Latest AI and machine learning research in dementia for healthcare professionals.
INTRODUCTION: Treatment response in Alzheimer's disease (AD) varies substantially across patients, yet no validated frameworks exist to estimate heterogeneous treatment effects (HTE) from observational data while controlling for confounding bias. METHODS: We developed a causal machine learning framework integrating expert-guided causal graphs, complementary HTE estimators, sensitivity analyses, an...
Body mass index (BMI), type 2 diabetes (T2D) and associated cardiometabolic features modify Alzheimer's disease (AD) risk, yet shared mechanisms remain poorly understood. Using sex- and age-stratified genotyping data for BMI and T2D, we investigate how these traits converge on shared genetic pathways to AD risk. Employing multi-trait, machine learning and single-cell transcriptomics, we identify s...
INTRODUCTION: Cognitively unimpaired (CU) adults show substantial variation in their risk of developing mild cognitive impairment (MCI), yet most subt...
Background: Natural language processing (NLP) systems integrated into clinical workflows show promise for detecting early cognitive impairment, yet ca...
Background: Subtle changes in spontaneous language production are among the earliest indicators of cognitive decline. Identifying linguistically inter...
Early Alzheimer's disease often evades timely detection because typical diagnostics are based on symptomatic thinking rather than intrinsic neurodegen...
Introduction: Plasma phosphorylated tau-217 is widely used as a plasma-based biomarker for Alzheimer's Disease detection, demonstrating superior accur...
-Synuclein (-syn) strains can serve as discriminators between Parkinson's disease (PD) and related -synucleinopathies. The relationship between -syn s...
Normative modeling learns a healthy reference distribution and quantifies subject-specific deviations to capture heterogeneous disease effects. In Alz...
Background: Identifying early brain-based markers of cognitive decline is critical for preventive strategies in Alzheimer's disease. Individuals with ...
Aging and genetic risk shape the molecular programs that confer cellular vulnerability in Alzheimer's disease (AD), but whether these programs differ ...
A limitation of social contingency research with infants is that scientists can only instruct the caregivers to modulate their interactive behaviour w...
BACKGROUND AND OBJECTIVES: Effective education on Alzheimer's disease (AD) requires methods fostering empathy, confidence, and knowledge. Artificial i...
Timely and accurate diagnosis of dementia remains a critical yet challenging task. Although machine learning (ML) techniques have shown considerable p...
Background: Amyotrophic lateral sclerosis (ALS) is clinically heterogeneous, and genetic modifiers may drive molecular endophenotypes without obvious ...
Early detection of dementia enables timely intervention and better care planning. Electroencephalography, being accessible and noninvasive, offers a p...
Genetic-based risk prediction is becoming increasingly available for a wide range of common diseases thanks to the growth of large-scale biobanks and ...
Early and accurate diagnosis of Alzheimer's disease (AD) remains a critical challenge in neuroimaging-based clinical decision support systems. In this...
Online cancer peer-support communities generate large volumes of patient-authored and caregiver-authored text that may reflect distress, coping, and i...
Background: Digital health technologies, including artificial intelligence (AI)-powered tools and virtual reality (VR) interventions, are increasingly...