Emergency Medicine

Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification.

OBJECTIVES: During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators in order to monitor both the epidemic growth and potential public health consequences of preventative measures such as lockdown. We assessed whether the automatic classification of the content of calls to emergency medical communication centers could provide relevant and res...

Microscope-Based Automated Quantification of Liver Fibrosis in Mice Using a Deep Learning Algorithm.

In preclinical studies that involve animal models for hepatic fibrosis, accurate quantification of the fibrosis is of utmost importance. The use of digital image analysis based on deep learning artificial intelligence (AI) algorithms can facilitate accurate evaluation of liver fibrosis in these models. In the present study, we compared the quantitative evaluation of collagen proportionate area in ...

Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks.

With the rapid growth and increasing use of brain MRI, there is an interest in automated image classification to aid human interpretation and improve ...

Performance of an artificial intelligence tool with real-time clinical workflow integration - Detection of intracranial hemorrhage and pulmonary embolism.

Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case-control and prospective cohort study.

BACKGROUND: Lung auscultation is fundamental to the clinical diagnosis of respiratory disease. However, auscultation is a subjective practice and inte...

Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach.

Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algo...

Critical evaluation of deep neural networks for wrist fracture detection.

Wrist Fracture is the most common type of fracture with a high incidence rate. Conventional radiography (i.e. X-ray imaging) is used for wrist fractur...

Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients.

Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection a...

Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach.

OBJECTIVE: Hemorrhagic fever with renal syndrome (HFRS), one of the main public health concerns in mainland China, is a group of clinically similar di...

GGL-Tox: Geometric Graph Learning for Toxicity Prediction.

Toxicity analysis is a major challenge in drug design and discovery. Recently significant progress has been made through machine learning due to its a...

Using artificial intelligence to diagnose fresh osteoporotic vertebral fractures on magnetic resonance images.

BACKGROUND CONTEXT: Accurate diagnosis of osteoporotic vertebral fracture (OVF) is important for improving treatment outcomes; however, the gold stand...

Development status of telesurgery robotic system.

As an emerging field, telesurgery robotic system is changing the traditional medical mode and can delivery remote surgical treatment anywhere in the w...

Artificial Intelligence-Aided Precision Medicine for COVID-19: Strategic Areas of Research and Development.

Artificial intelligence (AI) technologies can play a key role in preventing, detecting, and monitoring epidemics. In this paper, we provide an overvie...

Automated remote decision-making algorithm as a primary triage system using machine learning techniques.

OBJECTIVE: An objective and convenient primary triage procedure is needed for prioritizing patients who need help in mass casualty incident (MCI) situ...

AI support for ethical decision-making around resuscitation: proceed with care.

Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven...

Predicting opioid overdose risk of patients with opioid prescriptions using electronic health records based on temporal deep learning.

The US is experiencing an opioid epidemic, and opioid overdose is causing more than 100 deaths per day. Early identification of patients at high risk ...

Validation of the Artificial Intelligence-Based Predictive Optimal Trees in Emergency Surgery Risk (POTTER) Calculator in Emergency General Surgery and Emergency Laparotomy Patients.

BACKGROUND: The Predictive Optimal Trees in Emergency Surgery Risk (POTTER) tool is an artificial intelligence-based calculator for the prediction of ...

Key factors in a rigorous longitudinal image-based assessment of retinopathy of prematurity.

To describe a database of longitudinally graded telemedicine retinal images to be used as a comparator for future studies assessing grader recall bias...

Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial Hemorrhage.

BACKGROUND: Immediate and accurate detection of intracranial hemorrhages (ICHs) is essential to provide a good clinical outcome for patients with ICH....

Deep Neural Network Approach for Continuous ECG-Based Automated External Defibrillator Shock Advisory System During Cardiopulmonary Resuscitation.

Background Because chest compressions induce artifacts in the ECG, current automated external defibrillators instruct the user to stop cardiopulmonary...