Geriatrics

Prediction of Clinically Significant Depressive Symptoms at 2-Year Follow-Up in Older Adults: Machine Learning Study Using the English Longitudinal Study of Ageing.

BACKGROUND: Depression in older adults is often underdiagnosed due to atypical symptom presentation and generational stigma, leading to delayed intervention. Early identification of individuals at risk of developing elevated depressive symptoms is therefore critical, but traditional approaches show limited predictive accuracy. To date, no study has applied machine learning (ML) models to predict c...

CausalTCC: causal temporal contrastive learning for automated Alzheimer's disease biomarker discovery with bio-electrical signals.

OBJECTIVE: Learning robust representations from scarce labeled bio-electrical time-series data remains a critical challenge in clinical diagnosis. While contrastive learning has shown promise, existing approaches often overlook the intrinsic causal dynamics inherent in physiological signals, leading to over-smoothed representations. This study presents CausalTCC, an end-to-end framework for causal...

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

Intravoxel incoherent motion (IVIM) is a diffusion-weighted magnetic resonance imaging (MRI) method that models slow (D, tissue diffusivity) and fast ...

Innovation districts and transformative workspaces: A scoping review of AI-pet robots companionship for aging employees balancing productivity and wellbeing.

BACKGROUND: Considering the future of work and an aging workforce, emerging technologies such as artificial intelligence (AI) and robots are promising...

Reframing Cardiometabolic Risk and Frailty Through Sarcopenic Obesity.

Variations of global brain asymmetry are associated with aging and related diseases.

Lateralization is a hallmark of brain organization, yet the structural basis underlying this phenomenon remains a critical, unresolved question in cog...

Construction and evaluation of a CatBoost-based machine learning model for sarcopenia prediction using bioelectrical impedance analysis (BIA) and handgrip strength (HGS) from a nationally representative dataset of Korean older adults.

This study aimed to develop and validate an explainable machine learning model for predicting sarcopenia in older adults using nationally representati...

An explainable machine learning model for predicting one-year osteoporosis risk: development and validation in a prospective cohort.

OBJECTIVE: This study aimed to develop and validate an interpretable machine learning model using readily available biochemical indicators to predict ...

Readability and Linguistic Characteristics of Alzheimer's Disease and Related Dementias Prevention, Symptom, and Treatment Information from Generative Artificial Intelligence Chatbots.

PURPOSE: To examine the readability and linguistic characteristics of Alzheimer's disease and related dementias (ADRD) prevention, symptom, and treatm...

NeuroHarm‑Kit: An Open‑Source Toolbox for Benchmarking Deep‑Learning Harmonization of Multi‑Site T1‑Weighted MRI.

Multi‑site magnetic resonance imaging (MRI) studies enable studying brain structure across diverse populations, but scanner‑related variability remain...

BHOD: a bone health optimized dietary pattern designed for osteoporosis prevention.

Existing dietary patterns were not specifically designed to target osteoporosis and lack the precision required for effective prevention. We aimed to ...

Impact of artificial intelligence intervention on awareness of anatomical landmarks in laparoscopic gastrectomy: An exploratory study.

BACKGROUND: We have developed gastric cancer/gastrectomy artificial intelligence (gAI) that leverages the tacit knowledge of experienced surgeons to v...

Leveraging Swin Transformer for Enhanced Diagnosis of Alzheimer's Disease Using Multi-Shell Diffusion MRI.

OBJECTIVE: This study aims to support early diagnosis of Alzheimer's disease and detection of amyloid accumulation by leveraging the microstructural i...

SELongVLM: Empowering Long Video Language Models With Self-Corrective Clip Selection.

Recent advances in multimodal large language models (MLLMs) have enabled impressive progress in visual-language reasoning, yet long-video understandin...

End-to-End Autonomous Quantification of Brain Aneurysm and Parent Artery Morphology at CT Angiography.

Purpose To develop and validate an end-to-end autonomous platform for the quantification and visualization of brain aneurysm and parent artery morphol...

An Aging Clock Based on Immune Repertoire Features: COVID-19 Accelerates Aging.

Aging induces immunosenescence, a progressive decline in immune function underpinning age-related pathogen vulnerability, yet T/B cell receptor (TCR/B...

Beyond Discrimination: Incomplete Model Evaluation in Machine Learning for Frailty Transitions.

Detecting Adverse Events Following Acute Care Encounters among Veterans Prescribed Non-Steroidal Anti-Inflammatory Drugs.

OBJECTIVES: To model the occurrence of adverse events (AEs) among Veterans prescribed nonsteroidal anti-inflammatory drugs (NSAIDs) during emergency d...

Prognostic significance of the endothelial activation and stress index in acute respiratory distress syndrome: a retrospective cohort study.

Endothelial cells play a crucial role in the pathogenesis of acute respiratory distress syndrome (ARDS). The Endothelial Activation and Stress Index (...

The creation and verification of a detection model for mild cognitive impairment by employing eye-tracking and gait metrics.

BackgroundMild cognitive impairment is a prodromal stage of dementia, and early identification is crucial for prognosis.ObjectiveThis study aims to cr...