Hamstring Injuries: A Paradigm for Return to Play.

Muscle injuries are the most common sports-related injuries, with hamstring involvement most common in professional athletes. These injuries can lead to significant time lost from play and have a high risk of reinjury. We review the anatomy, mechanisms of injury, diagnostic imaging modalities, and treatment techniques for hamstring injuries. We also present the latest evidence related to return to play (RTP) after hamstring injuries, including a review of articles targeted to RTP in European soccer (Union of European Football Associations), American football (National Football League), and other professional sports. Review of imaging findings in hamstring injury, grading systems for injuries, considerations for RTP, as well as advances in injury prevention, are discussed.

Deep Learning-Assisted Identification of Femoroacetabular Impingement (FAI) on Routine Pelvic Radiographs.

To use a novel deep learning system to localize the hip joints and detect findings of cam-type femoroacetabular impingement (FAI). A retrospective search of hip/pelvis radiographs obtained in patients to evaluate for FAI yielded 3050 total studies. Each hip was classified separately by the original interpreting radiologist in the following manner: 724 hips had severe cam-type FAI morphology, 962 moderate cam-type FAI morphology, 846 mild cam-type FAI morphology, and 518 hips were normal. The anteroposterior (AP) view from each study was anonymized and extracted. After localization of the hip joints by a novel convolutional neural network (CNN) based on the focal loss principle, a second CNN classified the images of the hip as cam positive, or no FAI. Accuracy was 74% for diagnosing normal vs. abnormal cam-type FAI morphology, with aggregate sensitivity and specificity of 0.821 and 0.669, respectively, at the chosen operating point. The aggregate AUC was 0.736. A deep learning system can be applied to detect FAI-related changes on single view pelvic radiographs. Deep learning is useful for quickly identifying and categorizing pathology on imaging, which may aid the interpreting radiologist.