JBJS Rev. 2021 Dec 8;9(12). doi: 10.2106/JBJS.RVW.20.00247.
ABSTRACT
»: Suspected scaphoid fractures are a diagnostic and therapeutic challenge despite the advances in knowledge regarding these injuries and imaging techniques. The risks and restrictions of routine immobilization as well as the restriction of activities in a young and active population must be weighed against the risks of nonunion that are associated with a missed fracture.
»: The prevalence of true fractures among suspected fractures is low. This greatly reduces the statistical probability that a positive diagnostic test will correspond with a true fracture, reducing the positive predictive value of an investigation.
»: There is no consensus reference standard for a true fracture; therefore, alternative statistical methods for calculating sensitivity, specificity, and positive and negative predictive values are required.
»: Clinical prediction rules that incorporate a set of demographic and clinical factors may allow stratification of secondary imaging, which, in turn, could increase the pretest probability of a scaphoid fracture and improve the diagnostic performance of the sophisticated radiographic investigations that are available.
»: Machine-learning-derived probability calculators may augment risk stratification and can improve through retraining, although these theoretical benefits need further prospective evaluation.
»: Convolutional neural networks (CNNs) are a form of artificial intelligence that have demonstrated great promise in the recognition of scaphoid fractures on radiographs. However, in the more challenging diagnostic scenario of a suspected or so-called “clinical” scaphoid fracture, CNNs have not yet proven superior to a diagnosis that has been made by an experienced surgeon.
PMID:34879033 | DOI:10.2106/JBJS.RVW.20.00247
