Aims. Rotator cuff muscle atrophy and fatty infiltration affect the clinical outcomes of rotator cuff tear patients. However, there is no effective treatment for fatty infiltration at this time. High-intensity interval training (HIIT) helps to activate beige adipose tissue. The goal of this study was to test the role of HIIT in improving muscle quality in a rotator cuff tear model via the β3 adrenergic receptor (β3AR). Methods. Three-month-old C57BL/6 J mice underwent a unilateral rotator cuff injury procedure. Mice were forced to run on a treadmill with the HIIT programme during the first to sixth weeks or seventh to 12th weeks after tendon tear surgery. To study the role of β3AR, SR59230A, a selective β3AR antagonist, was administered to mice ten minutes before each exercise through intraperitoneal injection. Supraspinatus muscle, interscapular brown fat, and inguinal subcutaneous white fat were harvested at the end of the 12th week after tendon tear and analyzed biomechanically, histologically, and biochemically. Results. Histological analysis of supraspinatus muscle showed that HIIT improved muscle atrophy, fatty infiltration, and contractile force compared to the no exercise group. In the HIIT groups, supraspinatus muscle, interscapular brown fat, and inguinal subcutaneous white fat showed increased expression of tyrosine hydroxylase and uncoupling protein 1, and upregulated the β3AR thermogenesis pathway. However, the effect of HIIT was not present in mice injected with SR59230A, suggesting that HIIT affected muscles via β3AR. Conclusion. HIIT improved supraspinatus muscle quality and function after rotator cuff tears by activating systemic sympathetic nerve fibre near adipocytes and β3AR. Cite this article: Bone Joint Res 2023;12(8):455–466

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. Methods. A smartwatch was used to collect inertial data from 42 patients performing shoulder physiotherapy exercises for rotator cuff injuries in both in-clinic and at-home settings. A two-stage ML approach was used to detect out-of-distribution (OOD) data (to remove non-exercise data) and subsequently for classification of exercises. We evaluated the performance impact of grouping exercises by motion type, inclusion of non-exercise data for algorithm training, and a patient-specific approach to exercise classification. Algorithm performance was evaluated using both in-clinic and at-home data. Results. The patient-specific approach with engineered features achieved the highest in-clinic performance for differentiating physiotherapy exercise from non-exercise activity (area under the receiver operating characteristic (AUROC) = 0.924). Including non-exercise data in algorithm training further improved classifier performance (random forest, AUROC = 0.985). The highest accuracy achieved for classifying individual in-clinic exercises was 0.903, using a patient-specific method with deep neural network model extracted features. Grouping exercises by motion type improved exercise classification. For at-home data, OOD detection yielded similar performance with the non-exercise data in the algorithm training (fully convolutional network AUROC = 0.919). Conclusion. Including non-exercise data in algorithm training improves detection of exercises. A patient-specific approach leveraging data from earlier patient-supervised sessions should be considered but is highly dependent on per-patient data quality. Cite this article: Bone Joint Res 2023;12(3):165–177

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.

Objectives

To report the five-year results of a randomised controlled trial examining the effectiveness of arthroscopic acromioplasty in the treatment of stage II shoulder impingement syndrome.