Latest AI and machine learning research in alzheimer's disease for healthcare professionals.
Abstract Background Neurodegenerative diseases, including Alzheimer's disease (AD), exhibit substantial clinical and molecular heterogeneity, complicating accurate diagnosis and development of effective therapies. Although multi-omics profiling provides unprecedented molecular resolution, systematic integration of high-dimensional, imbalanced data modalities with disease-relevant biological networ...
Alzheimer's disease (AD) and Lewy body dementia (LBD) present overlapping clinical features yet require distinct diagnostic strategies. While neuroimaging-based brain network analysis is promising, atlas-based representations may obscure individualized anatomy. Gyral folding-based networks using three-hinge gyri provide a biologically grounded alternative, but inter-individual variability in corti...
Multimodal fusion frameworks, which integrate diverse medical imaging modalities (e.g., MRI, CT), have shown great potential in applications such as s...
Biomedical Knowledge Graphs (BKGs) offer integrative representations of complex biology, yet their utility is compromised by the limitations of curren...
Background: Australian health practitioners are regulated under the Health Practitioner Regulation National Law, with serious conduct matters referred...
Alzheimer's disease (AD) is a progressive neurodegenerative condition necessitating early and precise diagnosis to provide prompt clinical management....
Calcium dynamics controls learning and memory. Changes in calcium-induced calcium release (CICR), which is caused by opening ryanodine receptors (RyR)...
INTRODUCTION: Treatment response in Alzheimer's disease (AD) varies substantially across patients, yet no validated frameworks exist to estimate heter...
Neurological health score (NHS), indicating the health of brain and nervous system, helps in identifying high risk individuals, and in recommending li...
Body mass index (BMI), type 2 diabetes (T2D) and associated cardiometabolic features modify Alzheimer's disease (AD) risk, yet shared mechanisms remai...
Aging is asynchronous across cells and organs, but whether plasma proteins can capture cell type-specific aging and predict disease and mortality rema...
Portable, ultra-low-field (ULF) magnetic resonance imaging has the potential to expand access to neuroimaging but currently suffers from coarse spatia...
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...
We study same-source multi-view learning and adversarial robustness for next-day direction prediction with financial image representations. On Shangha...
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...