Dementia

Deep learning maps local brain aging in relation to cognition across human adulthood.

Brain aging, the strongest risk factor for Alzheimer's disease (AD), varies across cortical regions. Global brain age (GBA), an imaging-derived measure of neuroanatomic decline, reduces structural aging to a single summary value. This can potentially obscure regional patterns of cognitive vulnerability preceding AD. This study introduces a deep-learning architecture trained on the [Formula: see te...

Retinal and Choroidal Vascular Metrics Predict Cerebral Atrophy and Cognitive Impairment in Noninfarcted Large Artery Stenosis.

PURPOSE: Large artery stenosis (LAS) can drive cognitive impairment and neurodegeneration even without overt infarction, yet scalable biomarkers for capturing this silent vascular brain injury are limited. We investigated whether retinal and choroidal microvascular and neurostructural measures reflect cerebral atrophy and cognitive impairment in noninfarcted LAS. METHODS: In a multicenter cohort o...

Adaptive cascading artificial intelligence for Alzheimer's disease assessment: a clinically oriented narrative review and implementation framework.

Artificial intelligence (AI) has achieved remarkable success in the diagnosis of Alzheimer's disease (AD) in the literature, where many of the models ...

Conversational Capacity in Triadic Telemedicine: A Conceptual Framework and Evidence Synthesis.

BACKGROUND: Telemedicine is conventionally modeled as a dyadic clinician-patient encounter, yet a third party-caregiver, community health worker, nurs...

Biomarker-Conditioned Vision Transformers with Deformable Biomarker Attention for Alzheimer's Disease Classification.

Alzheimer's disease (AD) classification from structural magnetic resonance imaging (MRI) remains challenging, particularly when distinguishing mild co...

Classification of tau status with machine learning models in amyloid-positive cohorts.

INTRODUCTION: Although tau positron emission tomography (PET) imaging is effective for staging tau pathology, it is limited clinically by cost and ava...

Artificial intelligence in retinal imaging for early Alzheimer's disease detection: A review.

BackgroundAlzheimer's disease (AD) is a progressive neurodegenerative disorder that necessitates early, accessible, and non-invasive diagnostic method...

Artificial intelligence for early diagnosis of mild cognitive impairment: A scoping review comparing diagnostic accuracy with physician performance.

BACKGROUND: Mild cognitive impairment (MCI) represents a clinically critical and heterogeneous syndrome characterized by cognitive decline exceeding n...

Association of PM2.5 and its components with cognitive dysfunction among rural older adults: Epidemiological evidence with hypothesis-generating network toxicology analysis.

BACKGROUND: Although fine particulate matter (PM2.5) has been associated with cognitive dysfunction (CD), the roles of specific PM2.5 components and p...

Agentic AI for scaling diagnosis and care in neurodegenerative disease.

US healthcare systems are struggling to meet the growing demand for neurological care, particularly in Alzheimer's disease and related dementias. Gene...

Performance of 5 Large Language Models in Perioperative Consultation for Pediatric Hypospadias: Cross-Sectional Comparative Study.

BACKGROUND: Hypospadias is a common congenital malformation requiring surgery. Caregivers face substantial perioperative information needs, and large ...

Advancing the FAIRness of Multimodal Imaging Research Through the OMOP MI-CDM Framework: A Case Replication Study in Alzheimer's Disease.

The objective of this study is to demonstrate an end-to-end approach for operationalizing the Findable, Accessible, Interoperable, and Reusable (FAIR)...

Guideline Concordance of Large Language Model Responses to Parent-oriented Guideline Prompts About Pediatric Acute Bacterial Arthritis.

BACKGROUND: Large language models (LLMs) are increasingly used by patients and caregivers to obtain medical information. In pediatric acute bacterial ...

Global trends in Alzheimer's and dementia research (2019-2024): A machine learning-based bibliometric analysis.

BackgroundDementia, including Alzheimer's disease, represents one of the fastest-growing global health challenges. To coordinate research priorities, ...

Heterogeneity in the association between antihypertensive medications and Alzheimer's disease and related dementias risk: A multidimensional machine learning analysis to inform drug repurposing.

BackgroundAlzheimer's disease and related dementias (ADRD) place an immense burden on patients, families, and health systems in the United States. Hyp...

Predicting future brain atrophy based on longitudinal MRI.

BackgroundNeuron loss is a hallmark of neurodegenerative diseases and leads to brain atrophy detectable with magnetic resonance imaging (MRI). Accurat...

Non-transgenic rodent models associated with Alzheimer's disease: applications, evaluation, and perspectives.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder. Familial AD accounts for less than 1% of cases, while sporadic AD (SAD) accounts...

Integration of multi-omics and artificial intelligence for therapeutic insights in Alzheimer's disease: A comprehensive review.

Alzheimer's disease (AD) is a progressively worsening type of brain disorder that damages the nerve cells. It is marked by the buildup of amyloid-β pl...

NeuroOmics-Net: An interpretable multimodal deep learning framework for Alzheimer's disease diagnosis and progression prediction using neuroimaging, EEG, and genomic data.

Accurate diagnosis and progression prediction of Alzheimer's disease (AD) remain challenging due to the heterogeneous nature of the disease, which inv...

Comparative Screening of Alzheimer's Disease, Lewy Body Dementia, and Frontotemporal Dementia Using miRNA and Machine Learning.

Current dementia diagnostic methods can be costly, invasive, or limited in their ability to distinguish between disorders with overlapping clinical sy...