Emergency Medicine

[Orthopaedic robot assisted closed reduction and cannulated screw internal fixation for the treatment of femoral neck fractures].

OBJECTIVE: To investigate the preliminary clinical effect of closed reduction and cannulated nail internal fixation for femoral neck fracture assisted by robot navigation and positioning system.

[Real-time traking and virtual reality technology-assisted trauma orthopaedic surgery robot for femoral neck fractures].

OBJECTIVE: To investigate the effectiveness of real-time tracking and virtual reality technology（RTVI） used to assist the intraoperative alignment of the trauma orthopaedic surgery robot for the treatment of femoral neck fractures and its impact on the treatment outcome.

Deep-Learning Uncovers certain CCM Isoforms as Transcription Factors.

BACKGROUND: Cerebral Cavernous Malformations (CCMs) are brain vascular abnormalities associated with an increased risk of hemorrhagic strokes. Familia...

Deep Learning for Midfacial Fracture Detection in CT Images.

This study deploys the deep learning-based object detection algorithms to detect midfacial fractures in computed tomography (CT) images. The object de...

Utilization of Machine Learning Approaches to Predict Mortality in Pediatric Warzone Casualties.

BACKGROUND: Identification of pediatric trauma patients at the highest risk for death may promote optimization of care. This becomes increasingly impo...

Research on workflow recognition for liver rupture repair surgery.

Liver rupture repair surgery serves as one tool to treat liver rupture, especially beneficial for cases of mild liver rupture hemorrhage. Liver ruptur...

A Pilot Report on Extracting Symptom Onset Date and Time from Clinical Notes in Patients Presenting with Chest Pain

Acute coronary syndrome (ACS) is an acute heart disease that often evolves rapidly. In ACS patients presenting with no-ST-segment elevation (NSTE-ACS)...

Improving Emergency Department Visit Risk Prediction: Exploring the Operational Utility of Applied Patient Portal Messages.

Patient portal messages represent a unique source of clinical data due to how they represent the voice of the patient, provide a glimpse into care del...

Better Blood Pressure Control for Stroke Patients in the ICU: A Deep Reinforcement Learning with Supervised Guidance Approach for Adaptive Infusion Rate Tuning.

Blood pressure variability (BPV) plays a critical role in vascular diseases, particularly in acute ischemic stroke patients in intensive care units (I...

Integrating Remote Patient Monitoring Data into Machine Learning Models for Predicting Emergency Department Utilization.

The integration of Remote Patient Monitoring (RPM) data into risk stratification models has emerged as a promising approach for improving healthcare d...

Prosthetically driven robot-assisted implant surgery: turning a major failure into a simple case.

This case report presents a challenging case of catastrophic failure of a fixed partial denture involving fracture of the prosthesis as well as suppor...

Applications of Artificial Intelligence and Machine Learning in Emergency Medicine Triage - A Systematic Review.

BACKGROUND: Overcrowding in Emergency departments adversely impacts efficiency, patient outcomes, and resource allocation. Accurate triage systems are...

FDA Review of Radiologic AI Algorithms: Process and Challenges.

A Food and Drug Administration (FDA)-cleared artificial intelligence (AI) algorithm misdiagnosed a finding as an intracranial hemorrhage in a patient,...

Alternative methods go green! Green toxicology as a sustainable approach for assessing chemical safety and designing safer chemicals.

Green toxicology is marching chemistry into the 21st century. This emerging framework will transform how chemical safety is evaluated by incorporating...

A CT-based Deep Learning Model for Predicting Subsequent Fracture Risk in Patients with Hip Fracture.

Background Patients have the highest risk of subsequent fractures in the first few years after an initial fracture, yet models to predict short-term s...

Examination-Level Supervision for Deep Learning-based Intracranial Hemorrhage Detection on Head CT Scans.

Purpose To compare the effectiveness of weak supervision (ie, with examination-level labels only) and strong supervision (ie, with image-level labels)...

Hemi-diaphragm detection of chest X-ray images based on convolutional neural network and graphics.

BACKGROUND: Chest X-rays (CXR) are widely used to facilitate the diagnosis and treatment of critically ill and emergency patients in clinical practice...

An Innovative Deep Learning Approach to Spinal Fracture Detection in CT Images.

AIM: Spinal fractures, particularly vertebral compression fractures, pose a significant challenge in medical imaging due to their small-scale nature a...

Advanced Machine Learning Models for Predicting Post-Thrombolysis Hemorrhagic Transformation in Acute Ischemic Stroke Patients: A Systematic Review and Meta-Analysis.

Thrombolytic therapy is essential for acute ischemic stroke (AIS) management but poses a risk of hemorrhagic transformation (HT), necessitating accur...

E-validation - Unleashing AI for validation.

The validation of new approach methods (NAMs) in toxicology faces significant challenges, including the integration of diverse data, selection of appr...