AIMC Topic: Skull Fractures

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Deep Learning-Assisted Diagnosis of Pediatric Skull Fractures on Plain Radiographs.

Korean journal of radiology
OBJECTIVE: To develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children.

Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study.

Lancet (London, England)
BACKGROUND: Non-contrast head CT scan is the current standard for initial imaging of patients with head trauma or stroke symptoms. We aimed to develop and validate a set of deep learning algorithms for automated detection of the following key finding...

Classification of Porcine Cranial Fracture Patterns Using a Fracture Printing Interface.

Journal of forensic sciences
Distinguishing between accidental and abusive head trauma in children can be difficult, as there is a lack of baseline data for pediatric cranial fracture patterns. A porcine head model has recently been developed and utilized in a series of studies ...

Predictive modeling in pediatric traumatic brain injury using machine learning.

BMC medical research methodology
BACKGROUND: Pediatric traumatic brain injury (TBI) constitutes a significant burden and diagnostic challenge in the emergency department (ED). While large North American research networks have derived clinical prediction rules for the head injured ch...

Intelligent Recognition and Segmentation of Blunt Craniocerebral Injury CT Images Based on DeepLabV3+ Model.

Fa yi xue za zhi
OBJECTIVES: To achieve intelligent recognition and segmentation of common craniocerebral injuries (hereinafter referred to as "segmentation") by training convolutional neural network DeepLabV3+ model based on CT images of blunt craniocerebral injury ...