Emergency Medicine

Combat medic testing of a novel monitoring capability for early detection of hemorrhage.

BACKGROUND: Current out-of-hospital protocols to determine hemorrhagic shock in civilian trauma systems rely on standard vital signs with military guidelines relying on heart rate and strength of the radial pulse on palpation, all of which have proven to provide little forewarning for the need to implement early intervention prior to decompensation. We tested the hypothesis that addition of a real...

Assessment of a Deep Learning Algorithm for the Detection of Rib Fractures on Whole-Body Trauma Computed Tomography.

OBJECTIVE: To assess the diagnostic performance of a deep learning-based algorithm for automated detection of acute and chronic rib fractures on whole-body trauma CT.

Risk assessment for intra-abdominal injury following blunt trauma in children: Derivation and validation of a machine learning model.

BACKGROUND: Computed tomography is the criterion standard for diagnosing intra-abdominal injury (IAI) but is expensive and risks radiation exposure. T...

Emergency Clinical Procedure Detection With Deep Learning.

Information about a patient's state is critical for hospitals to provide timely care and treatment. Prior work on improving the information flow from ...

Potential Prognostic Markers in the Heart Rate Variability Features for Early Diagnosis of Sepsis in the Pediatric Intensive Care Unit using Convolutional Neural Network Classifiers.

Blood infection due to different circumstances could immediately develop to an extreme body reaction that leads to a serious life-threatening conditio...

A Two Cascaded Network Integrating Regional-based YOLO and 3D-CNN for Cerebral Microbleeds Detection.

Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular dis...

Deep Learning for Neuroimaging Segmentation with a Novel Data Augmentation Strategy.

Brain insults such as cerebral ischemia and intracranial hemorrhage are critical stroke conditions with high mortality rates. Currently, medical image...

Automated Assessment System for Neonatal Endotracheal Intubation Using Dilated Convolutional Neural Network.

Neonatal endotracheal intubation (ETI) is an important, complex resuscitation skill, which requires a significant amount of practice to master. Curren...

Potential Prognostic Markers in the Heart Rate Variability Features for Early Diagnosis of Sepsis in the Pediatric Intensive Care Unit using Convolutional Neural Network Classifiers.

Blood infection due to different circumstances could immediately develop to an extreme body reaction that leads to a serious life-threatening conditio...

Classification of Intracranial Hemorrhage Subtypes Using Deep Learning on CT Scans.

Intracranial hemorrhage is a pathological condition that requires fast diagnosis and decision making. Recently, a neural network model for classificat...

Mobile Robotic Telepresence Between Hospital and School: Lessons Learned.

If a pupil falls seriously ill, it is not only a shock for the pupil himself or herself, but also for his or her family and classmates. The project "V...

Investigating the Barriers to Physician Adoption of an Artificial Intelligence- Based Decision Support System in Emergency Care: An Interpretative Qualitative Study.

The development of artificial intelligence (AI) systems to support diagnostic decision-making is rapidly expanding in health care. However, important ...

Natural language processing of prehospital emergency medical services trauma records allows for automated characterization of treatment appropriateness.

BACKGROUND: Incomplete prehospital trauma care is a significant contributor to preventable deaths. Current databases lack timelines easily constructib...

Machine Learning Prediction of Postoperative Emergency Department Hospital Readmission.

BACKGROUND: Although prediction of hospital readmissions has been studied in medical patients, it has received relatively little attention in surgical...

Limited evidence of benefits of patient operated intelligent primary care triage tools: findings of a literature review.

INTRODUCTION: There is consistent evidence that the workload in general practices is substantially increasing. The digitalisation of healthcare includ...

[Advances in the research of artificial intelligence technology assisting the diagnosis of burn depth].

The early accurate diagnosis of burn depth is of great significance in determining the corresponding clinical intervention methods and judging the pro...

Acceptability and Perceived Utility of Telemedical Consultation during Cardiac Arrest Resuscitation. A Multicenter Survey.

Many clinicians who participate in or lead in-hospital cardiac arrest (IHCA) resuscitations lack confidence for this task or worry about errors. Well...

[Detection of inferior myocardial infarction based on densely connected convolutional neural network].

Inferior myocardial infarction is an acute ischemic heart disease with high mortality, which is easy to induce life-threatening complications such as ...

Performance of a Deep Learning Algorithm for Automated Segmentation and Quantification of Traumatic Pelvic Hematomas on CT.

The volume of pelvic hematoma at CT has been shown to be the strongest independent predictor of major arterial injury requiring angioembolization in t...

Using a Dual-Input Convolutional Neural Network for Automated Detection of Pediatric Supracondylar Fracture on Conventional Radiography.

OBJECTIVES: This study aimed to develop a dual-input convolutional neural network (CNN)-based deep-learning algorithm that utilizes both anteroposteri...