Primary Care

Exchange of Quantitative Computed Tomography Assessed Body Composition Data Using Fast Healthcare Interoperability Resources as a Necessary Step Toward Interoperable Integration of Opportunistic Screening Into Clinical Practice: Methodological Development Study.

BACKGROUND: Fast Healthcare Interoperability Resources (FHIR) is a widely used standard for storing and exchanging health care data. At the same time, image-based artificial intelligence (AI) models for quantifying relevant body structures and organs from routine computed tomography (CT)/magnetic resonance imaging scans have emerged. The missing link, simultaneously a needed step in advancing pers...

Digital health technologies in swallowing care from screening to rehabilitation: A narrative review.

OBJECTIVES: Digital health technologies (DHTs) have rapidly advanced in the past two decades, through developments in mobile and wearable devices and most recently with the explosion of artificial intelligence (AI) capabilities and subsequent extension into the health space. DHT has myriad potential applications to deglutology, many of which have undergone promising investigations and developments...

Surface Electromyography Combined with Artificial Intelligence in Predicting Neuromuscular Falls in the Elderly: A Narrative Review of Present Applications and Future Perspectives.

: Falls among the elderly are a major public health concern, leading to increased disability and mortality. Traditional protective measures are import...

A Scoping Review of AI-Driven Digital Interventions in Mental Health Care: Mapping Applications Across Screening, Support, Monitoring, Prevention, and Clinical Education.

BACKGROUND/OBJECTIVES: Artificial intelligence (AI)-enabled digital interventions are increasingly used to expand access to mental health care. This P...

Imaging patterns and recommendations for diagnosis, staging, and management of lung cancer.

Lung cancer is the second most commonly diagnosed cancer worldwide. In the present era of targeted therapy for various lung cancer mutations, it is es...

Diagnostic value of fully automated CT pulmonary angiography in patients with chronic thromboembolic pulmonary hypertension and chronic thromboembolic disease.

OBJECTIVES: To evaluate the value of employing artificial intelligence (AI)-assisted CT pulmonary angiography (CTPA) for patients with chronic thrombo...

Delirium in Critically Ill Geriatric Surgical Patients: A Systematic Review of Screening, Risk Factors, Diagnosis, and Management.

OBJECTIVE: This systematic review aims to evaluate optimal early screening strategies, significant risk factors, and effective diagnostic and manageme...

Integrating Artificial Intelligence and Precision Therapeutics for Advancing the Diagnosis and Treatment of Age-Related Macular Degeneration.

Age-related macular degeneration (AMD) is a multifactorial retinal disease influenced by complex molecular mechanisms, including genetic susceptibilit...

Risk prediction of stroke-associated pneumonia in acute ischemic stroke with atrial fibrillation using machine learning models.

Stroke-associated pneumonia (SAP) is a serious complication of acute ischemic stroke (AIS), significantly affecting patient prognosis and increasing h...

Harnessing the hybrid machine learning methods for stroke risk classification.

Stroke is a leading global cause of death, with 80% of cases considered preventable through early detection and awareness. This study employs artifici...

Gender-specific sarcopenia screening in hemodialysis: insights from lower limb strength and physiological indicators.

OBJECTIVE: Maintenance hemodialysis (MHD) patients often suffer from sarcopenia, affecting lower limb muscle strength and increasing the risk of falls...

Contemporary Prevention and Management of Postoperative Delirium in Cardiac Surgery Patients.

This review examines the prevention and management of postoperative delirium in cardiac surgery patients, a frequent complication linked with signific...

Study on Liver Sinusoidal Endothelial Cell Fenestrations Based on Cellular Omics-Structure Integration Technology and Its Application in Metabolic Diseases.

This study developed a new Cellular Omics-Structural Integration (COSI) technology platform to address the limitation of traditional technologies in s...

Synergistic AI-resident approach achieves superior diagnostic accuracy in tertiary ophthalmic care for glaucoma and retinal disease.

INTRODUCTION: Artificial intelligence (AI) shows promise in ophthalmology, but its potential in tertiary care settings in Latin America remains unders...

A data-driven scoring framework for personalized employee health check-ups: Integrating historical laboratory trends and evidence-based prevalence.

INTRODUCTION: Rising healthcare costs and growing demand for personalized preventive care have highlighted the need for data-driven approaches to opti...

The Forcing Factors of Physical Inactivity and Obesity in the United States - An Artificial Intelligence Analysis of an Ecological Framework.

BACKGROUND: A sedentary lifestyle, and obesity, are primary factors forcing the ongoing chronic disease health crisis in the United States. The aim of...

Machine Learning for Predicting the Transition From Gestational Diabetes to Type 2 Diabetes: A Systematic Review.

Gestational diabetes mellitus (GDM) significantly increases the risk of developing type 2 diabetes (T2D) postpartum. Early identification of high-risk...

T2D-LVDD: neural network-based predictive models for left ventricular diastolic dysfunction in type 2 diabetes.

Cardiovascular disease complications are the leading cause of morbidity and mortality in patients with Type 2 diabetes (T2DM). Left ventricular diasto...

Evaluating prediction of short-term tolerability of five type 2 diabetes drug classes using routine clinical features: UK population-based study.

AIMS: A precision medicine approach in type 2 diabetes (T2D) needs to consider potential treatment risks alongside established benefits for glycaemic ...

Impact of sarcopenia and obesity on mortality in older adults with SARS-CoV-2 infection: automated deep learning body composition analysis in the NAPKON-SUEP cohort.

INTRODUCTION: Severe respiratory infections pose a major challenge in clinical practice, especially in older adults. Body composition analysis could p...