Emergency Medicine

Adrenal Insufficiency in Septic Patients Admitted to Intensive Care Unit: Prevalence and Associated Factors.

Adrenal insufficiency (AI) is associated with an increase in the risk of mortality in ICU-admitted septic patients. It should be suspected not only in patients with septic shock but also in those with sepsis. The aim of this study was to investigate the prevalence of AI in the spectrum of septic patients and determine the main predictors of this condition. This study included 99 patients with th...

Lactate levels as a prognostic predict in cardiogenic shock under venoarterial extracorporeal membrane oxygenation support.

INTRODUCTION AND OBJECTIVES: Lactate and its evolution are associated with the prognosis of patients in shock, although there is little evidence in those assisted with an extracorporeal venoarterial oxygenation membrane (VA-ECMO). Our objective was to evaluate its prognostic value in cardiogenic shock assisted with VA-ECMO.

External validation of a commercially available deep learning algorithm for fracture detection in children.

PURPOSE: The purpose of this study was to conduct an external validation of a fracture assessment deep learning algorithm (Rayvolve®) using digital ra...

Machine learning approach for the prediction of postpartum hemorrhage in vaginal birth.

Postpartum hemorrhage is the leading cause of maternal morbidity. Clinical prediction of postpartum hemorrhage remains challenging, particularly in th...

New Models to Predict the Acute and Chronic Toxicities of Representative Species of the Main Trophic Levels of Aquatic Environments.

To assess the impact of chemicals on an aquatic environment, toxicological data for three trophic levels are needed to address the chronic and acute t...

Deep Learning-Based Diagnosis Method of Emergency Colorectal Pathology.

One of the most common malignant tumors of the digestive tract is emergency colorectal cancer. In recent years, both morbidity and mortality rates, pa...

Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality.

This paper demonstrates how the combustion of fossil fuels for transport purpose might cause health implications. Based on an original case study [i.e...

Evaluation of the Postoperative Nursing Effect of Thoracic Surgery Assisted by Artificial Intelligence Robot.

In order to evaluate the postoperative nursing effect of artificial intelligence robot-assisted thoracic surgery, this study proposed the Da Vinci rob...

First-in-Human Robot-Assisted Subretinal Drug Delivery Under Local Anesthesia.

PURPOSE: To report the results of a first-in-human study using a robotic device to assist subretinal drug delivery in patients undergoing vitreoretina...

Automated detection and segmentation of intracranial hemorrhage suspect hyperdensities in non-contrast-enhanced CT scans of acute stroke patients.

OBJECTIVES: Artif icial intelligence (AI)-based image analysis is increasingly applied in the acute stroke field. Its implementation for the detection...

[Reflections on the state of the art].

If the progress made so far allows to save more and more lives, the resuscitation of 2021 is still a young medical speciality and in full evolution. M...

[Development of severity and mortality prediction models for covid-19 patients at emergency department including the chest x-ray].

OBJECTIVES: To develop prognosis prediction models for COVID-19 patients attending an emergency department (ED) based on initial chest X-ray (CXR), de...

Classification of target tissues of Eisenia fetida using sequential multimodal chemical analysis and machine learning.

Acquiring comprehensive knowledge about the uptake of pollutants, impact on tissue integrity and the effects at the molecular level in organisms is of...

Effect of deep learning-based assistive technology use on chest radiograph interpretation by emergency department physicians: a prospective interventional simulation-based study.

BACKGROUND: Interpretation of chest radiographs (CRs) by emergency department (ED) physicians is inferior to that by radiologists. Recent studies have...

Development and Validation of a Deep Learning Strategy for Automated View Classification of Pediatric Focused Assessment With Sonography for Trauma.

OBJECTIVE: Pediatric focused assessment with sonography for trauma (FAST) is a sequence of ultrasound views rapidly performed by clinicians to diagnos...

Machine learning versus traditional methods for the development of risk stratification scores: a case study using original Canadian Syncope Risk Score data.

Artificial Intelligence and machine learning (ML) methods are promising for risk-stratification, but the added benefit over traditional statistical me...

Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach.

There is increasing evidence that patient heterogeneity significantly hinders advancement in clinical trials and individualized care. This study aimed...

DICOM Image ANalysis and Archive (DIANA): an Open-Source System for Clinical AI Applications.

In the era of data-driven medicine, rapid access and accurate interpretation of medical images are becoming increasingly important. The DICOM Image AN...

Machine learning techniques for mortality prediction in emergency departments: a systematic review.

OBJECTIVES: This systematic review aimed to assess the performance and clinical feasibility of machine learning (ML) algorithms in prediction of in-ho...

Using explainable machine learning to identify patients at risk of reattendance at discharge from emergency departments.

Short-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance co...