Development of an artificial intelligence-assisted computed tomography diagnosis technology for rib fracture and evaluation of its clinical usefulness.

Artificial intelligence algorithms utilizing deep learning are helpful tools for diagnostic imaging. A deep learning-based automatic detection algorithm was developed for rib fractures on computed tomography (CT) images of high-energy trauma patients. In this study, the clinical effectiveness of this algorithm was evaluated.

Detection of acute rib fractures on CT images with convolutional neural networks: effect of location and type of fracture and reader's experience.

Purpose: The evaluation of all ribs on thin-slice CT images is time consuming and it can be difficult to accurately assess the location and type of rib fracture in an emergency. The aim of our study was to develop and validate a convolutional neural network (CNN) algorithm for the detection of acute rib fractures on thoracic CT images and to investigate the effect of the CNN algorithm on radiologists' performance.

Methods: The dataset for development of a CNN consisted of 539 thoracic CT scans with 4906 acute rib fractures.

Cell surface hydrophobicity and colony morphology of Trichosporon asahii clinical isolates.

Trichosporon asahii is a pathogenic basidiomycetous yeast. Individual strains of T. asahii have different colony morphologies.