Geriatrics

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 resonance imaging (MRI) have recently shown strong potential for automated diagnosis. Accurate classification typically relies on high-resolution (HR) 3D MRI acquired with thin axial slices to reduce partial-volume artefacts, capture fine anatomical...

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...

Explainable Machine Learning for Perioperative Risk Stratification of Radiographic Adjacent Segment Degeneration After Short-Segment Lumbar Fusion.

STUDY DESIGN: Retrospective Cohort Study. OBJECTIVES: To develop and externally validate an explainable machine-learning framework for perioperative r...

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 ...

Evaluation Methods for Inference-Time Retrieval-Augmented and Graph Retrieval-Augmented Large Language Models in Health Care: Scoping Review.

BACKGROUND: Inference-time retrieval augmentation is increasingly used to improve the traceability and verifiability of large language model (LLM) app...

A Multimodal Ultrasound Radiomics Model Integrating Contrast-Enhanced Phases: Retrospective Temporal Test Cohort Evaluation and Comparison with Sonographers for Cervical Lymph Node Characterization.

RATIONALE AND OBJECTIVES: To develop a multimodal ultrasound radiomics model based on conventional ultrasound and three-phase contrast-enhanced ultras...

Eyettention II: A dual-sequence architecture for modeling fixation location, within-word landing position, and fixation duration in reading.

The way our eyes move while reading provides valuable insights into both the reader's cognitive processes and the properties of the text. In particula...

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...

An end-to-end deep learning image compression method for satellite images based on entropy model.

With the extensive applications of satellite image data in environmental monitoring and geographic surveying and mapping, the amount of data has incre...

A machine learning-based framework for individualized physical activity and strength targets related to women's bone health.

While substantial evidence supports the associations between physical activity and bone health, the present study aims to advance the field by applyin...

Predictive accuracy of a general-purpose artificial intelligence model for cut-out following proximal femoral nailing.

INTRODUCTION: Cut-out is the most consequential mechanical complication after proximal femoral nailing and requires prompt recognition. General-purpos...

Genome-wide analysis of subcortical aging identifies a spatially structured pattern of genetic associations.

Local brain age (LBA) is a spatially resolved biomarker of brain aging that captures regional deviations from chronological age, yet its genetic archi...

Development and Validation of an XGBoost-SHAP Model for Predicting Adverse Outcomes in Elderly Cardiovascular Patients With Polypharmacy: A Retrospective Cohort Study.

This study aims to develop and validate an interpretable machine learning model using Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlan...

Estimation of pulmonary function from time-resolved dynamic chest radiography using machine learning in patients with respiratory disease.

BACKGROUND: Pulmonary function tests (PFTs), particularly spirometry, are the reference standard for assessing airflow limitation in respiratory disea...

Harnessing Exhaled Breath for Lung Cancer Early Detection-Results From the ExPeL Study.

Scalable, non-invasive tools are critically needed to improve early lung cancer detection and optimize primary care referral pathways. We evaluated In...

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...

Comment on "Machine Learning Approaches to Identify Physical, Psychological, and Social Predictors of Worsening Frailty Transitions Among Community-Dwelling Older Adults: A Longitudinal Cohort Study".

Meeting the 24-Hour Movement Guidelines and Frailty Risk: Evidence From Two Nationally Representative Population Studies and Machine-Learning Models.

AIM: This study aimed to examine the association between meeting the 24-Hour Movement Guideline components-moderate-to-vigorous physical activity (MVP...

Integrating gut‑brain axis insights into bundled nutrition care for critically ill patients: From mechanisms to implementation (Review).

Critical illness induces a marked disruption of the gut‑brain axis, which is characterized by systemic inflammation and the rapid collapse of intestin...