Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments.

ABSTRACT: Background and purpose - Artificial intelligence has rapidly become a powerful method in image analysis with the use of convolutional neural networks (CNNs). We assessed the ability of a CNN, with a fast object detection algorithm previously identifying the regions of interest, to detect distal radius fractures (DRFs) on anterior-posterior (AP) wrist radiographs. Patients and methods - 2,340 AP wrist radiographs from 2,340 patients were enrolled in this study. We trained the CNN to analyze wrist radiographs in the dataset. Feasibility of the object detection algorithm was evaluated by intersection of the union (IOU). The diagnostic performance of the network was measured by area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, specificity, and Youden Index;

SUBMITTER: Gan K 

PROVIDER: S-EPMC6718190 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature