Geriatrics

Characteristics of electroencephalographic changes induced by different hypnotics in elderly patients: a narrative review.

Aging is associated with widespread structural and functional changes in the brain including reduced...

Association between red cell distribution width-albumin ratio and osteoarthritis in middle-aged and older adults: Analysis of NHANES data (1999-2018).

OBJECTIVE: This study aimed to investigate the association between the Red blood cell distribution w...

Fracture risk scores using output from an opportunistic screen of low bone density from conventional X-ray.

Fracture risk is commonly assessed by FRAX, a tool that estimates 10-yr risk for major osteoporotic ...

Spine age derived from DXA vertebral fracture assessment images predicts incident fractures and mortality: the Manitoba Bone Mineral Density Registry.

Biological age may better predict health outcomes than chronological age by capturing individual het...

K-MIMIC: a nationwide Korean multi-institutional Multimodal intensive care dataset.

BACKGROUND: Recent advancements in critical care have highlighted the need for comprehensive, multim...

AI Agent for Delirium Screening among Patients in Oncology and Cardiac Intensive Care Units: A Proof-of-Concept Study.

AIM: To develop and evaluate an autonomous artificial intelligence (AI) agent to support nurse-led d...

Study design for an emulated trial of a 2 arm, parallel, stratified, adaptive, RCT of CABG versus PCI in people requiring myocardial revascularization at high risk (High-Risk REVASC).

AIM: This study aims to use routinely collected health data and trial emulation methodology to infor...

Uncertainty in deep learning for EEG under dataset shifts.

As artificial intelligence (AI) is increasingly integrated into medical diagnostics, it is essential...

A machine learning model for prediction of risk of dementia superimposed on delirium in intensive care patients.

BACKGROUND: The objective of this study was to construct a predictive model using multiple machine l...

MRI neuroimaging-based Alzheimer's disease stage classification using deep neural network with convolutional block attention module and GAN-style noise injection.

Millions of individuals worldwide suffer from Alzheimer's disease (AD), a chronic, incurable neurolo...

Amyloid-β and phosphorylated-tau accumulation in retrosplenial cortex of young Alzheimer's disease model: Impact on network activity.

BackgroundThe retrosplenial cortex (RSC) is a cortical area that functions as a key component of the...

Risk Factors for Postoperative Delirium in Nonintensive Care Unit Patients: Machine Learning Approach.

Postoperative delirium is a frequent and serious complication lacking effective prediction tools for...

Developing a Multimodal Screening Algorithm for Mild Cognitive Impairment and Early Dementia in Home Health Care: Protocol for a Cross-Sectional Case-Control Study Using Speech Analysis, Large Language Models, and Electronic Health Records.

BACKGROUND: Mild cognitive impairment and early dementia (MCI-ED) are frequently unrecognized in rou...

An interpretable AI system reduces false-positive MRI diagnoses by stratifying high-risk breast lesions.

Breast cancer diagnosis using magnetic resonance imaging remains limited by high false-positive rate...

Multimodal fusion and explainability of artificial intelligence models in Alzheimer's Disease detection.

The integration of multimodal data has emerged as a powerful strategy for enhancing the accuracy and...

Multi-dimensional EEG analysis reveals distinct neurophysiological patterns in Alzheimer's and frontotemporal dementia.

Investigating the Utility of Explainable Artificial Intelligence for Neuroimaging-Based Dementia Diagnosis and Prognosis.

Artificial intelligence and neuroimaging enable accurate dementia prediction but often involve 'blac...

Biases in an artificial intelligence image-generator's depictions of healthy aging and Alzheimer's.

OBJECTIVE: This content analysis study investigates potential biases in image generation by 2 artifi...

Functional and Clinical: An Explainable Deep Learning Model for Multimodal Alzheimer's Disease Classification.

PURPOSE: Functional magnetic resonance imaging (fMRI) and deep learning models can classify Alzheime...

Interpretable model-driven end-to-end deep learning of geometric shaping for DML-based IM/DD systems.

Direct modulation lasers (DMLs) are a low-cost technology for short-reach intensity modulation and d...

Development of an interpretable machine learning model for predicting 4-year chronic kidney disease risk in elderly hypertensive patients.

INTRODUCTION: Age and hypertension are key drivers of renal impairment, predisposing older hypertens...