Alzheimer's Disease

From voxel discovery to regional interaction: A multi-level interpretable framework for Alzheimer's disease diagnosis.

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder where early diagnosis serves as the only viable window for effective intervention. While Structural MRI (sMRI) is a primary clinical tool for this purpose, manual diagnosis is heavily constrained by clinician expertise and the difficulty of visually quantifying subtle, diffuse anatomical changes. Deep learning has emerged as a ...

A preliminary analysis of relationship between dual-task gait and age-related cognitive ability using neural network models.

BackgroundAging causes declines in cognitive and motor functions, often manifested in altered gait. Dual-task gait is a sensitive marker for early functional changes. This study investigated whether neural network (NN) models can differentiate gait patterns across age groups under dual-task conditions, and which joint features contribute most to age classification.MethodsThirty-six healthy were re...

Rational drug design for Alzheimer's disease: from approved therapies to next-generation clinical candidates and AI-guided innovation.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by multifactorial pathology, including amyloid-β (Aβ) aggregation, ...

Integrative machine learning reveals telomere-associated gene modules reflecting neuronal dysregulation in Alzheimer's disease.

BackgroundTelomere dysfunction contributes to cellular aging and genome instability, but telomere-associated transcription in Alzheimer's disease (AD)...

Quantifying generalization error in machine learning prediction of cognitive decline.

BACKGROUND: Predicting cognitive decline as a continuum, from healthy age-related decline to mild cognitive impairment and dementia, enables more prec...

Artificial intelligence-based 3D segmentation of tangle-associated TDP-43 in neurodegeneration.

TAR DNA-binding protein 43 (TDP-43) inclusions are often associated with hyperphosphorylated tau, thus neurofibrillary tangles as the hallmark of Alzh...

Effect of genetic factors on [18F]FDG PET metabolic phenotypes in dementia with Lewy bodies.

BACKGROUND: Neuroimaging with [18F]FDG PET can support the diagnosis of Dementia with Lewy Bodies (DLB), but it remains unclear how genetic factors in...

Data-driven trajectories of atrophy explain clinical heterogeneity across Lewy body diseases.

BACKGROUND: Lewy body diseases (LBD) collectively share α-synuclein Lewy pathology, yet present wide clinical heterogeneity, with overlapping motor an...

miRNA-mRNA Interaction Network Analysis in Alzheimer's Disease for Biomarker Discovery.

Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by widespread dysregulation of gene expression and regulatory pathways....

Two-stage deep learning framework for the restoration of incomplete-ring PET images.

Positron Emission Tomography (PET) is an important molecular imaging tool widely used in medicine. Traditional PET systems rely on complete detector r...

A Generalized Approach to Solving Deep Learning-Based Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level Dependent Magnitude (QSM + qBOLD or QQ) for Oxygen Extraction Fraction (OEF) Mapping Across Diverse Acquisition Schemes.

PURPOSE: QQ, a recently proposed oxygen extraction fraction (OEF) mapping technique combining quantitative susceptibility mapping (QSM) and quantitati...

Developing an integrated algorithm to support autism diagnostic decisions: Model performance and statistical fairness.

BACKGROUND: Best practices in diagnosing autism spectrum disorder require an expert diagnostician to integrate multiple sources of information, includ...

Nurse-Led Large Language Model Chatbot for Predicting and Preventing Complications After Coronary Artery Bypass Grafting: Protocol for a Randomized Controlled Trial.

BACKGROUND: Thirty-day unplanned readmission following coronary artery bypass grafting (CABG) affects 10%-20% of patients and is a key quality indicat...

Super-Resolution of Through-Plane Undersampled MRIs in Alzheimer's Disease Diagnosis.

Alzheimer's disease is a complex neurodegenerative disorder and the leading cause of dementia worldwide. Learning-based techniques applied to magnetic...

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 measur...

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 c...

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...

Engineering selective amyloid precursor protein inhibitors by machine learning and deep mutational scanning.

Deep mutational scanning (DMS) has proven effective for mapping protein-protein interactions (PPIs), but it cannot provide complete coverage of the mu...