AIMC Topic: Aged

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Exploring Age-Related Patterns in Smartphone Keystroke Dynamics Considering Temporal Variability: Cross-Sectional Study With AI-Based Analysis.

JMIR mHealth and uHealth
BACKGROUND: Keystroke dynamics on smartphones have emerged as a promising form of passive digital biomarker. While previous studies have explored their utility in several diseases and disorders, relatively few have examined how these dynamics change ...

Research Status and Trends in Virtual Reality Technology for Older Adults: Bibliometric and Visual Analysis.

JMIR aging
BACKGROUND: Virtual reality (VR) technology is increasingly applied in aging-related research. Although existing bibliometric studies have focused on specific applications, such as older adults' acceptance of VR and its use in cognitive rehabilitatio...

Virtual Wound Care in Australian Nursing Homes: Protocol for a Pilot and Feasibility Study.

JMIR research protocols
BACKGROUND: Chronic wounds, those which have not healed in a timely manner, are a significant health and economic burden. Older people, especially those living in nursing homes, are disproportionately affected by chronic wounds, and effective managem...

Radiomics-based MRI models for predicting breast cancer axillary lymph node involvement in comparison with Node-RADS: a proof-of-concept study.

European radiology experimental
BACKGROUND: Detection of axillary lymph node (LN) involvement is essential for staging breast cancer and optimizing treatment. This proof-of-concept two-center study explored the feasibility of magnetic resonance imaging (MRI) radiomics-based machine...

Deep-learning prediction of breast cancer hormone receptor status from CEM: a preliminary study.

European radiology experimental
BACKGROUND: Hormone receptor (HR) status guides breast cancer therapy. Deep learning (DL) applied to contrast-enhanced mammography (CEM) might offer a noninvasive means for HR status prediction, but class imbalance challenges model development and as...

Novel artificial intelligence model predicts the need for reduction of distal radius fractures.

European journal of orthopaedic surgery & traumatology : orthopedie traumatologie
PURPOSE: Distal radius fractures are among the most common upper extremity injuries. While convolutional neural networks (CNNs) have shown promise in fracture detection, no models have specifically addressed the need for reduction, which is a critica...

Factors influencing immediate post-angiographic occlusion outcomes in intracranial aneurysms treated with the woven endobridge device: a multi-center analysis and predictive model from the WorldWideWEB consortium.

Neurosurgical review
The Woven EndoBridge (WEB) device treats wide-necked bifurcation aneurysms, but occlusion rates vary. This study aims to identify factors associated with immediate WEB device occlusion. Data from patients treated with WEB devices across 36 sites were...

ceRNA regulatory network and immune-neurodegenerative mechanisms of peripheral CD4+ T cells in parkinson's disease.

PloS one
Parkinson's disease (PD) is a neurodegenerative disorder characterized by dopaminergic neuron loss and neuroinflammation, with emerging roles of peripheral immune dysregulation in disease progression. This study aimed to investigate the regulatory ne...

Identification of PIWI-interacting RNAs based models for lung adenocarcinoma early detection: a multicenter cohort study.

Molecular biomedicine
Early detection of lung adenocarcinoma (LUAD) remains a major clinical challenge despite the widespread application of low-dose computed tomography (LDCT). Circulating PIWI-interacting RNAs (piRNAs), characterized by tumor-specific expression and hig...

Development of explainable machine learning models to predict side effects in patients with rheumatoid arthritis taking methotrexate treatment: a nationwide multicentre cohort study.

BMJ open
OBJECTIVES: Methotrexate (MTX) effectively controls rheumatoid arthritis (RA) but often leads to side effects (SE) such as gastrointestinal (GI) issues, liver toxicity and bone marrow suppression. To develop clinically interpretable machine learning ...