Emergency Medicine

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 department (ED) diagnoses are a useful source of information for injury epidemiological surveillance purposes. However, ED data collection systems often use free-text fields to report patient diagnoses. Machine learning techniques (MLTs) are powerful tools for automatic text classification. The MLT system i...

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 platelets to each other and to endothelial cells. In traumatic and surgical haemorrhage, both blood cells and plasmatic factors are consumed, leading to consumption coagulopathy and fluid resuscitation. This often results in large amounts of crystalloids and blood products being infused. Additional adm...

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 specific findings becoming increasingly prevalent a...

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 local structural environments dictate performance ...

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 jeopardize their health and put patient safety at risk. I...

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 disrupt the function of these organelles can lead to ...

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 no proven medical treatments that can improve the...

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 Department (ED). To address this aim, we developed ...

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 pressure. Society is questioning overall performan...

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 chart review in a clinical setting when assisted by a...

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 turbo spin echo (TSEDL) sequences relative to standar...

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 detection of incomplete fractures and the prevention ...

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 investigation and recognition by physicians. Computed tom...

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 assess the ability of an artificial intelligence tria...

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 fracture area for proper treatment planning. This syste...

Pulmonary contusion: automated deep learning-based quantitative visualization.

PURPOSE: Rapid automated CT volumetry of pulmonary contusion may predict progression to Acute Respiratory Distress Syndrome (ARDS) and help guide earl...

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. Unfortunately, gathering new toxicological information...

Artificial intelligence versus surgeon gestalt in predicting risk of emergency general surgery.

BACKGROUND: Artificial intelligence (AI) risk prediction algorithms such as the smartphone-available Predictive OpTimal Trees in Emergency Surgery Ris...

CT Angiography Radiomics Combining Traditional Risk Factors to Predict Brain Arteriovenous Malformation Rupture: a Machine Learning, Multicenter Study.

This study aimed to develop a machine learning model for predicting brain arteriovenous malformation (bAVM) rupture using a combination of traditional...