Alzheimer's Disease

Improving transthyretin cardiac amyloidosis detection from electrocardiograms through the Willem artificial intelligence platform.

BACKGROUND: Transthyretin amyloid cardiomyopathy (ATTR-CM) is a frequently underdiagnosed disease in which delay in diagnosis limits the efficacy of treatments. Artificial intelligence (AI) applied to standard 12-lead electrocardiograms (ECGs) is promising and may help improve ATTR-CM early detection. OBJECTIVES: The goals of this study were to extend the Willem AI cloud platform for ATTR-CM detec...

AI-Driven Multimodal Analysis of Neuroimaging and Speech Data for Diagnosis of Alzheimer's, Parkinson's, and Epilepsy.

This study investigates the application of machine learning (ML) techniques combined with neuroimaging and speech signal processing for the early detection of neurological disorders, including Alzheimer's disease, Parkinson's disease, and epilepsy. A multisource analysis dataset consisting of neuro-images and speech features was utilized to train and evaluate various ML classifiers, such as K-near...

Eye movement features are associated with cognitive and mobility decline.

BackgroundEye movement is a vital indicator of neurodegenerative diseases, brain health, and behavior. However, existing knowledge is limited to patie...

Focal-DenseNet: A Risk Assessment Framework for Alzheimer's Disease in Heterogeneous MRI Data.

Alzheimer's disease (AD) is a progressive neurodegenerative disease of the nervous system, which has become an important public health issue attractin...

Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor-Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study.

BACKGROUND: As the global population continues to age, the prevalence of geriatric conditions, including dementia and frailty, is also increasing. Ear...

Artificial intelligence for educating family caregivers of people with dementia: A mixed methods systematic review.

Dementia is a growing global health challenge, with the majority of individuals living with dementia receiving care from family members. However, many...

OPTIMUS: Predicting Multivariate Outcomes in Alzheimer's Disease Using Multi-Modal Data Amidst Missing Values.

OBJECTIVE: Alzheimer's disease, a progressive neurodegenerative disorder, involves neural, genetic, and proteomic factors and impacts multiple cogniti...

From Cultural Context to System Resilience: Advancing Global Mental Health for Older Adults.

Population aging worldwide has intensified the need to understand how mental health in later life is shaped by both cultural norms and structural syst...

Design and Development of an Automated Electroencephalogram Signal Processing and Diagnostic Support Architecture Employing Advanced Deep Learning Models for Early Accurate and Scalable Detection of Neurological Diseases.

BACKGROUND AND PURPOSE: Electroencephalogram (EEG) signals play a vital role in analyzing neurological activity and diagnosing various neurological di...

Multimodal radiomics of precisely segmented hippocampal subfields: Iron deposition and structural biomarkers for early diagnosis of Alzheimer's disease.

Profiling imaging biomarkers of prodromal Alzheimer's disease (AD) against AD dementia may aid earlier diagnosis, yet approaches jointly capturing iro...

Towards Cognitive Impairment Screening in Elderly Communities with Audio-Visual Modal Disentangled Representation Learning.

Alzheimer's disease (AD) is pressing global health concerns, for which early diagnosis is critical to effective intervention. However, conventional ap...

A deep joint-learning proteomics model for diagnosis of six conditions associated with dementia.

Co-pathology is a common feature of neurodegenerative diseases that complicates diagnosis, treatment and clinical management. However, sensitive, spec...

Multi-Omics Analysis for Identifying Glial Dysfunction-Associated Genes as Therapeutic Targets and Drug Candidates in Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, amyloid-β plaques, and neurofibrillary tangle...

AI-enabled digital phenotyping for Alzheimer's disease: a review of multimodal sensor integration and symptom trajectories.

Cross entropy gradient analysis for Alzheimer's disease assessment.

BackgroundAlzheimer's disease (AD) involves progressive cognitive decline associated with disrupted coordination and information exchange across brain...

Polygenic and polysocial risk scores in post-type 2 diabetes dementia: Risk stratification and predictive modeling in the UK Biobank cohort.

BackgroundDementia is a common complication of type 2 diabetes mellitus (T2DM), influenced by both genetic susceptibility and social disadvantages. Wh...

Enhancing Parkinson's Disease Staging: An Integrative Deep Learning Framework for Multimodal Feature Selection.

Parkinson's disease (PD) affects 10 million globally, with accurate staging essential for personalized treatment planning. Current UPDRS assessments a...

A unified deep learning framework for cross-platform harmonization of multi-tracer PET quantification in neurodegenerative disease.

Quantitative PET underpins diagnosis and treatment monitoring in neurodegenerative disease, yet systematic biases between PET-MRI and PET-CT preclude ...

Artificial intelligence for battlefield triage in large-scale combat operations: Opportunities, limits, and ethical considerations.

Large-scale combat operations (LSCOs) impose major constraints on battlefield medical systems, combining sustained casualty inflow, degraded communica...

Comparative Mortality Risk of Aripiprazole, Olanzapine, Quetiapine, and Risperidone in Alzheimer's Disease: A Real‑World Retrospective Cohort Study with Treatment Effect Heterogeneity Analysis.

BACKGROUND: Second-generation antipsychotics (SGAs) are frequently used off-label to manage behavioral symptoms in Alzheimer's disease (AD), despite o...