Emergency Medicine

Deep-Learning-Enabled Microwave-Induced Thermoacoustic Tomography Based on ResAttU-Net for Transcranial Brain Hemorrhage Detection.

OBJECTIVE: Hemorrhagic stroke is a leading threat to human's health. The fast-developing microwave-i...

Identifying Differences in the Performance of Machine Learning Models for Off-Targets Trained on Publicly Available and Proprietary Data Sets.

Each year, publicly available databases are updated with new compounds from different research insti...

ChatGPT-4: dermatology aid in the emergency room.

Pediatric Injury Surveillance From Uncoded Emergency Department Admission Records in Italy: Machine Learning-Based Text-Mining Approach.

BACKGROUND: Unintentional injury is the leading cause of death in young children. Emergency departme...

Impact of Additional Administration of von Willebrand Factor Concentrates to Thrombocyte Transfusion in Perioperative Bleeding in Cardiac Surgery.

BACKGROUND: Von Willebrand factor (vWF) is an important part of blood coagulation since it binds pla...

AI tools in Emergency Radiology reading room: a new era of Radiology.

Artificial intelligence tools in radiology practices have surged, with modules developed to target s...

Applying a Smartwatch to Predict Work-related Fatigue for Emergency Healthcare Professionals: Machine Learning Method.

INTRODUCTION: Healthcare professionals frequently experience work-related fatigue, which may jeopard...

Quantifying disorder one atom at a time using an interpretable graph neural network paradigm.

Quantifying the level of atomic disorder within materials is critical to understanding how evolving ...

Predicting the Mitochondrial Toxicity of Small Molecules: Insights from Mechanistic Assays and Cell Painting Data.

Mitochondrial toxicity is a significant concern in the drug discovery process, as compounds that dis...

Surgical Robotics for Intracerebral Hemorrhage Treatment: State of the Art and Future Directions.

Intracerebral hemorrhage (ICH) is a stroke subtype with high mortality and disability, and there are...

Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study.

Predicting clinical deterioration in COVID-19 patients remains a challenging task in the Emergency D...

New approach methodologies in human regulatory toxicology - Not if, but how and when!

The predominantly animal-centric approach of chemical safety assessment has increasingly come under ...

Doctors Identify Hemorrhage Better during Chart Review when Assisted by Artificial Intelligence.

OBJECTIVES: This study evaluated if medical doctors could identify more hemorrhage events during cha...

In Reply: Deep Neural Networks Can Accurately Detect Blood Loss and Hemorrhage Control Task Success from Video.

Feasibility and clinical usefulness of deep learning-accelerated MRI for acute painful fracture patients wearing a splint: A prospective comparative study.

OBJECTIVE: To evaluate the feasibility and clinical usefulness of deep learning (DL)-accelerated tur...

Detection of incomplete atypical femoral fracture on anteroposterior radiographs via explainable artificial intelligence.

One of the key aspects of the diagnosis and treatment of atypical femoral fractures is the early det...

Computerised emergency work-up for mass-like brain MRI lesions: can explainable AI support radiologists?

A comparison of performance between a deep learning model with residents for localization and classification of intracranial hemorrhage.

Intracranial hemorrhage (ICH) from traumatic brain injury (TBI) requires prompt radiological investi...

APPRAISE-HRI: AN ARTIFICIAL INTELLIGENCE ALGORITHM FOR TRIAGE OF HEMORRHAGE CASUALTIES.

Background: Hemorrhage remains the leading cause of death on the battlefield. This study aims to ass...

Evaluating the Accuracy and Reliability of Blowout Fracture Area Measurement Methods: A Review and the Potential Role of Artificial Intelligence.

Blowout fractures are a common type of facial injury that requires accurate measurement of the fract...

The Bigger Fish: A Comparison of Meta-Learning QSAR Models on Low-Resourced Aquatic Toxicity Regression Tasks.

Toxicological information as needed for risk assessments of chemical compounds is often sparse. Unfo...