Dementia

Artificial intelligence prediction of In-Hospital mortality in patients with dementia: A multi-center study.

BACKGROUND: Prediction of mortality is very important for care planning in hospitalized patients with dementia and artificial intelligence has the potential to serve as a solution; however, this issue remains unclear. Thus, this study was conducted to elucidate this matter.

SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning.

Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for transcriptome-wide association studies (TWAS). To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (S...

Fully Automated Hippocampus Segmentation using T2-informed Deep Convolutional Neural Networks.

Hippocampal atrophy (tissue loss) has become a fundamental outcome parameter in clinical trials on Alzheimer's disease. To accurately estimate hippoca...

A novel graph neural network method for Alzheimer's disease classification.

Alzheimer's disease (AD) is a chronic neurodegenerative disease. Early diagnosis are very important to timely treatment and delay the progression of t...

Understanding machine learning applications in dementia research and clinical practice: a review for biomedical scientists and clinicians.

Several (inter)national longitudinal dementia observational datasets encompassing demographic information, neuroimaging, biomarkers, neuropsychologica...

Evolution of white matter hyperintensity segmentation methods and implementation over the past two decades; an incomplete shift towards deep learning.

This systematic review examines the prevalence, underlying mechanisms, cohort characteristics, evaluation criteria, and cohort types in white matter h...

Predicting the severity of mood and neuropsychiatric symptoms from digital biomarkers using wearable physiological data and deep learning.

Neuropsychiatric symptoms (NPS) and mood disorders are common in individuals with mild cognitive impairment (MCI) and increase the risk of progression...

Elucidating Microglial Heterogeneity and Functions in Alzheimer's Disease Using Single-cell Analysis and Convolutional Neural Network Disease Model Construction.

In this study, we conducted an in-depth exploration of Alzheimer's Disease (AD) by integrating state-of-the-art methodologies, including single-cell R...

Identification of profiles associated with conversions between the Alzheimer's disease stages, using a machine learning approach.

BACKGROUND: The identification of factors involved in the conversion across the different Alzheimer's disease (AD) stages is crucial to prevent or slo...

Exceptional performance with minimal data using a generative adversarial network for alzheimer's disease classification.

The classification of Alzheimer's disease (AD) using deep learning models is hindered by the limited availability of data. Medical image datasets are ...

Enhancing identification performance of cognitive impairment high-risk based on a semi-supervised learning method.

BACKGROUND: Cognitive assessment plays a pivotal role in the early detection of cognitive impairment, particularly in the prevention and management of...

E-BDL: Enhanced Band-Dependent Learning Framework for Augmented Radar Sensing.

Radar sensors, leveraging the Doppler effect, enable the nonintrusive capture of kinetic and physiological motions while preserving privacy. Deep lear...

XAI-Based Assessment of the AMURA Model for Detecting Amyloid-β and Tau Microstructural Signatures in Alzheimer's Disease.

Brain microstructural changes already occur in the earliest phases of Alzheimer's disease (AD) as evidenced in diffusion magnetic resonance imaging (d...

Neuropsychological and electrophysiological measurements for diagnosis and prediction of dementia: a review on Machine Learning approach.

INTRODUCTION: Emerging and advanced technologies in the field of Artificial Intelligence (AI) represent promising methods to predict and diagnose neur...

Disentangling brain atrophy heterogeneity in Alzheimer's disease: A deep self-supervised approach with interpretable latent space.

Alzheimer's disease (AD) is heterogeneous, but existing methods for capturing this heterogeneity through dimensionality reduction and unsupervised clu...

Improving brain atrophy quantification with deep learning from automated labels using tissue similarity priors.

Brain atrophy measurements derived from magnetic resonance imaging (MRI) are a promising marker for the diagnosis and prognosis of neurodegenerative p...

Unbiased analysis of spatial learning strategies in a modified Barnes maze using convolutional neural networks.

Assessment of spatial learning abilities is central to behavioral neuroscience and a useful tool for animal model validation and drug development. How...

A Novel Implementation of a Social Robot for Sustainable Human Engagement in Homecare Services for Ageing Populations.

This research addresses the rapid aging phenomenon prevalent in Asian societies, which has led to a significant increase in elderly individuals reloca...

A model for identifying potentially inappropriate medication used in older people with dementia: a machine learning study.

BACKGROUND: Older adults with dementia often face the risk of potentially inappropriate medication (PIM) use. The quality of PIM evaluation is hindere...

Self-Explainable Graph Neural Network for Alzheimer Disease and Related Dementias Risk Prediction: Algorithm Development and Validation Study.

BACKGROUND: Alzheimer disease and related dementias (ADRD) rank as the sixth leading cause of death in the United States, underlining the importance o...