Alzheimer's Disease

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...

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 decl...

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 ...

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 g...

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 personaliz...

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 syst...

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, combi...

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 behavior...

Circulatory dietary and gut-derived metabolites predict early cognitive decline.

INTRODUCTION: A key component of disease prevention is the identification of at-risk individuals. Mi...

Quantitative pathology and APOE genotype reveal dementia risk and progression in Lewy body disease.

Dementia in Lewy body diseases (LBD) is common and arises through heterogeneous and incompletely und...

The Deep Learning Revolution in Neuroimaging: Insights from a Bibliometric Analysis (2014-2024).

This paper presents a bibliometric analysis of the fast-growing area of deep learning in neuroimagin...

Toward ultimate NMR resolution with deep learning.

Resolution in NMR is defined as the ability to distinguish and accurately determine signal positions...

Closed-loop transcranial ultrasound stimulation based on NREM and REM sleep for bidirectional modulation of sleep neural oscillation and memory.

Sleep plays an important role in memory integration. Closed-loop physical stimulation during rapid e...

Reappraising cardiac function with myocardial contraction fraction: normal values, disease detection, and prognostication.

AIMS: Assessing cardiac function is critical for managing cardiovascular disease, guiding treatment,...

Parvalbumin Neuron-Targeted Loss of Alzheimer's Disease Risk Gene BIN1 Is Insufficient to Drive Cognitive or Network Excitability Changes.

Bridging integrator 1 (BIN1) is one of the strongest genetic risk factors for Alzheimer's disease (A...

A machine learning approach using echocardiographic and hematological parameters to classify cerebral small vessel disease burden.

BACKGROUND: Cerebral small vessel disease (CSVD) is a leading cause of stroke and dementia and is as...

Screening for Alzheimer's disease in the community using an AI-driven screening platform: design of the PREDICTOM study.

BACKGROUND: Recent developments in physiological, imaging and digital biomarkers combined with the a...

Addressing disparities in transthyretin amyloid cardiomyopathy: A systematic review of artificial intelligence in the early identification to improve patient outcomes.

BACKGROUND: Transthyretin amyloid cardiomyopathy (ATTR-CM) remains substantially underdiagnosed amon...

Enriched resting-state EEG prediction of cognitive decline in prodromal Alzheimer's disease: a machine-learning approach.

OBJECTIVE: We evaluated the accuracy of standard machine learning (ML) algorithms in predicting 1-ye...

Machine-learning approach on echocardiography to improve the detection of transthyretin amyloid cardiomyopathy: GRAAL algorithm.

AIMS: Transthyretin amyloid cardiomyopathy (ATTR-CM) is an increasingly recognized cause of heart fa...