Emergency Medicine

Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19.

To tune and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung di...

Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury.

The study aims to measure the effectiveness of an AI-based traumatic intracranial hemorrhage prediction model in the decisions of emergency physicians regarding ordering head computed tomography (CT) scans. We developed a deep-learning model for predicting traumatic intracranial hemorrhages (DEEPTICH) using a national trauma registry with 1.8 million cases. For simulation, 24 cases were selected f...

Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning.

BACKGROUND: Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department wit...

Deep Learning Applications for Acute Stroke Management.

Brain imaging is essential to the clinical care of patients with stroke, a leading cause of disability and death worldwide. Whereas advanced neuroimag...

Simplified Transfer Learning for Chest Radiography Models Using Less Data.

Background Developing deep learning models for radiology requires large data sets and substantial computational resources. Data set size limitations c...

Application of multiple deep learning models for automatic burn wound assessment.

PURPOSE: Accurate assessment of the percentage of total body surface area (%TBSA) burned is crucial in managing burn injuries. It is difficult to esti...

A deep learning-based automatic system for intracranial aneurysms diagnosis on three-dimensional digital subtraction angiographic images.

BACKGROUND: Intracranial aneurysms (IAs) are a life-threatening disease. Their rupture can lead to hemorrhagic stroke. Most studies applying deep lear...

Machine Learning in the Prediction of Trauma Outcomes: A Systematic Review.

STUDY OBJECTIVE: Machine learning models carry unique potential as decision-making aids and prediction tools for improving patient care. Traumatically...

Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System.

BACKGROUND: With diabetes incidence growing globally and metformin still being the first-line for its treatment, metformin's toxicity and overdose hav...

Using natural language processing to identify acute care patients who lack advance directives, decisional capacity, and surrogate decision makers.

The prevalence of patients who are Incapacitated with No Evident Advance Directives or Surrogates (INEADS) remains unknown because such data are not r...

Research on emergency management of urban waterlogging based on similarity fusion of multi-source heterogeneous data.

Global warming has seriously affected the local climate characteristics of cities, resulting in the frequent occurrence of urban waterlogging with sev...

Editorial Comment: Progress Update on Acute Fracture Diagnosis With Artificial Intelligence.

Deep Learning for FAST Quality Assessment.

OBJECTIVES: To determine the feasibility of using a deep learning (DL) algorithm to assess the quality of focused assessment with sonography in trauma...

Added value of an artificial intelligence solution for fracture detection in the radiologist's daily trauma emergencies workflow.

PURPOSE: The main objective of this study was to compare radiologists' performance without and with artificial intelligence (AI) assistance for the de...

Intelligent Research Based on Deep Learning Recognition Method in Vehicle-Road Cooperative Information Interaction System.

The vehicle-road collaborative information interaction system is an emerging technology system that realizes the sharing of information between vehicl...

Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives.

Over the past few decades, data-driven machine learning (ML) has distinguished itself from hypothesis-driven studies and has recently received much at...

DeepStroke: An efficient stroke screening framework for emergency rooms with multimodal adversarial deep learning.

In an emergency room (ER) setting, stroke triage or screening is a common challenge. A quick CT is usually done instead of MRI due to MRI's slow throu...

Lower-extremity fatigue fracture detection and grading based on deep learning models of radiographs.

OBJECTIVES: To identify the feasibility of deep learning-based diagnostic models for detecting and assessing lower-extremity fatigue fracture severity...

A study on a vehicle semi-active suspension control system based on road elevation identification.

A semi-active suspension system can effectively improve vehicle ride comfort and handling stability, and the active detection of road information is k...

Application of Recurrent Neural Network Algorithm in Intelligent Detection of Clinical Ultrasound Images of Human Lungs.

Lung ultrasound has great application value in the differential diagnosis of pulmonary exudative lesions. It has good sensitivity and specificity for ...