The unparalleled velocity achieved by overhead throwers subjects the shoulder to extreme forces, resulting in both adaptive changes and pathologic findings that can be detected at imaging. A key biomechanical principle of throwing is achieving maximum external rotation, which initially leads to adaptive changes that may result in a pathologic cascade of injuries. In addition to the well-established concepts of glenohumeral internal rotation deficit and internal impingement, osseous and soft-tissue injuries of the shoulder unique to overhead athletes are illustrated. The epidemiology and biomechanics of throwing injuries are reviewed, and examples from the authors' institutional experience with competitive, collegiate, and professional baseball players are provided to demonstrate the constellation of unique imaging findings seen in overhead throwing athletes. Given the widespread popularity of baseball, and other sports relying on overhead throwing motions at all playing levels from recreational to professional, it is important for radiologists in various practice settings to be familiar with the special mechanisms, locations, and types of shoulder injuries seen in the overhead throwing population. RSNA, 2018.
To use deep learning with advanced data augmentation to accurately diagnose and classify femoral neck fractures. A retrospective study of patients with femoral neck fractures was performed. One thousand sixty-three AP hip radiographs were obtained from 550 patients. Ground truth labels of Garden fracture classification were applied as follows: (1) 127 Garden I and II fracture radiographs, (2) 610 Garden III and IV fracture radiographs, and (3) 326 normal hip radiographs. After localization by an initial network, a second CNN classified the images as Garden I/II fracture, Garden III/IV fracture, or no fracture. Advanced data augmentation techniques expanded the training set: (1) generative adversarial network (GAN); (2) digitally reconstructed radiographs (DRRs) from preoperative hip CT scans. In all, 9063 images, real and generated, were available for training and testing. A deep neural network was designed and tuned based on a 20% validation group. A holdout test dataset consisted of 105 real images, 35 in each class. Two class prediction of fracture versus no fracture (AUC 0.92): accuracy 92.3%, sensitivity 0.91, specificity 0.93, PPV 0.96, NPV 0.86. Three class prediction of Garden I/II, Garden III/IV, or normal (AUC 0.96): accuracy 86.0%, sensitivity 0.79, specificity 0.90, PPV 0.80, NPV 0.90. Without any advanced augmentation, the AUC for two-class prediction was 0.80. With DRR as the only advanced augmentation, AUC was 0.91 and with GAN only AUC was 0.87. GANs and DRRs can be used to improve the accuracy of a tool to diagnose and classify femoral neck fractures.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.