Emergency Medicine

Enhanced diagnosis of diabetic retinopathy: integrating advanced algorithms for automated detection and classification.

AIM: To develop an automated diagnostic system for early detection of diabetic retinopathy (DR) using fundus images by identifying exudates, hemorrhages, and microaneurysms with advanced image processing and machine learning techniques. METHODS: Fundus images from the IDRiD dataset and additional Kaggle datasets were used. A wavelet-based band-pass filter was applied for edge enhancement of retina...

Evaluation of Brain Age on Magnetic Resonance Imaging Using Machine Learning Techniques in Patients With Hearing Loss.

OBJECTIVES: Hearing impairment is strongly linked to cognitive decline, with individuals having mild-to-severe hearing loss estimated as having a twofold to fivefold increased risk of all-cause dementia. Given the lack of a cure for dementia, addressing conditions associated with increased dementia risk, like hearing loss, is essential. "Brain age," estimated from magnetic resonance imaging (MRI) ...

Explainable Machine Learning Guided Enhanced Sampling of Protein Conformational Transition in HSP90.

Elucidating the thermodynamics, kinetics, and mechanisms of protein conformational transitions remains a longstanding challenge in molecular dynamics ...

Exploring the potential targets and mechanisms of malachite green induced lung adenocarcinoma through network toxicology and molecular docking.

Malachite green (MG), a synthetic dye, has turned into a major risk to human health, because of its toxicity of teratogenic, genotoxic, carcinogenic, ...

Independent bone-level diagnostic accuracy study of an AI tool for detecting appendicular skeletal fractures on radiographs.

OBJECTIVES: To perform an in-depth evaluation of the diagnostic test accuracy of a commercially available AI tool for assistance in fracture detection...

Artificial Intelligence in Trauma Care: A Systematic Review of Resuscitation, Diagnosis, Risk Prediction, and Management.

BACKGROUND: Artificial intelligence (AI) is increasingly being explored in trauma care as a tool to support clinical decision-making. OBJECTIVE: To ev...

Development and validation of a novel blood-based biomarker for gastric cancer triage in chronic dyspepsia.

Global implementation of gastric cancer (GC) screening in chronic dyspepsia populations faces challenges due to the high number-needed-to-scope (NNS) ...

Elucidating formability limits in warm incremental sheet forming of AZ61 magnesium alloy using integrated grey relational and data-driven analysis.

The growing demand for lightweight structural components in the automotive and aerospace sectors has intensified interest in magnesium alloys; however...

Machine learning-based prognostic modeling of early clinical outcomes in very low birth weight infants: insights from a nationwide cohort on delivery room resuscitation, prematurity-related complications, and mortality.

Chest X-Ray evaluation using GPT for tube thoracostomy or conservative care in non-tension spontaneous pneumothorax.

BACKGROUND/OBJECTIVE: Spontaneous pneumothorax (SP) commonly presents to the emergency department (ED), and clinicians must rapidly decide between con...

Machine Learning-Based Pattern Recognition of Risk Factors for Low Back Pain among Adolescent Cricket Players in Dhaka City.

Low back pain (LBP) is common among adolescent cricketers, often due to repetitive lumbar stress. This study investigated LBP among 450 adolescent cri...

Cross-sectional research: Application of an Artificial Intelligence-Based Pediatric Early Warning Score in the Pediatric Emergency Department.

BACKGROUND: There are a large number of pediatric emergency patients. Due to the fact that the children cannot describe their own conditions, there is...

Association of initial core volume on non-contrast CT using a deep learning algorithm with clinical outcomes in acute ischemic stroke: a potential tool for selection and prognosis?

BACKGROUND: In an extended time window, contrast-based neuroimaging is valuable for treatment selection or prognosis in patients with stroke undergoin...

Large Language Models in Cardiology: Systematic Review.

BACKGROUND: Large language models (LLMs) are increasingly used in health care, but their role in cardiology has not yet been systematically evaluated....

Targeted use of large language models for EHR-based computable phenotyping.

OBJECTIVE: Computable phenotypes derived from electronic health records (EHRs) are central to clinical research and quality reporting. Although large ...

Nursing Perceptions of the Intended Use of Artificial Intelligence to Prevent Medication Errors: A Qualitative Descriptive Study.

AIM: To explore the perceptions of nursing professionals in high-demand healthcare services regarding the adoption of AI-based support systems for the...

The tip of the iceberg: Profiling cooling agents using computational approaches to inform tobacco regulatory science.

Cooling agents (chemicals added to impart a cooling sensation) in tobacco products are receiving increased attention due to their use as menthol subst...

Construction and interpretability evaluation of a prediction model for radiographic pneumonia in children at outpatient and emergency departments.

BACKGROUND: Radiographic confirmation is crucial for pediatric pneumonia diagnosis, but chest X-ray overuse in outpatient and emergency settings raise...

The predictive value of serological markers for successful weaning and 30-day mortality in patients with severe intracerebral hemorrhage.

AIM: This study intends to screen serological indicators related to weaning outcomes and 30-day mortality in severe intracerebral hemorrhage (ICH) pat...

Prediction of acetylcholinesterase inhibition associated with Alzheimer's disease using hybrid descriptor and graph-based machine learning models.

Accurate prediction of acetylcholinesterase (AChE) inhibitory activity is important in drug discovery and environmental toxicology because AChE inhibi...