (AD) diagnosis support, clinical and imaging information is often collected at irregular visits. Integrating these...
Human brain magnetic resonance imaging (MRI) revolutionized our ability to non-invasively probe individual differences in neuroanatomy. These anatomic...
Longitudinal dementia progression prediction is essential for clinical decision-making. However, models often degrade on external cohorts due to syste...
Cerebral microbleeds (CMBs) are MRI markers of small vessel disease and the microbleed component of amyloid related imaging abnormalities (ARIA-H), bu...
Alzheimers disease (AD) is a brain disorder that develops slowly and mainly affects memory, thinking, language, and daily activities. It is one of the...
INTRODUCTION: Accurate MRI-based identification of Alzheimer's disease (AD), mild cognitive impairment (MCI), and related dementias remains challengin...
Background: Polypharmacy is common in people living with dementia (PLwD) and associated with adverse outcomes. Although Structured Medication Reviews ...
Aging is caused, fully in large part, by the progressive accumulation of damage, yet quantifying age-related damage across tissues and conditions rema...
Accurately predicting the temporal evolution of clinical biomarkers is crucial for the early diagnosis and management of neurodegenerative diseases su...
The accessible chemical space dwarfs any tractable screening budget, and most artificial intelligence drug discovery pipelines respond by docking and ...
Background: Large language model (LLM) agents increasingly automate bioinformatics analyses, but most existing bioinformatics tools were built for sta...
ABSTRACT Dementia classification in heterogeneous populations is complicated by the influence of education, language, socioeconomic position and healt...
Intrinsically disordered proteins and regions (IDPs) are ubiquitous cellular regulators. Uncovering how their transient, multivalent interactions orga...