Emergency Medicine

Explainable machine learning for early prediction and anatomical classification of pulmonary embolism in the emergency department.

BACKGROUND: Pulmonary thromboembolism (PTE) is a life‑threatening condition that requires prompt and accurate evaluation in the emergency department (ED). Standardized clinical scoring systems, including the Wells and revised Geneva scores, form the cornerstone of initial risk stratification but have limited specificity, leading to unnecessary D‑dimer testing and frequent overuse of CT pulmonary a...

Improving Surgical Scheduling Efficiency with Operating Room Coordination and Allocation at Centre Antoine Lacassagne: A Comparative Analysis of Patient Outcomes and Operational Metrics.

BACKGROUND: Operating room (OR) scheduling is critical for timely patient care, optimal resource usage, and equitable surgical access. Despite the cost and complexity of ORs, few tools address patient scheduling comprehensively. This study evaluates ORCAL (Operating Room Coordination and Allocation at Centre Antoine Lacassagne), a scheduling assistant designed to improve OR scheduling efficiency. ...

Deep-learning CT reconstruction improves image quality but not diagnostic accuracy for sacral fragility fractures: MRI remains essential.

PURPOSE: To determine whether modern deep-learning CT reconstruction improves trauma-relevant diagnostic performance for sacral fragility fractures (S...

Development and validation of a routine blood test-based model to predict in-hospital postoperative pulmonary infection in older patients with hip fracture.

BACKGROUND: Postoperative pulmonary infection (PPI) is a common and serious complication in older adults undergoing hip fracture surgery, leading to p...

Enhancing Emergency Medical Response Education through Generative AI-Powered Game-Based Learning: A Retrospective Comparative Study.

OBJECTIVE: Traditional lecture-based learning (LBL) is often insufficient for cultivating the practical decision-making skills required in high-stakes...

Machine learning-based prediction of hospital-associated complications after tibial fracture surgery in older patients: a nationwide Japanese database study.

BACKGROUND: Older adults requiring emergency surgery for acute tibial fractures are vulnerable to hospital-associated complications (HACs), but admiss...

The Rise of AI Companions: From Prediction to Surgery in the Digital Twin Era.

Artificial intelligence (AI) is rapidly reshaping orthopaedic surgery, supported by advances in data science, computational power, and perioperative d...

Development of Convolutional Neural Networks for Classification and Characterisation of Proximal Humerus Fractures on Computed Tomography.

BACKGROUND: Agreement between surgeons on classification, characterization and choice of treatment for proximal humerus fractures (PHFs) is poor, lead...

Comprehensive analysis of the association between perfluorooctanoic acid exposure and osteosarcoma progression.

Perfluorooctanoic acid (PFOA), a persistent environmental pollutant, represents a chronic environmental stressor, yet its role in osteosarcoma progres...

Predictive Models for Time to First Opioid Use Disorder or Opioid Overdose Among Older Adults.

BACKGROUND: Limited data exist on predictive models incorporating patient-reported and claims-based measures to identify older adults at risk for opio...

Enhancing out-of-hospital emergency care via lexical machine learning modeling of chief complaints.

Emergency medical services (EMS) professionals make high-stakes decisions in austere environments. To support out-of-hospital emergency care, we devel...

Screening of core targets for Di(2-ethylhexyl) Phthalate-related gastric cancer based on machine learning, molecular docking, and SHAP analysis.

PURPOSE: Given the existing uncertainties regarding the link between Di(2-ethylhexyl) phthalate (DEHP) exposure and gastric cancer (GC) progression, t...

"Check Symptoms & Get Care": Mount Sinai's AI Triage Solution.

Mount Sinai Health System (MSHS), one of New York City's largest academic medical centers, faced a common patient-access challenge: individuals presen...

Evaluation of deep learning architectures for predicting ligand interactions with neurologically relevant GPCRs.

G protein-coupled receptors (GPCRs) are therapeutic targets for over 30% of approved drugs, yet specific GPCR subtypes act as molecular initiating eve...

Chest Radiograph-based Artificial Intelligence for Osteoporosis: Accuracy and Associations With Fracture and Mortality.

OBJECTIVES: To evaluate an AI tool for opportunistic osteoporosis screening using chest radiographs (CXRs), focusing on its diagnostic accuracy agains...

Clinician-supervised large language AI model after-discharge instruction generation for common emergency department presentations.

OBJECTIVES: After-discharge instructions often fail due to poor usability and language misalignment. We evaluated a clinician-supervised method for ge...

Fast MRI of bones in the knee - An AI-driven reconstruction approach for adiabatic inversion recovery prepared ultra-short echo time sequences.

PURPOSE: Inversion recovery prepared ultra-short echo time (IR-UTE)-based MRI enables radiation-free visualization of osseous tissue. However, achievi...

Machine Learning Prediction of Postoperative Mortality in Older Emergency Surgery Patients.

INTRODUCTION: The older adult population account for 35% of emergency general surgery (EGS) admissions and have an increased risk-adjusted odds of mor...

Artificial Intelligence (AI): Some fundamentals to know its place and potential in forensic toxicology.

Bouncing back from stress: objective markers of expressive flexibility and resilience in emergency healthcare workers using computer vision.

Healthcare workers (HCWs) in emergency departments face significant mental health risk due to chronic stressors and repeated trauma, yet symptom under...