AIMC Topic: Craniocerebral Trauma
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Factors Associated with Abusive Head Trauma in Young Children Presenting to Emergency Medical Services Using a Large Language Model.
Prehospital emergency care
Jan 17, 2025
OBJECTIVES: Abusive head trauma (AHT) is a leading cause of death in young children. Analyses of patient characteristics presenting to Emergency Medical Services (EMS) are often limited to structured data fields. Artificial Intelligence (AI) and Larg...
Machine learning model to study the rugby head impact in a laboratory setting.
PloS one
Jan 6, 2025
The incidence of head impacts in rugby has been a growing concern for player safety. While rugby headgear shows potential to mitigate head impact intensity during laboratory simulations, evaluating its on-field effectiveness is challenging. Current r...
Artificial intelligence in paediatric head trauma: enhancing diagnostic accuracy for skull fractures and brain haemorrhages.
Neurosurgical review
Sep 19, 2024
Pediatric head trauma is a significant cause of morbidity and mortality, with children, particularly those under two years old, being more susceptible to skull fractures due to their unique physiological and developmental characteristics. A recent st...
Estimating helmet wearing rates via a scalable, low-cost algorithm: a novel integration of deep learning and google street view.
BMC public health
Jun 20, 2024
INTRODUCTION: Wearing a helmet reduces the risk of head injuries substantially in the event of a motorcycle crash. Countries around the world are committed to promoting helmet use, but the progress has been slow and uneven. There is an urgent need fo...
Development of a Deep Learning Model for Retinal Hemorrhage Detection on Head Computed Tomography in Young Children.
JAMA network open
Jun 1, 2023
IMPORTANCE: Abusive head trauma (AHT) in children is often missed in medical encounters, and retinal hemorrhage (RH) is considered strong evidence for AHT. Although head computed tomography (CT) is obtained routinely, all but exceptionally large RHs ...
The Current State of Susceptibility-Weighted Imaging and Quantitative Susceptibility Mapping in Head Trauma.
Neuroimaging clinics of North America
Feb 26, 2023
Susceptibility-weighted imaging (SWI) is a MR imaging technique suited to detect structural and microstructural abnormalities in traumatic brain injury (TBI). This review article provide an insight in to the physics principles of SWI and its clinical...
Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury.
Scientific reports
Jul 21, 2022
The study aims to measure the effectiveness of an AI-based traumatic intracranial hemorrhage prediction model in the decisions of emergency physicians regarding ordering head computed tomography (CT) scans. We developed a deep-learning model for pred...
Crash test-based assessment of injury risks for adults and children when colliding with personal mobility devices and service robots.
Scientific reports
Mar 28, 2022
Autonomous mobility devices such as transport, cleaning, and delivery robots, hold a massive economic and social benefit. However, their deployment should not endanger bystanders, particularly vulnerable populations such as children and older adults ...
Effect of pedestrian physique differences on head injury prediction in car-to-pedestrian accidents using deep learning.
Traffic injury prevention
Oct 26, 2021
OBJECTIVE: The aim of this study is to identify the effects of pedestrian physique differences on head injury prediction in car-to-pedestrian accidents via deep learning.
Application of a deep learning image reconstruction (DLIR) algorithm in head CT imaging for children to improve image quality and lesion detection.
BMC medical imaging
Jul 8, 2021
BACKGROUND: To evaluate the performance of a Deep Learning Image Reconstruction (DLIR) algorithm in pediatric head CT for improving image quality and lesion detection with 0.625 mm thin-slice images.
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