Latest AI and machine learning research in emergency medicine for healthcare professionals.
PURPOSE: The study explored the clinical influence, effectiveness, limitations, and human comparison outcomes of machine learning in diagnosing (1) dental diseases, (2) periodontal diseases, (3) trauma and neuralgias, (4) cysts and tumors, (5) glandular disorders, and (6) bone and temporomandibular joint as possible causes of dental and orofacial pain.
Acute gastrointestinal bleeding is the most common gastrointestinal cause for hospitalization. For high-risk patients requiring intensive care unit stay, predicting transfusion needs during the first 24 h using dynamic risk assessment may improve resuscitation with red blood cell transfusion in admitted patients with severe acute gastrointestinal bleeding. A patient cohort admitted for acute gastr...
BACKGROUND: Artificial intelligence (AI) applications are growing at an unprecedented pace in health care, including disease diagnosis, triage or scre...
Numerical simulation of wildland fire spread is useful to predict the locations that are likely to burn and to support decision in an operational cont...
In sub-Saharan Africa, there is a significant unmet need for emergency care, with a shortage of trained providers. One model to increase the number of...
We investigated the potential of machine learning techniques, at an early stage after stroke, to predict the need for ankle-foot orthosis (AFO) in str...
Artificial intelligence (AI) is a branch of Informatics that uses algorithms to tirelessly process data, understand its meaning and provide the desire...
Deep learning methods have been shown to achieve excellent performance on diagnostic tasks, but how to optimally combine them with expert knowledge an...
Suction during robotic surgery has traditionally been performed by a bedside assistant. Adequately skilled assistants are not always available. We as...
BACKGROUND AND PURPOSE: Randomized controlled trials have demonstrated the importance of time to endovascular therapy (EVT) in clinical outcomes in la...
For decades, it has been postulated that digital pathology is the future. By now it is safe to say that we are living that future. Digital pathology h...
PURPOSE: To evaluate whether a deep learning model (DLM) could increase the detection sensitivity of radiologists for intracranial aneurysms on CT ang...
Electron microscopy (EM) enables high-resolution visualization of protein distributions in biological tissues. For detection, gold nanoparticles are t...
Burn is a common traumatic disease with high morbidity and mortality. The treatment of burns requires accurate and reliable diagnosis of burn wounds a...
Fall detection is a major challenge for researchers. Indeed, a fall can cause injuries such as femoral neck fracture, brain hemorrhage, or skin burns,...
OBJECTIVE: To determine the institutional diagnostic accuracy of an artificial intelligence (AI) decision support systems (DSS), Aidoc, in diagnosing ...
The current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to...
In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.g. the order of an event relativ...
BACKGROUND: Fractures around the knee joint are inherently complex in terms of treatment; complication rates are high, and they are difficult to diagn...
Novel photoactive and enzymatically active nanomotors were developed for efficient organic pollutant degradation. The developed preparation route is s...