Geriatrics

Cerebrospinal fluid inflammatory cytokines as prognostic indicators for cognitive decline across Alzheimer's disease spectrum.

BackgroundNeuroinflammation actively contributes to the pathophysiology of Alzheimer's disease (AD); however, the value of neuroinflammatory biomarkers for disease-staging or predicting disease progression remains unclear.ObjectiveTo investigate diagnostic and prognostic utility of inflammatory biomarkers in combination with conventional AD biomarkers.MethodsData from 258 participants in the Alzhe...

An end-to-end neural network for 4D cardiac CT reconstruction using single-beat scans.

Motion artifacts remain a significant challenge in cardiac CT imaging, often impairing the accurate detection and diagnosis of cardiac diseases. These artifacts result from involuntary cardiac motion, and traditional mitigation methods typically rely on retrospective rescans, which increase radiation exposure and are less effective for patients with irregular heart rhythms. In this study, we propo...

The clinical significance of an AI-based assumption model for neurocognitive diseases using a novel dual-task system.

Dual-task composed of gait or stepping tasks combined with cognitive tasks has been well-established as valuable tools for detecting neurocognitive di...

Revolutionizing Alzheimer's disease detection with a cutting-edge CAPCBAM deep learning framework.

Early and accurate diagnosis of Alzheimer's disease (AD) is crucial for effective treatment. While the integration of deep learning techniques for AD ...

Artificial intelligence real-time automated recognition of the gastric antrum cross-sectional area and motility rhythm via bedside ultrasound: a pilot study.

The cross-sectional area (CSA) of the gastric antrum and its motility rhythm reflects the gastrointestinal function of critically ill patients. Monito...

Explainable machine learning framework for biomarker discovery by combining biological age and frailty prediction.

Biological age (BA) and frailty represent two distinct health measures that offer valuable insights into the aging process. Comparing and analyzing bl...

A Multimodal Approach for Early Identification of Mild Cognitive Impairment and Alzheimer's Disease With Fusion Network Using Eye Movements and Speech.

Detecting Alzheimer's disease (AD) in its earliest stages, particularly during an onset of Mild Cognitive Impairment (MCI), remains challenging due to...

Social robot PIO intervention for improving cognitive function and depression in older adults with mild to moderate dementia in day care centers: A randomized controlled trial.

The increases in the older population, the prevalence of dementia, and the resulting social costs are burdensome to individuals, families, and the nat...

Alzheimer's Disease Dementia Prevalence in the United States: A County-Level Spatial Machine Learning Analysis.

A growing body of literature has examined the impact of neighborhood characteristics on Alzheimer's disease (AD) dementia, yet the spatial variability...

Computational modelling for risk assessment of neurological disorder in diabetes using Hodgkin-Huxley model.

BACKGROUND: Diabetes mellitus, characterized by chronic glucose dysregulation, significantly increases the risk of neurological disorders such as cogn...

Daily Automated Prediction of Delirium Risk in Hospitalized Patients: Model Development and Validation.

BACKGROUND: Delirium is common in hospitalized patients and is correlated with increased morbidity and mortality. Despite this, delirium is underdiagn...

Transcriptomic analyses of human brains with Alzheimer's disease identified dysregulated epilepsy-causing genes.

BACKGROUND & OBJECTIVE: Alzheimer's Disease (AD) patients at multiple stages of disease progression have a high prevalence of seizures. However, wheth...

Automated opportunistic screening for osteoporosis using deep learning-based automatic segmentation and radiomics on proximal femur images from low-dose abdominal CT.

RATIONALE AND OBJECTIVES: To establish an automated osteoporosis detection model based on low-dose abdominal CT (LDCT). This model combined a deep lea...

Early prediction of sepsis associated encephalopathy in elderly ICU patients using machine learning models: a retrospective study based on the MIMIC-IV database.

BACKGROUND: Sepsis associated encephalopathy (SAE) is prevalent among elderly patients in the ICU and significantly affects patient prognosis. Due to ...

Predicting Intraoperative Burst Suppression Using Preoperative EEG and Patient Characteristics.

Burst suppression (BS) is an electroencephalogram (EEG) pattern observed in patients undergoing general anesthesia. The occurrence of BS is associated...

Comparative analysis of ChatGPT-4o mini, ChatGPT-4o and Gemini Advanced in the treatment of postmenopausal osteoporosis.

BACKGROUND: Osteoporosis is a sex-specific disease. Postmenopausal osteoporosis (PMOP) has been the focus of public health research worldwide. The pur...

A prediction model of pediatric bone density from plain spine radiographs using deep learning.

Osteoporosis, a bone disease characterized by decreased bone mineral density (BMD) resulting in decreased mechanical strength and an increased fractur...

Identification of aging-related biomarkers for intervertebral disc degeneration in whole blood samples based on bioinformatics and machine learning.

INTRODUCTION: Aging is characterized by gradual structural and functional changes in the body over time, with intervertebral disc degeneration (IVDD) ...

Comparative study of XGBoost and logistic regression for predicting sarcopenia in postsurgical gastric cancer patients.

The use of machine learning (ML) techniques, particularly XGBoost and logistic regression, to predict sarcopenia among postsurgical gastric cancer pat...

c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder that requires early diagnosis for effective management. However, issues with currently availa...