Emergency Medicine

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence.

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein ...

Regenerative rehabilitation measures to restore tissue function after arsenic exposure.

Environmental exposure of arsenic impairs the cardiometabolic profile, skeletal muscle health, and neurological function. Such declining tissue health is observed as early as in one's childhood, where the exposure is prevalent, thereby accelerating the effect of time's arrow. Despite the known deleterious effects of arsenic exposure, there is a paucity of specific treatment plans for restoring tis...

The future of burn management: How can machine learning lead to a revolution in improving the rehabilitation of burn patients?

Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm.

It is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department....

Investigating the Aryl Hydrocarbon Receptor Agonist/Antagonist Conformational Switch Using Well-Tempered Metadynamics Simulations.

The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that mediates biological signals to control various complicated cellula...

A machine learning algorithm-based predictive model for pressure injury risk in emergency patients: A prospective cohort study.

OBJECTIVES: To construct pressure injury risk prediction models for emergency patients based on different machine learning algorithms, to optimize the...

Design and testing of ultrasound probe adapters for a robotic imaging platform.

Medical imaging-based triage is a critical tool for emergency medicine in both civilian and military settings. Ultrasound imaging can be used to rapid...

Deep learning-based harmonization of trabecular bone microstructures between high- and low-resolution CT imaging.

BACKGROUND: Osteoporosis is a bone disease related to increased bone loss and fracture-risk. The variability in bone strength is partially explained b...

Comparison of an Ensemble of Machine Learning Models and the BERT Language Model for Analysis of Text Descriptions of Brain CT Reports to Determine the Presence of Intracranial Hemorrhage.

UNLABELLED: is to train and test an ensemble of machine learning models, as well as to compare its performance with the BERT language model pre-train...

Development of a deep learning model that predicts critical events of pediatric patients admitted to general wards.

Early detection of deteriorating patients is important to prevent life-threatening events and improve clinical outcomes. Efforts have been made to det...

GADNN: a revolutionary hybrid deep learning neural network for age and sex determination utilizing cone beam computed tomography images of maxillary and frontal sinuses.

INTRODUCTION: The determination of identity factors such as age and sex has gained significance in both criminal and civil cases. Paranasal sinuses li...

Biochemical implications of robotic surgery: a new frontier in the operating room.

Robotic surgery represents a milestone in surgical procedures, offering advantages such as less invasive methods, elimination of tremors, scaled motio...

Brain is also time: good short-term outcome predictions of artificial intelligence in spontaneous intracerebral hemorrhage pave the way for the long-term assessment.

Prediction of Hematoma Expansion in Intracerebral Hemorrhage in 24 Hours by Machine Learning Algorithm.

OBJECTIVE: The significance of noncontrast computer tomography (CT) image markers in predicting hematoma expansion (HE) following intracerebral hemorr...

Path tracking control of a steerable catheter in transcatheter cardiology interventions.

PURPOSE: Intracardiac transcatheter interventions allow for reducing trauma and hospitalization stays as compared to standard surgery. In the treatmen...

Real-time machine learning-assisted sepsis alert enhances the timeliness of antibiotic administration and diagnostic accuracy in emergency department patients with sepsis: a cluster-randomized trial.

Machine learning (ML) has been applied in sepsis recognition across different healthcare settings with outstanding diagnostic accuracy. However, the a...

Effect of robotic gait training on muscle and bone characteristics in spinal cord transected rats.

Osteoporosis and loss of muscle mass are secondary issues with spinal cord injury. Robotic gait training has provided evidence of increasing bone dens...

Machine learning prediction of footwear slip resistance on glycerol-contaminated surfaces: A pilot study.

Slippery surfaces due to oil spills pose a significant risk in various environments, including industrial workplaces, kitchens, garages, and outdoor a...

Accuracy of automated segmentation and volumetry of acute intracerebral hemorrhage following minimally invasive surgery using a patch-based convolutional neural network in a small dataset.

PURPOSE: In cases of acute intracerebral hemorrhage (ICH) volume estimation is of prognostic and therapeutic value following minimally invasive surger...

Deep Learning for Automated Detection and Localization of Traumatic Abdominal Solid Organ Injuries on CT Scans.

Computed tomography (CT) is the most commonly used diagnostic modality for blunt abdominal trauma (BAT), significantly influencing management approach...