Aims. The aim of this study was to create artificial intelligence (AI) software with the purpose of providing a second opinion to physicians to support distal radius fracture (DRF) detection, and to compare the accuracy of fracture detection of physicians with and without software support. Methods. The dataset consisted of 26,121 anonymized anterior-posterior (AP) and lateral standard view radiographs of the wrist, with and without DRF. The convolutional neural network (CNN) model was trained to detect the presence of a DRF by comparing the radiographs containing a fracture to the inconspicuous ones. A total of 11 physicians (six surgeons in training and five hand surgeons) assessed 200 pairs of randomly selected digital radiographs of the wrist (AP and lateral) for the presence of a DRF. The same images were first evaluated without, and then with, the support of the CNN model, and the diagnostic accuracy of the two methods was compared. Results. At the time of the study, the CNN model showed an area under the receiver operating curve of 0.97. AI assistance improved the physician’s sensitivity (correct fracture detection) from 80% to 87%, and the specificity (correct fracture exclusion) from 91% to 95%. The overall error rate (combined false positive and false negative) was reduced from 14% without AI to 9% with AI. Conclusion. The use of a CNN model as a second opinion can improve the diagnostic accuracy of DRF detection in the study setting. Cite this article: Bone Joint Res 2024;13(10):588–595

Aims. To evaluate if union of clavicle fractures can be predicted at six weeks post-injury by the presence of bridging callus on ultrasound. Methods. Adult patients managed nonoperatively with a displaced mid-shaft clavicle were recruited prospectively. Ultrasound evaluation of the fracture was undertaken to determine if sonographic bridging callus was present. Clinical risk factors at six weeks were used to stratify patients at high risk of nonunion with a combination of Quick Disabilities of the Arm, Shoulder and Hand questionnaire (QuickDASH) ≥ 40, fracture movement on examination, or absence of callus on radiograph. Results. A total of 112 patients completed follow-up at six months with a nonunion incidence of 16.7% (n = 18/112). Sonographic bridging callus was detected in 62.5% (n = 70/112) of the cohort at six weeks post-injury. If present, union occurred in 98.6% of the fractures (n = 69/70). If absent, nonunion developed in 40.5% of cases (n = 17/42). The sensitivity to predict union with sonographic bridging callus at six weeks was 73.4% and the specificity was 94.4%. Regression analysis found that failure to detect sonographic bridging callus at six weeks was associated with older age, female sex, simple fracture pattern, smoking, and greater fracture displacement (Nagelkerke R. 2. = 0.48). Of the cohort, 30.4% (n = 34/112) had absent sonographic bridging callus in addition to one or more of the clinical risk factors at six weeks that predispose to nonunion. If one was present the nonunion rate was 35%, 60% with two, and 100% when combined with all three. Conclusion. Ultrasound combined with clinical risk factors can accurately predict fracture healing at six weeks following a displaced midshaft clavicle fracture. Cite this article: Bone Joint Res 2021;10(2):113–121

Objectives. The objective of this study was to perform a meta-analysis of all randomised controlled trials (RCTs) comparing surgical and non-surgical management of fractures of the proximal humerus, and to determine whether further analyses based on complexity of fracture, or the type of surgical intervention, produced disparate findings on patient outcomes. Methods. A systematic review of the literature was performed identifying all RCTs that compared surgical and non-surgical management of fractures of the proximal humerus. Meta-analysis of clinical outcomes was performed where possible. Subgroup analysis based on the type of fracture, and a sensitivity analysis based on the type of surgical intervention, were also performed. Results. Seven studies including 528 patients were included. The overall meta-analysis found that there was no difference in clinical outcomes. However, subgroup and sensitivity analyses found improved patient outcomes for more complex fractures managed surgically. Four-part fractures that underwent surgery had improved long-term health utility scores (mean difference, MD 95% CI 0.04 to 0.28; p = 0.007). They were also less likely to result in osteoarthritis, osteonecrosis and non/malunion (OR 7.38, 95% CI 1.97 to 27.60; p = 0.003). Another significant subgroup finding was that secondary surgery was more common for patients that underwent internal fixation compared with conservative management within the studies with predominantly three-part fractures (OR 0.15, 95% CI 0.04 to 0.63; p = 0.009). Conclusion. This meta-analysis has demonstrated that differences in the type of fracture and surgical treatment result in outcomes that are distinct from those generated from analysis of all types of fracture and surgical treatments grouped together. This has important implications for clinical decision making and should highlight the need for future trials to adopt more specific inclusion criteria. Cite this article: S. Sabharwal, N. K. Patel, D. Griffiths, T. Athanasiou, C. M. Gupte, P. Reilly. Trials based on specific fracture configuration and surgical procedures likely to be more relevant for decision making in the management of fractures of the proximal humerus: Findings of a meta-analysisBone Joint Res 2016;5:470–480. DOI: 10.1302/2046-3758.510.2000638

Aims

An objective technological solution for tracking adherence to at-home shoulder physiotherapy is important for improving patient engagement and rehabilitation outcomes, but remains a significant challenge. The aim of this research was to evaluate performance of machine-learning (ML) methodologies for detecting and classifying inertial data collected during in-clinic and at-home shoulder physiotherapy exercise.