Geriatrics

Clinical-radiological machine learning model for non-invasive diagnosis and stratification of peripheral artery disease: a multicenter study.

BACKGROUND AND OBJECTIVE: Peripheral artery disease (PAD) is an atherosclerotic disorder prevalent in the elderly that leads to peripheral function decline and body composition changes. Current diagnostic approaches lack sensitivity for early PAD detection and staging. This study aimed to develop and validate machine learning (ML) models of clinical and CT-based radiological features to improve PA...

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 neural plasticity, slower information processing, and impaired network integration. These age-related alterations influence the brain's response to anesthetic agents, particularly electroencephalography (EEG) activity. This narrative review summarizes the characteristic EEG features induced by comm...

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 width-to-albumin ratio (RAR) and osteoarthritis (OA...

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 fracture (MOF) and hip fracture. FRAX scores are o...

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 heterogeneity in aging. We investigated whether accel...

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

BACKGROUND: Recent advancements in critical care have highlighted the need for comprehensive, multimodal datasets to support clinical decision-making ...

Full-Scope Vectorization of Geographical Elements from Large-Size Remote Sensing Imagery.

Large-size very-high-resolution (VHR) remote sensing imagery has emerged as a critical data source for high-precision vector mapping of multi-scale ge...

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 delirium screening and guideline-concordant prevent...

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 inform the design of a pragmatic randomized controlled ...

Uncertainty in deep learning for EEG under dataset shifts.

As artificial intelligence (AI) is increasingly integrated into medical diagnostics, it is essential that predictive models provide not only accurate ...

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 learning algorithms to predict the risk of dementia...

Development of machine learning-driven non-invasive diagnostic models for idiopathic membranous nephropathy in Chinese patients.

BACKGROUND: Idiopathic membranous nephropathy (IMN) is a major cause of nephrotic syndrome and end-stage renal disease, but the gold-standard diagnost...

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 neurological disorder. For the longevity of people, a com...

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 core network of brain regions involved in cogniti...

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 general ward patients. This study aimed to identi...

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 routine care, particularly in home health care (HHC),...

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 rates and substantial inter-reader variability, especi...

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 interpretability of artificial intelligence (AI) ...

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 'black box' models that can be difficult to trust. Expl...