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. Materials …

Objective To evaluate the performance of a convolutional neural network (CNN) model that
can automatically detect and classify rib fractures, and output structured reports from …

Objective To compare rib fracture detection and classification by radiologists using CT
images with and without a deep learning model. Materials and methods A total of 8529 chest …

Rib fracture detection is time-consuming and demanding work for radiologists. This study
aimed to introduce a novel rib fracture detection system based on deep learning which can …

Artificial intelligence algorithms utilizing deep learning are helpful tools for diagnostic
imaging. A deep learning-based automatic detection algorithm was developed for rib …

Objectives: To investigate the impact of deep learning (DL) on radiologists' detection
accuracy and reading efficiency of rib fractures on CT. Methods: Blunt chest trauma patients …

Objective To develop a convolutional neural network (CNN) model for the automatic
detection and classification of rib fractures in actual clinical practice based on cross-modal …

Purpose To evaluate the performance of a deep learning–based algorithm for automatic
detection and labeling of rib fractures from multicenter chest CT images. Materials and …

Background Diagnosis of rib fractures plays an important role in identifying trauma severity.
However, quickly and precisely identifying the rib fractures in a large number of CT images …

Objectives: Trauma chest radiographs may contain subtle and time-critical pathology.
Artificial intelligence (AI) may aid in accurate reporting, timely identification and worklist …