Emergency Medicine

Convolutional autoencoder-based deep learning for intracerebral hemorrhage classification using brain CT images.

Intracerebral haemorrhage (ICH) is a common form of stroke that affects millions of people worldwide. The incidence is associated with a high rate of mortality and morbidity. Accurate diagnosis using brain non-contrast computed tomography (NCCT) is crucial for decision-making on potentially life-saving surgery. Limited access to expert readers and inter-observer variability imposes barriers to tim...

Applying Machine Learning to the ANZELA-QI Database to Predict Adverse Outcomes for Patients Undergoing Emergency Laparotomy.

BACKGROUND: Emergency laparotomy is associated with high rates of morbidity and mortality. Accurate, individualised risk prediction models can be used to improve shared decision-making, discharge planning and enhance patient flow. This study used the ANZELA-QI database to apply novel machine learning models to stratify the risk of adverse outcomes in patients undergoing emergency laparotomy.

Moving towards making (quantitative) structure-activity relationships ((Q)SARs) for toxicity-related endpoints findable, accessible, interoperable and reusable (FAIR).

(Quantitative) structure-activity relationships ((Q)SARs) are widely used in chemical safety assessment to predict toxicological effects. Many thousan...

Development of a deep-learning algorithm for etiological classification of subarachnoid hemorrhage using non-contrast CT scans.

OBJECTIVES: This study aims to develop a deep learning algorithm for differentiating aneurysmal subarachnoid hemorrhage (aSAH) from non-aneurysmal sub...

A deep learning-based approach to automated rib fracture detection and CWIS classification.

PURPOSE: Trauma-induced rib fractures are a common injury. The number and characteristics of these fractures influence whether a patient is treated no...

Impact of test set composition on AI performance in pediatric wrist fracture detection in X-rays.

OBJECTIVES: To evaluate how different test set sampling strategies-random selection and balanced sampling-affect the performance of artificial intelli...

The underlying molecular mechanisms and biomarkers of Hip fracture combined with deep vein thrombosis based on self sequencing bioinformatics analysis.

BACKGROUND: Thrombus formation is a severe complication in orthopedic surgery, significantly increasing mortality in patients with fractures. Therefor...

Research on Machine Learning Models Based on Cranial CT Scan for Assessing Prognosis of Emergency Brain Injury.

BACKGROUND: To evaluate the prognosis of patients with traumatic brain injury according to the Computed Tomography (CT) findings of skull fracture and...

Dynamic Predictive Models of Cardiogenic Shock in STEMI: Focus on Interventional and Critical Care Phases.

: While early risk stratification in STEMI is essential, the threat of cardiogenic shock (CS) persists after revascularization due to reperfusion inju...

The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients.

BACKGROUND: Acute upper gastrointestinal bleeding (AUGIB) is one of the most common critical diseases encountered in the intensive care unit (ICU), wi...

The impact of different task contexts on emergency responders' trust and usage intention of artificial intelligence.

Proper use of artificial intelligence (AI) can significantly enhance emergency responders' performance. However, they do not always trust or appropria...

Patient Triage and Guidance in Emergency Departments Using Large Language Models: Multimetric Study.

BACKGROUND: Emergency departments (EDs) face significant challenges due to overcrowding, prolonged waiting times, and staff shortages, leading to incr...

Efficacy of laryngeal mask epinephrine in neonatal resuscitation; an ovine study.

BACKGROUND: The efficacy of Laryngeal Mask Airway (LMA) epinephrine during neonatal resuscitation has not been studied. We hypothesize that LMA epinep...

Intravascular Lithotripsy Versus Rotational Atherectomy for Calcified Coronary Lesions: A Systematic Review and an Updated Meta-Analysis of Clinical Outcomes.

BACKGROUND: Severe coronary artery calcification (CAC) remains a significant challenge in interventional cardiology, especially in elderly and comorbi...

Chlorogenic acid alleviates deoxynivalenol-induced damage in porcine trophectoderm cells by regulating the PI3K/AKT signaling pathway†.

The proliferation and migration of porcine trophectoderm (pTr) cells are crucial processes during the early stages of embryo implantation in sows. The...

Evaluating large language model workflows in clinical decision support for triage and referral and diagnosis.

Accurate medical decision-making is critical for both patients and clinicians. Patients often struggle to interpret their symptoms, determine their se...

Deep learning approach based on a patch residual for pediatric supracondylar subtle fracture detection.

Supracondylar humerus fractures in children are among the most common elbow fractures in pediatrics. However, their diagnosis can be particularly chal...

A correlational study of plasma galectin-3 as a potential predictive marker of postoperative delirium in patients with acute aortic dissection.

This study aimed to demonstrate whether plasma galectin-3 could predict the development of postoperative delirium (POD) in patients with acute aortic ...

Automated Emergent Large Vessel Occlusion Detection Using Viz.ai Software and Its Impact on Stroke Workflow Metrics and Patient Outcomes in Stroke Centers: A Systematic Review and Meta-analysis.

The implementation of artificial intelligence (AI), particularly Viz.ai software in stroke care, has emerged as a promising tool to enhance the detect...

Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review.

This work provides a comprehensive review of the recent advancements in the toughening modification methods for epoxy resins. The study explores a var...