Latest AI and machine learning research in emergency medicine for healthcare professionals.
Nowhere is the influence of artificial intelligence (AI) likely to be more profoundly felt than in health care, from patient triage and diagnosis to surgery and follow-up. Over the medium-term, these effects will be more acute in the cardiovascular imaging context, in which AI models are already successfully performing at approximately human levels of accuracy and efficiency in certain application...
Cardiovascular and chronic respiratory diseases are global threats to public health and cause approximately 19 million deaths worldwide annually. This high mortality rate can be reduced with the use of technological advancements in medical science that can facilitate continuous monitoring of physiological parameters-blood pressure, cholesterol levels, blood glucose, etc. The futuristic values of t...
Coronavirus disease 2019 revolutionized the digital health care. This pandemic was the catalyst for not only a sudden but also widespread paradigm shi...
Identification of burn depth with sufficient accuracy is a challenging problem. This paper presents a deep convolutional neural network to classify bu...
BACKGROUND: This pilot study investigated plasma concentrations of hyaluronan, heparan sulfate, and syndecan-1 as possible biomarkers for glycocalyx i...
High-throughput chemical screening approaches often employ microscopy to capture photomicrographs from multi-well cell culture plates, generating thou...
BACKGROUND: Machine learning (ML) is an emerging tool for predicting need of end-of-life discussion and palliative care, by using mortality as a proxy...
INTRODUCTION: Patients undergoing laparotomy for emergency general surgery (EGS) conditions, constitute a high-risk group with poor outcomes. These pa...
 The objective of the study was to review the obstetric outcomes of complete hydatidiform molar pregnancies with a coexisting fetus (CHMCF), a rare c...
With the increase in Internet of Things (IoT) devices and network communications, but with less bandwidth growth, the resulting constraints must be ov...
Autonomous intelligent systems are starting to influence clinical practice, as ways to both readily exploit experts' knowledge when contextual conditi...
An adequate classification of proximal femur fractures from X-ray images is crucial for the treatment choice and the patients' clinical outcome. We re...
BACKGROUND: Sepsis-associated thrombocytopenia (SAT) is common in critical patients and results in the elevation of mortality. Red cell distribution w...
In this unprecedented era of the overwhelming volume of medical data, machine learning can be a promising tool that may shed light on an individualize...
Digital pathology has recently been more broadly deployed, fueling artificial intelligence (AI) application development and more systematic use of ima...
Background Advances in computer processing and improvements in data availability have led to the development of machine learning (ML) techniques for m...
An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validated ...
BACKGROUND: Approximately one-third of spontaneous intracerebral hemorrhage patients did not know the onset time and were excluded from studies about ...
Various intelligent technologies have been applied during COVID-19, which has become a worldwide public health emergency and brought significant chall...
High precision is optimal in prehospital diagnostic algorithms for strokes and large vessel occlusions. We hypothesized that prehospital diagnostic al...