Aims

The purpose of this study was to develop a convolutional neural network (CNN) for fracture detection, classification, and identification of greater tuberosity displacement ≥ 1 cm, neck-shaft angle (NSA) ≤ 100°, shaft translation, and articular fracture involvement, on plain radiographs.

In this study, we describe a morphological classification for greater tuberosity fractures of the proximal humerus. We divided these fractures into three types: avulsion, split and depression. We retrospectively reviewed all shoulder radiographs showing isolated greater tuberosity fractures in a Level I trauma centre between July 2007 and July 2012. We identified 199 cases where records and radiographs were reviewed and included 79 men and 120 women with a mean age of 58 years (23 to 96). The morphological classification was applied to the first 139 cases by three reviewers on two occasions using the Kappa statistic and compared with the AO and Neer classifications. The inter- and intra-observer reliability of the morphological classification was 0.73 to 0.77 and 0.69 to 0.86, respectively. This was superior to the Neer (0.31 to 0.35/0.54 to 0.63) and AO (0.30 to 0.32/0.59 to 0.65) classifications. The distribution of avulsion, split and depression type fractures was 39%, 41%, and 20%, respectively. This classification of greater tuberosity fractures is more reliable than the Neer or AO classifications. These distinct fracture morphologies are likely to have implications in terms of pathophysiology and surgical technique. Cite this article: Bone Joint J 2014;96-B:646–51