Detecting pediatric appendicular fractures using artificial intelligence.

Objective: The primary objective was to assess the diagnostic accuracy of a deep learning-based artificial intelligence model for the detection of acute appendicular fractures in pediatric patients presenting with a recent history of trauma to the emergency department. The secondary goal was to examine the effect of assistive support on the emergency doctor's ability to detect fractures.

Methods: The dataset was 5,150 radiographs of which 850 showed fractures, while 4,300 radiographs did not show any fractures.

Measurement of optic nerve sheath diameter on computed tomography for the differentiation of transient ischemic attacks.

Objective: The objective of this study was to investigate whether the measurement of mean optic nerve sheath diameter in patients with transient ischemic attack could be used to distinguish between control groups, the acute ischemic stroke group, and subgroups within the acute ischemic stroke category.

Methods: Retrospectively, the mean optic nerve sheath diameters of patients aged 18 years and older belonging to control, transient ischemic attack, acute ischemic stroke, and subgroups within the acute ischemic stroke category were measured with initial computed tomography conducted in the emergency department.

Results: Out of the 773 patients included in the study, 318 (41.