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. Methods. The CNN was trained and tested on radiographs sourced from 11 hospitals in Australia and externally validated on radiographs from the Netherlands. Each radiograph was paired with corresponding CT scans to serve as the reference standard based on dual independent evaluation by trained researchers and attending orthopaedic surgeons. Presence of a fracture, classification (non- to minimally displaced; two-part, multipart, and glenohumeral dislocation), and four characteristics were determined on 2D and 3D CT scans and subsequently allocated to each series of radiographs. Fracture characteristics included greater tuberosity displacement ≥ 1 cm, NSA ≤ 100°, shaft translation (0% to < 75%, 75% to 95%, > 95%), and the extent of articular involvement (0% to < 15%, 15% to 35%, or > 35%). Results. For detection and classification, the algorithm was trained on 1,709 radiographs (n = 803), tested on 567 radiographs (n = 244), and subsequently externally validated on 535 radiographs (n = 227). For characterization, healthy shoulders and glenohumeral dislocation were excluded. The overall accuracy for fracture detection was 94% (area under the receiver operating characteristic curve (AUC) = 0.98) and for classification 78% (AUC 0.68 to 0.93). Accuracy to detect greater tuberosity fracture displacement ≥ 1 cm was 35.0% (AUC 0.57). The CNN did not recognize NSAs ≤ 100° (AUC 0.42), nor fractures with ≥ 75% shaft translation (AUC 0.51 to 0.53), or with ≥ 15% articular involvement (AUC 0.48 to 0.49). For all objectives, the model’s performance on the external dataset showed similar accuracy levels. Conclusion. CNNs proficiently rule out proximal humerus fractures on plain radiographs. Despite rigorous training methodology based on CT imaging with multi-rater consensus to serve as the reference standard, artificial intelligence-driven classification is insufficient for clinical implementation. The CNN exhibited poor diagnostic ability to detect greater tuberosity displacement ≥ 1 cm and failed to identify NSAs ≤ 100°, shaft translations, or articular fractures. Cite this article: Bone Joint J 2024;106-B(11):1348–1360

Introduction. Short-segment posterior instrumentation for spine fractures is threatened by unacceptable failure rates. Two important design objectives of pedicle screws, bending and pullout strength, may conflict with each other. Hypothesis. Multiobjective optimization study with artificial neural network (ANN) algorithm and genetic algorithm (GA). Materials & Methods. Three-dimensional finite element (FE) methods were applied to investigate the optimal designs of pedicle screws with an outer diameter of 7 mm using a multiobjective approach for these two objectives. Based on the FE results on an L25 orthogonal array, two objective functions were developed by an ANN algorithm. Then, the trade-off solutions known as Pareto optima were explored by a GA. The optimal design was validated by mechanical tests. Results. The knee solutions of the Pareto fronts had simultaneous high bending and pullout strength ranging from 92 to 94 percent of their maxima. The corresponding range of the design parameters was 3.8 to 4.06 mm for inner diameter and 3.21 to 3.3 mm for pitch; 0 mm for beginning position of conical angle, 0.4 mm for proximal root radius, 5 degrees for proximal half angle, and 0.1 mm for thread width. The optimal design was well validated by mechanical tests, comparing with commercially available pedicle screws. Discussion & Conclusions. The optimal design of pedicle screws obtained could achieve an ideal with high mechanical performance in both bending and pullout tests

Aims

Heterotopic ossification (HO) is a common complication after elbow trauma and can cause severe upper limb disability. Although multiple prognostic factors have been reported to be associated with the development of post-traumatic HO, no model has yet been able to combine these predictors more succinctly to convey prognostic information and medical measures to patients. Therefore, this study aimed to identify prognostic factors leading to the formation of HO after surgery for elbow trauma, and to establish and validate a nomogram to predict the probability of HO formation in such particular injuries.