Aims. Mechanical stimulation is a key factor in the development and healing of tendon-bone insertion. Treadmill training is an important rehabilitation treatment. This study aims to investigate the benefits of treadmill training initiated on postoperative day 7 for tendon-bone insertion healing. Methods. A tendon-bone insertion injury healing model was established in 92 C57BL/6 male mice. All mice were divided into control and training groups by random digital table method. The control group mice had full free activity in the cage, and the training group mice started the treadmill training on postoperative day 7. The quality of tendon-bone insertion healing was evaluated by histology, immunohistochemistry, reverse transcription quantitative polymerase chain reaction, Western blotting, micro-CT, micro-MRI, open field tests, and CatWalk gait and biomechanical assessments. Results. Our results showed a significantly higher tendon-bone insertion histomorphological score in the training group, and the messenger RNA and protein expression levels of type II collagen (COL2A1), SOX9, and type X collagen (COL10A1) were significantly elevated. Additionally, tendon-bone insertion resulted in less scar hyperplasia after treadmill training, the bone mineral density (BMD) and bone volume/tissue volume (BV/TV) were significantly improved, and the force required to induce failure became stronger in the training group. Functionally, the motor ability, limb stride length, and stride frequency of mice with tendon-bone insertion injuries were significantly improved in the training group compared with the control group. Conclusion. Treadmill training initiated on postoperative day 7 is beneficial to tendon-bone insertion healing, promoting biomechanical strength and motor function. Our findings are expected to guide clinical rehabilitation training programmes. Cite this article: Bone Joint Res 2023;12(5):339–351

Aims. The decrease in the number of satellite cells (SCs), contributing to myofibre formation and reconstitution, and their proliferative capacity, leads to muscle loss, a condition known as sarcopenia. Resistance training can prevent muscle loss; however, the underlying mechanisms of resistance training effects on SCs are not well understood. We therefore conducted a comprehensive transcriptome analysis of SCs in a mouse model. Methods. We compared the differentially expressed genes of SCs in young mice (eight weeks old), middle-aged (48-week-old) mice with resistance training intervention (MID+ T), and mice without exercise (MID) using next-generation sequencing and bioinformatics. Results. After the bioinformatic analysis, the PI3K-Akt signalling pathway and the regulation of actin cytoskeleton in particular were highlighted among the top ten pathways with the most differentially expressed genes involved in the young/MID and MID+ T/MID groups. The expression of Gng5, Atf2, and Rtor in the PI3K-Akt signalling pathway was higher in the young and MID+ T groups compared with the MID group. Similarly, Limk1, Arhgef12, and Araf in the regulation of the actin cytoskeleton pathway had a similar bias. Moreover, the protein expression profiles of Atf2, Rptor, and Ccnd3 in each group were paralleled with the results of NGS. Conclusion. Our results revealed that age-induced muscle loss might result from age-influenced genes that contribute to muscle development in SCs. After resistance training, age-impaired genes were reactivated, and age-induced genes were depressed. The change fold in these genes in the young/MID mice resembled those in the MID + T/MID group, suggesting that resistance training can rejuvenate the self-renewing ability of SCs by recovering age-influenced genes to prevent sarcopenia. Cite this article: Bone Joint Res 2022;11(2):121–133

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. Trained immunity confers non-specific protection against various types of infectious diseases, including bone and joint infection. Platelets are active participants in the immune response to pathogens and foreign substances, but their role in trained immunity remains elusive. Methods. We first trained the innate immune system of C57BL/6 mice via intravenous injection of two toll-like receptor agonists (zymosan and lipopolysaccharide). Two, four, and eight weeks later, we isolated platelets from immunity-trained and control mice, and then assessed whether immunity training altered platelet releasate. To better understand the role of immunity-trained platelets in bone and joint infection development, we transfused platelets from immunity-trained mice into naïve mice, and then challenged the recipient mice with Staphylococcus aureus or Escherichia coli. Results. After immunity training, the levels of pro-inflammatory cytokines (tumour necrosis factor alpha (TNF-α), interleukin (IL)-17A) and chemokines (CCL5, CXCL4, CXCL5, CXCL7, CXCL12) increased significantly in platelet releasate, while the levels of anti-inflammatory cytokines (IL-4, IL-13) decreased. Other platelet-secreted factors (e.g. platelet-derived growth factor (PDGF)-AA, PDGF-AB, PDGF-BB, cathepsin D, serotonin, and histamine) were statistically indistinguishable between the two groups. Transfusion of platelets from trained mice into naïve mice reduced infection risk and bacterial burden after local or systemic challenge with either S. aureus or E. coli. Conclusion. Immunity training altered platelet releasate by increasing the levels of inflammatory cytokines/chemokines and decreasing the levels of anti-inflammatory cytokines. Transfusion of platelets from immunity-trained mice conferred protection against bone and joint infection, suggesting that alteration of platelet releasate might be an important mechanism underlying trained immunity and may have clinical implications. Cite this article: Bone Joint Res 2022;11(2):73–81

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

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction. Cite this article: Bone Joint Res 2023;12(7):447–454

Aims. Manual impaction, with a mallet and introducer, remains the standard method of installing cementless acetabular cups during total hip arthroplasty (THA). This study aims to quantify the accuracy and precision of manual impaction strikes during the seating of an acetabular component. This understanding aims to help improve impaction surgical techniques and inform the development of future technologies. Methods. Posterior approach THAs were carried out on three cadavers by an expert orthopaedic surgeon. An instrumented mallet and introducer were used to insert cementless acetabular cups. The motion of the mallet, relative to the introducer, was analyzed for a total of 110 strikes split into low-, medium-, and high-effort strikes. Three parameters were extracted from these data: strike vector, strike offset, and mallet face alignment. Results. The force vector of the mallet strike, relative to the introducer axis, was misaligned by an average of 18.1°, resulting in an average wasted strike energy of 6.1%. Furthermore, the mean strike offset was 19.8 mm from the centre of the introducer axis and the mallet face, relative to the introducer strike face, was misaligned by a mean angle of 15.2° from the introducer strike face. Conclusion. The direction of the impact vector in manual impaction lacks both accuracy and precision. There is an opportunity to improve this through more advanced impaction instruments or surgical training. Cite this article: Bone Joint Res 2024;13(4):193–200

Aims. This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA. Methods. Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze immune cell infiltration. Correlation analysis was used to explore the relationship among the hub genes and immune cells, as well as markers related to articular cartilage degradation and bone mineralization. Results. A total of 46 genes were obtained from the intersection of significantly upregulated genes in osteoarthritic cartilage and the key module genes screened by WGCNA. Functional annotation analysis revealed that these genes were closely related to pathological responses associated with OA, such as inflammation and immunity. Four key dysregulated genes (cartilage acidic protein 1 (CRTAC1), iodothyronine deiodinase 2 (DIO2), angiopoietin-related protein 2 (ANGPTL2), and MAGE family member D1 (MAGED1)) were identified after using machine-learning algorithms. These genes had high diagnostic value in both the training cohort and external validation cohort (receiver operating characteristic > 0.8). The upregulated expression of these hub genes in osteoarthritic cartilage signified higher levels of immune infiltration as well as the expression of metalloproteinases and mineralization markers, suggesting harmful biological alterations and indicating that these hub genes play an important role in the pathogenesis of OA. A competing endogenous RNA network was constructed to reveal the underlying post-transcriptional regulatory mechanisms. Conclusion. The current study explores and validates a dysregulated key gene set in osteoarthritic cartilage that is capable of accurately diagnosing OA and characterizing the biological alterations in osteoarthritic cartilage; this may become a promising indicator in clinical decision-making. This study indicates that dysregulated key genes play an important role in the development and progression of OA, and may be potential therapeutic targets. Cite this article: Bone Joint Res 2024;13(2):66–82

Aims. To determine the major risk factors for unplanned reoperations (UROs) following corrective surgery for adult spinal deformity (ASD) and their interactions, using machine learning-based prediction algorithms and game theory. Methods. Patients who underwent surgery for ASD, with a minimum of two-year follow-up, were retrospectively reviewed. In total, 210 patients were included and randomly allocated into training (70% of the sample size) and test (the remaining 30%) sets to develop the machine learning algorithm. Risk factors were included in the analysis, along with clinical characteristics and parameters acquired through diagnostic radiology. Results. Overall, 152 patients without and 58 with a history of surgical revision following surgery for ASD were observed; the mean age was 68.9 years (SD 8.7) and 66.9 years (SD 6.6), respectively. On implementing a random forest model, the classification of URO events resulted in a balanced accuracy of 86.8%. Among machine learning-extracted risk factors, URO, proximal junction failure (PJF), and postoperative distance from the posterosuperior corner of C7 and the vertical axis from the centroid of C2 (SVA) were significant upon Kaplan-Meier survival analysis. Conclusion. The major risk factors for URO following surgery for ASD, i.e. postoperative SVA and PJF, and their interactions were identified using a machine learning algorithm and game theory. Clinical benefits will depend on patient risk profiles. Cite this article: Bone Joint Res 2023;12(4):245–255

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The incidence of limb fractures in patients living with HIV (PLWH) is increasing. However, due to their immunodeficiency status, the operation and rehabilitation of these patients present unique challenges. Currently, it is urgent to establish a standardized perioperative rehabilitation plan based on the concept of enhanced recovery after surgery (ERAS). This study aimed to validate the effectiveness of ERAS in the perioperative period of PLWH with limb fractures.

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This study was designed to develop a model for predicting bone mineral density (BMD) loss of the femur after total hip arthroplasty (THA) using artificial intelligence (AI), and to identify factors that influence the prediction. Additionally, we virtually examined the efficacy of administration of bisphosphonate for cases with severe BMD loss based on the predictive model.

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This study aimed to develop and validate a fully automated system that quantifies proximal femoral bone mineral density (BMD) from CT images.

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A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance.

Cite this article: Bone Joint Res 2023;12(6):372–374.

Cite this article: Bone Joint Res 2023;12(8):494–496.

Cite this article: Bone Joint Res 2023;12(4):256–258.

Despite the vast quantities of published artificial intelligence (AI) algorithms that target trauma and orthopaedic applications, very few progress to inform clinical practice. One key reason for this is the lack of a clear pathway from development to deployment. In order to assist with this process, we have developed the Clinical Practice Integration of Artificial Intelligence (CPI-AI) framework – a five-stage approach to the clinical practice adoption of AI in the setting of trauma and orthopaedics, based on the IDEAL principles (https://www.ideal-collaboration.net/). Adherence to the framework would provide a robust evidence-based mechanism for developing trust in AI applications, where the underlying algorithms are unlikely to be fully understood by clinical teams.

Cite this article: Bone Joint Res 2024;13(9):507–512.

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We aimed to assess the reliability and validity of OpenPose, a posture estimation algorithm, for measurement of knee range of motion after total knee arthroplasty (TKA), in comparison to radiography and goniometry.

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The presence of facet tropism has been correlated with an elevated susceptibility to lumbar disc pathology. Our objective was to evaluate the impact of facet tropism on chronic lumbosacral discogenic pain through the analysis of clinical data and finite element modelling (FEM).