Abstract Background AI/ML CAD tools can potentially improve outcomes in the high-stakes,
high-volume model of trauma radiology. No prior scoping review has been undertaken to …

Penetrating abdominal trauma comprises a wide variety of injuries that will manifest
themselves at imaging depending on the distinct mechanism of injury. The use of computed …

Abstract Purpose We employ nnU-Net, a state-of-the-art self-configuring deep learning-
based semantic segmentation method for quantitative visualization of hemothorax (HTX) in …

Purpose To evaluate the feasibility of a multiscale deep learning algorithm for quantitative
visualization and measurement of traumatic hemoperitoneum and to compare diagnostic …

Introduction Blast injury is a unique condition that carries a high rate of morbidity and
mortality, often with mixed penetrating and blunt injuries. Objective This review highlights the …

Abdominal wall injuries occur in nearly one of 10 patients coming to the emergency
department after nonpenetrating trauma. Injuries range from minor, such as abdominal wall …

Background Reproducible approaches are needed to bring AI/ML for medical image
analysis closer to the bedside. Investigators wishing to shadow test cross-sectional medical …

Penetrating abdominopelvic trauma usually results from abdominal cavity violation from a
firearm injury or a stab wound and is a leading cause of morbidity and mortality from …

Extremity arterial injuries account for up to 50% of all arterial traumas. The speed, accuracy,
reproducibility, and close proximity of modern CT scanners to the trauma bay have led to the …

Objective: To present the radiological features of blast-related injuries in the victims of the 10
October 2015 Ankara bombing and emphasize the importance of imaging. Methods: This …