Emergency Medicine

Automatic ultrasound image alignment for diagnosis of pediatric distal forearm fractures.

PURPOSE: The study aims to develop an automatic method to align ultrasound images of the distal forearm for diagnosing pediatric fractures. This approach seeks to bypass the reliance on X-rays for fracture diagnosis, thereby minimizing radiation exposure and making the process less painful, as well as creating a more child-friendly diagnostic pathway.

External validation of an RSNA 2023 Abdominal Trauma AI Challenge high performing machine learning model in the detection and grading of splenic injuries on CT.

PURPOSE: This study aims to validate the performance of an award-winning machine learning (ML) model from the Radiological Society of North America (RSNA) 2023 Abdominal Trauma AI Challenge in detecting splenic injuries on CT scans using a large, geographically and temporally distinct external dataset.

Dual-Site Targeting by Peptide Inhibitors of the N-Terminal Domain of Hsp90: Mechanism and Design.

Heat shock protein 90 (Hsp90) is a pivotal molecular chaperone crucial in the maturation of client proteins, positioning it as a significant target fo...

Machine Learning for Predicting Critical Events Among Hospitalized Children.

IMPORTANCE: Unrecognized deterioration among hospitalized children is associated with a high risk of mortality and morbidity. The current approach to ...

Validation of an Artificial Intelligence-Powered Virtual Assistant for Emergency Triage in Neurology.

OBJECTIVES: Neurological emergencies pose significant challenges in medical care in resource-limited countries. Artificial intelligence (AI), particul...

AI-Based Virtual Triage and Telemedical Health Care for Ukrainian War Refugees and Displaced Persons.

Enhancing clinical decision support with physiological waveforms - A multimodal benchmark in emergency care.

BACKGROUND: AI-driven prediction algorithms have the potential to enhance emergency medicine by enabling rapid and accurate decision-making regarding ...

A machine learning tool for prediction of vertebral compression fracture following stereotactic body radiation therapy for spinal metastases.

BACKGROUND AND PURPOSE: The most common adverse event following spine stereotactic body radiotherapy (SBRT) is vertebral compression fracture (VCF). T...

Using artificial intelligence-based software for an unbiased discrimination of immune cell subtypes in the fracture hematoma and bone marrow of non-osteoporotic and osteoporotic mice.

It is well established that the early inflammatory response following fracture is essential for initiating subsequent bone regeneration. An imbalance ...

Predicting 30-day survival after in-hospital cardiac arrest: a nationwide cohort study using machine learning and SHAP analysis.

OBJECTIVE: In-hospital cardiac arrest (IHCA) presents a critical challenge with low survival rates and limited prediction tools. Despite advances in r...

Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression.

Identifying predictors of treatment response to repetitive transcranial magnetic stimulation (rTMS) remain elusive in treatment-resistant depression (...

Leveraging ChatGPT for improved decision-making in interfacility transfers for plastic surgery emergencies based on United States guidelines.

Plastic surgery emergencies-such as burns, facial trauma, and upper extremity injuries-often require timely specialist consultation. Yet, interfacilit...

A Novel Natural Language Processing Model for Triaging Head and Neck Patient Appointments.

OBJECTIVE: Inaccurate patient triage contributes to suboptimal clinical capacity management and delays in patient care, which in cancer patients may s...

Electrosurgical-laceration and stabilization of two PASCAL devices using artificial intelligence-based procedural planning: a case report.

BACKGROUND: Mitral transcatheter edge-to-edge repair (M-TEER) has been established as a treatment for severe mitral regurgitation (MR). However, recur...

Vision transformer and deep learning based weighted ensemble model for automated spine fracture type identification with GAN generated CT images.

The most common causes of spine fractures, or vertebral column fractures (VCF), are traumas like falls, injuries from sports, or accidents. CT scans a...

Machine learning for predicting medical outcomes associated with acute lithium poisoning.

The use of machine learning algorithms and artificial intelligence in medicine has attracted significant interest due to its ability to aid in predict...

Assessing artificial intelligence-generated patient discharge information for the emergency department: a pilot study.

BACKGROUND: Effective patient discharge information (PDI) in emergency departments (EDs) is vital and often more crucial than the diagnosis itself. Pa...

Deep learning modelling to forecast emergency department visits using calendar, meteorological, internet search data and stock market price.

BACKGROUND: Accurate prediction of hospital emergency department (ED) patient visits and acuity levels have potential to improve resource allocation i...

Ambulance route optimization in a mobile ambulance dispatch system using deep neural network (DNN).

The ambulance dispatch system plays a crucial role in emergency medical care by ensuring efficient communication, reducing response times, and ultimat...

Identification of pivotal genes and regulatory networks associated with SAH based on multi-omics analysis and machine learning.

Subarachnoid hemorrhage (SAH) is a disease with high mortality and morbidity, and its pathophysiology is complex but poorly understood. To investigate...