Emergency Medicine

Predicting chemical ecotoxicity by learning latent space chemical representations.

In silico prediction of chemical ecotoxicity (HC) represents an important complement to improve in vivo and in vitro toxicological assessment of manufactured chemicals. Recent application of machine learning models to predict chemical HC yields variable prediction performance that depends on effectively learning chemical representations from high-dimension data. To improve HC prediction performanc...

Prior information-based high-resolution tomography image reconstruction from a single digitally reconstructed radiograph.

Tomography images are essential for clinical diagnosis and trauma surgery, allowing doctors to understand the internal information of patients in more detail. Since the large amount of x-ray radiation from the continuous imaging during the process of computed tomography scanning can cause serious harm to the human body, reconstructing tomographic images from sparse views becomes a potential soluti...

When giants talk; robotic dialog during thoracolumbar and sacral surgery.

BACKGROUND: Spinal trauma patients treated in a specialized hybrid operating room (OR) using two robotic systems communicating during surgery.

Robotically applied hemostatic clamping for care-under-fire: harnessing bomb robots for hemorrhage control.

BACKGROUND: Early hemorrhage control after interpersonal violence is the most urgent requirement to preserve life and is now recognized as a responsib...

Deep Gaussian processes for multiple instance learning: Application to CT intracranial hemorrhage detection.

BACKGROUND AND OBJECTIVE: Intracranial hemorrhage (ICH) is a life-threatening emergency that can lead to brain damage or death, with high rates of mor...

AI-Enabled Advanced Development for Assessing Low Circulating Blood Volume for Emergency Medical Care: Comparison of Compensatory Reserve Machine-Learning Algorithms.

The application of artificial intelligence (AI) has provided new capabilities to develop advanced medical monitoring sensors for detection of clinical...

Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis.

Background Patients with fractures are a common emergency presentation and may be misdiagnosed at radiologic imaging. An increasing number of studies ...

Crash test-based assessment of injury risks for adults and children when colliding with personal mobility devices and service robots.

Autonomous mobility devices such as transport, cleaning, and delivery robots, hold a massive economic and social benefit. However, their deployment sh...

Single port robotic assisted sacrocolpopexy: technique and tips.

INTRODUCTION AND HYPOTHESIS: Sacrocolpopexy is the most durable surgical procedure for the treatment of symptomatic pelvic organ prolapse (Maher et al...

Deep Neural Networks Can Accurately Detect Blood Loss and Hemorrhage Control Task Success From Video.

BACKGROUND: Deep neural networks (DNNs) have not been proven to detect blood loss (BL) or predict surgeon performance from video.

Diagnostic accuracy of a commercially available deep-learning algorithm in supine chest radiographs following trauma.

OBJECTIVES: Trauma chest radiographs may contain subtle and time-critical pathology. Artificial intelligence (AI) may aid in accurate reporting, timel...

A deep learning-based approach to automatic proximal femur segmentation in quantitative CT images.

Automatic CT segmentation of proximal femur has a great potential for use in orthopedic diseases, especially in the imaging-based assessments of hip f...

Research on Lung Ultrasound Image Classification Based on Compressed Sensing.

Pneumothorax is a common injury in disaster rescue, traffic accidents, and war trauma environments and requires early diagnosis and treatment. The com...

What Influences the Way Radiologists Express Themselves in Their Reports? A Quantitative Assessment Using Natural Language Processing.

Although using standardized reports is encouraged, most emergency radiological reports in France remain in free-text format that can be mined with nat...

Data-driven discovery of Green's functions with human-understandable deep learning.

There is an opportunity for deep learning to revolutionize science and technology by revealing its findings in a human interpretable manner. To do thi...

Artificial Intelligence in Infection Management in the ICU.

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2022. Other selected articles can be found...

Artificial Intelligence in Critical Care Medicine.

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2022. Other selected articles can be found...

Evaluation of Nano-curcumin effects against Tartrazine-induced abnormalities in liver and kidney histology and other biochemical parameters.

In the current study, 40 albino male rats were investigated to evaluate the impact of Nano-curcumin (Nano-CUR) administration against Tartrazine (TZ)-...

Artificial Intelligence-Enabled Medical Analysis for Intracranial Cerebral Hemorrhage Detection and Classification.

Intracranial hemorrhage (ICH) becomes a crucial healthcare emergency, which requires earlier detection and accurate assessment. Owing to the increased...

Prediction Value of Epilepsy Secondary to Inferior Cavity Hemorrhage Based on Scalp EEG Wave Pattern in Deep Learning.

OBJECTIVE: To search the predictive value of epilepsy secondary to acute subarachnoid hemorrhage (aSAH) based on EEG wave pattern in deep learning.