Emergency Medicine

Machine Learning-Based Rapid Prediction of Torsional Performance of Personalized Peripheral Artery Stent.

The complex mechanical environment of peripheral arteries makes stents with poor torsional performance more prone to fracture, and stent fracture is considered a precursor to in-stent restenosis (ISR). Therefore, studying the torsional performance of stents is crucial. However, while the finite element method (FEM) can accurately simulate the torsional behavior of stents, its time-consuming nature...

A Non-contrast Head CT Foundation Model for Comprehensive Neuro-Trauma Triage

Recent advancements in AI and medical imaging offer transformative potential in emergency head CT interpretation for reducing assessment times and improving accuracy in the face of an increasing request of such scans and a global shortage in radiologists. This study introduces a 3D foundation model for detecting diverse neuro-trauma findings with high accuracy and efficiency. Using large languag...

Rapidly Built Medical Crash Cart! Lessons Learned and Impacts on High-Stakes Team Collaboration in the Emergency Room

Designing robots to support high-stakes teamwork in emergency settings presents unique challenges, including seamless integration into fast-paced en...

RELICT: A Replica Detection Framework for Medical Image Generation

Despite the potential of synthetic medical data for augmenting and improving the generalizability of deep learning models, memorization in generativ...

Multi-Target Federated Backdoor Attack Based on Feature Aggregation

Current federated backdoor attacks focus on collaboratively training backdoor triggers, where multiple compromised clients train their local trigger...

Facilitating Emergency Vehicle Passage in Congested Urban Areas Using Multi-agent Deep Reinforcement Learning

Emergency Response Time (ERT) is crucial for urban safety, measuring cities' ability to handle medical, fire, and crime emergencies. In NYC, medical...

Moving Beyond Medical Exam Questions: A Clinician-Annotated Dataset of Real-World Tasks and Ambiguity in Mental Healthcare

Current medical language model (LM) benchmarks often over-simplify the complexities of day-to-day clinical practice tasks and instead rely on evalua...

MHQA: A Diverse, Knowledge Intensive Mental Health Question Answering Challenge for Language Models

Mental health remains a challenging problem all over the world, with issues like depression, anxiety becoming increasingly common. Large Language Mo...

Deep learning model for identifying acute heart failure patients using electrocardiography in the emergency room.

AIMS: Acute heart failure (AHF) poses significant diagnostic challenges in the emergency room (ER) because of its varied clinical presentation and lim...

DynSegNet:Dynamic Architecture Adjustment for Adversarial Learning in Segmenting Hemorrhagic Lesions from Fundus Images

The hemorrhagic lesion segmentation plays a critical role in ophthalmic diagnosis, directly influencing early disease detection, treatment planning,...

PathFinder: A Multi-Modal Multi-Agent System for Medical Diagnostic Decision-Making Applied to Histopathology

Diagnosing diseases through histopathology whole slide images (WSIs) is fundamental in modern pathology but is challenged by the gigapixel scale and...

Towards Fair Medical AI: Adversarial Debiasing of 3D CT Foundation Embeddings

Self-supervised learning has revolutionized medical imaging by enabling efficient and generalizable feature extraction from large-scale unlabeled da...

AI-Based Thermal Video Analysis in Privacy-Preserving Healthcare: A Case Study on Detecting Time of Birth

Approximately 10% of newborns need some assistance to start breathing and 5\% proper ventilation. It is crucial that interventions are initiated as ...

Accurate AI-Driven Emergency Vehicle Location Tracking in Healthcare ITS Digital Twin

Creating a Digital Twin (DT) for Healthcare Intelligent Transportation Systems (HITS) is a hot research trend focusing on enhancing HITS management,...

Learning Generalizable Features for Tibial Plateau Fracture Segmentation Using Masked Autoencoder and Limited Annotations

Accurate automated segmentation of tibial plateau fractures (TPF) from computed tomography (CT) requires large amounts of annotated data to train de...

Improving Nursing Students' Learning Outcomes in Neonatal Resuscitation: A Quasi-Experimental Study Comparing AI-Assisted Care Plan Learning With Traditional Instruction.

AIM: The purpose of this study is to compare the efficacy of an artificial intelligence (AI)-based care plan learning strategy with standard training ...

Artificial Intelligence-Based Early Prediction of Acute Respiratory Failure in the Emergency Department Using Biosignal and Clinical Data.

PURPOSE: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing bi...

Scale to predict risk for refractory septic shock based on a hybrid approach using machine learning and regression modeling.

OBJECTIVE: To develop a scale to predict refractory septic shock (SS) based on clinical variables recorded during initial evaluations of patients.

Assessing Completeness of Clinical Histories Accompanying Imaging Orders Using Adapted Open-Source and Closed-Source Large Language Models.

Background Incomplete clinical histories are a well-known problem in radiology. Previous dedicated quality improvement efforts focusing on reproducibl...

Factors Contributing to Fatalities in Helicopter Emergency Medical Service Accidents.

INTRODUCTION: This study aimed to update and reinforce previous research on helicopter emergency medical service accidents in the United States. By in...