Emergency Medicine

Hitting the Bull's AI: Artificial Intelligence-derived Imaging Features and their Association with Outcomes in CT-guided Lung Biopsy, a Retrospective Study.

PURPOSE: This study aims to evaluate whether quantitative imaging features analyzed by an artificial intelligence (AI) tool are associated with success rate, histopathological results, and complication risks of CT-guided lung biopsies. METHODS: A retrospective study was conducted on 120 CT-guided biopsies of suspicious pulmonary lesions with pathology reports. Associations between technical succes...

Unraveling the molecular mechanisms of air pollution-induced lung adenocarcinoma through machine learning, network toxicology, and molecular docking.

OBJECTIVE: Airborne environmental contaminants are established carcinogens. This investigation elucidates the mechanistic contributions to pulmonary adenocarcinoma (LUAD) pathogenesis. METHODS: Transcriptomic profiling through differential expression analysis and weighted gene co-expression network analysis (WGCNA) identified malignancy-associated molecular targets. Computational screening of envi...

Development and validation of machine learning models to predict PTSD at multiple time points in hospitalized trauma patients.

BACKGROUND: Posttraumatic stress disorder (PTSD) is a severe trauma-related mental disorder with high global burden. Early identification remains chal...

SERS on analyte-enriched blood for rapid, culture-free sepsis recognition and causative pathogen identification with super operational neural networks.

Sepsis remains a leading cause of morbidity and mortality, yet routine diagnostics are slow, culture-dependent, and often lack the sensitivity or spec...

Improving Acute Care Surgery with Artificial Intelligence: A Practical Review.

Artificial intelligence (AI) and machine learning are poised to transform trauma care across the entire continuum, from prehospital triage to postoper...

Can Artificial Intelligence and Machine Learning help us improve motor vehicle crash trauma triage?

Understanding and Addressing Bias in Artificial Intelligence Systems: A Primer for the Emergency Medicine Physician.

Artificial intelligence (AI) tools and technologies are increasingly being integrated into emergency medicine (EM) practice, not only offering potenti...

Early Insights Among Emergency Medicine Physicians on Artificial Intelligence: A National, Convenience-sample Survey of the American College of Emergency Physicians.

OBJECTIVES: This study aimed to assess the current utilization of artificial intelligence (AI) tools among emergency physicians, their attitudes towar...

Micro- and nanoplastics (MNPs) in liquid food: From occurrence, health risks and migration mechanisms to AI-enabled analytical and circular solutions.

Microplastics (MPs, 1 μm-5 mm) and nanoplastics (NPs, <1 μm) are routinely detected in wide array of liquid food, including drinking water, milk, beve...

Elevated Retinal Neovascularization on Widefield Optical Coherence Tomography Angiography Predicts Complications in High-Risk Proliferative Diabetic Retinopathy.

PURPOSE: To determine whether retinal neovascularization (RNV) metrics derived from single-shot widefield swept-source OCT angiography (SS-OCTA) predi...

Generation of explainable QSAR models for predicting chemical-induced urinary tract toxicity.

Chemical-induced urinary tract toxicity, particularly in the bladder and ureters, remains undercharacterized relative to nephrotoxicity. We present an...

Prediction of independent ambulation at hospital discharge in patients with proximal femoral fractures based on preoperative clinical information: A retrospective study using extreme gradient boosting and SHapley additive explanations.

BACKGROUND: Independent ambulation at hospital discharge is a critical determinant of discharge destination and caregiving burden in older adults with...

A Machine Learning Strategy to Predict the Number of High-Acuity Children Who Leave Without Being Seen From the Emergency Department.

OBJECTIVES: The purpose of this study was to create an operationally useful machine learning model that predicts the number of high-acuity left withou...

Ensemble machine learning for early mortality risk stratification in septic orthopedic trauma: an international cohort study.

BACKGROUND: Sepsis represents a life-threatening complication in severe orthopedic trauma, significantly increasing short-term mortality risk. Despite...

Ethical challenges and opportunities for integrating predictive analytics in community-based overdose prevention.

As predictive analytics become more widely integrated into local public health responses to the United States overdose epidemic, community-based subst...

Plasticizer mixture exposure-induced preeclampsia pathogenesis: Integrated single-cell and bulk transcriptomics, network toxicology, and molecular docking insights.

PURPOSE: While plasticizers are known preeclampsia (PE) risk factors, the mechanisms of combined exposure to ATBC, DEP, DMP, and DOP remain unclear. T...

The micro- and nanoplastics exposome feedback loop drives synergistic air-lung toxicity.

Airborne micro- and nanoplastics (MNPs) are now recognized as persistent components of the atmospheric exposome. While their presence is established, ...

A clinical-AI correlation for integrating artificial intelligence into stroke care: a systematized literature review and practice framework.

BACKGROUND AND PURPOSE: The rapid integration of artificial intelligence (AI) into stroke care has outpaced many clinicians' ability to critically eva...

Beyond diagnosis: deep-learning-based analysis of hospitalization using abdominal radiographs in the emergency department.

PURPOSE: Abdominal radiography (AXR) is routinely performed in emergency departments (ED) but has limited clinical utility. Thus, this study developed...

Artificial intelligence-driven prostate cancer diagnosis: Enhancing accuracy and personalizing patient care.

Prostate cancer remains a major global burden; diagnostic pathways rely on prostate-specific antigen (PSA), multiparametric magnetic resonance imaging...