Latest AI and machine learning research in alzheimer's disease for healthcare professionals.
Electroencephalography (EEG) is a promising tool for automated detection of mild cognitive impairment (MCI) and dementia, but comparisons across studies are limited by inconsistent datasets and evaluation protocols. This study benchmarks ten deep learning models across four resting-state EEG datasets and eight binary classification tasks using a unified preprocessing pipeline and five-fold subject...
The gut microbiome has been increasingly implicated in Alzheimers disease (AD), with studies reporting numerous species- and genus-level differences. These findings established a growing catalog of AD-associated taxa, yet they typically evaluate taxa individually or in small sets rather than across microbial communities. Gut microorganisms act collectively through cross-feeding, competition, and m...
Neurodegenexrative diseases such as Alzheimer's disease and Parkinson's disease are diagnosed most reliably only after substantial, often irreversible...
Background Existing insulin resistance (IR) indices are predominantly developed in diabetic cohorts, limiting their generalizability. We developed a n...
Alzheimer's disease is a leading cause of death with no cure. Therefore, early detection is critical to slow progression and preserve quality of life....
Alzheimer's disease (AD) progression is a longitudinal process with subtle pathological cues in the early stages. Yet, computational constraints have ...
Perioperative risk prediction models are often limited by narrow surgical populations, incomplete intraoperative data, poor calibration, and limited i...
Neuroimaging and genetic testing are two important clinical references for nervous system diseases, offering complementary diagnostic information. How...
Dementia affects more than 55 million people worldwide, and its progressive decline is difficult to track using infrequent in-person assessments, whic...
Alzheimer's disease (AD) causes amyloid formation, neuritic dystrophy, gliosis, synapse loss, behavioral abnormalities, and weight loss. 5xFAD transge...
Access to holistic, multimodal data improves the performance of Artificial Intelligence (AI) in medical classification tasks compared to utilizing sin...
Causal discovery recovers directed structure from observational data and is increasingly used in clinical settings to support mechanism reasoning and ...
Care robots are increasingly being introduced into healthcare settings, raising important questions about their acceptance and ethical implementation....
Identifying reliable Alzheimer's disease (AD) markers typically requires manual, labor-intensive transcription and expert analysis, limiting its scale...
As populations age, cognitive decline from mild cognitive impairment (MCI) to dementia is a defining health challenge of the coming decades, yet routi...
Medical foundation models (FMs) are increasingly used for brain MRI analysis. However, their evaluation remains dominated by high-resource datasets, l...
The promise of multimodal fusion lies in combining complementary sources of evidence, yet more evidence does not always yield a better prediction. Rec...
EEG-based machine learning shows promise for neurodegenerative disease classification, but robustness to sample imbalance, center heterogeneity, and v...
Background: Dementia caregiving carries substantial emotional and psychological consequences, but most evidence comes from structured surveys and inte...
Accurate identification of Alzheimers disease (AD) using resting-state functional magnetic resonance imaging (rs-fMRI) remains challenging due to the ...