Aims

Pain is the most frequent complaint associated with osteonecrosis of the femoral head (ONFH), but the factors contributing to such pain are poorly understood. This study explored diverse demographic, clinical, radiological, psychological, and neurophysiological factors for their potential contribution to pain in patients with ONFH.

Introduction. Osteoarthritis (OA) causes pain, stiffness, and loss of function due to degenerative changes in joint cartilage and bone. In some forms of OA, exercise can alleviate symptoms by improving joint mobility and stability. However, excessive training after joint injury may have negative consequences for OA development. Sensory nerve fibers in joints release neuropeptides like alpha-calcitonin gene-related peptide (alpha-CGRP), potentially affecting OA progression. This study investigates the role of alpha-CGRP in OA pathogenesis under different exercise regimen in mice. Method. OA was induced in C57Bl/6J WT mice and alpha-CGRP KO mice via surgical destabilization of the medial meniscus (DMM) at 12 weeks of age (N=6). Treadmill exercise began 2 weeks post-surgery and was performed for 30 minutes, 5 days a week, for 2 or 6 weeks at intense (16 m/min, 15° incline) or moderate (10 m/min, 5° incline) levels. Histomorphometric assessment of cartilage degradation (OARSI scoring), serum cytokine analysis, immunohistochemistry, and nanoCT analysis were conducted. Result. OARSI scoring confirmed OA induction 4 weeks post-DMM surgery, with forced exercise exacerbating cartilage degradation regardless of intensity. No significant genotype-dependent differences were observed. Serum analysis revealed elevated cytokine levels associated with OA and inflammation in KO mice compared to WT mice 4 and 8 weeks post-surgery (VEGF-A, MCP-1, CXCL10, RANTES, MIP1-alpha, MIP1-beta, and RANKL). The observed effects were often exacerbated by intense exercise but rarely by DMM surgery. NanoCT analysis demonstrated increased sclerotic bone changes after 6 weeks of forced exercise in KO mice compared to WT mice. Conclusion. Our results suggest an OA promoting effect of exercise in early disease stages of posttraumatic OA. Intense exercise induced inflammatory processes correlated to increased cytokine levels in the serum that might exacerbate OA pathogenesis in later stages. The neuropeptide alpha-CGRP might play a role in protecting against these adverse effects

Introduction. Accurate assessment of alignment in pre-operative and post-operative knee radiographs is important for planning and evaluating knee replacement surgery. Existing methods predominantly rely on manual measurements using long-leg radiographs, which are time-consuming to perform and are prone to reliability errors. In this study, we propose a machine-learning-based approach to automatically measure anatomical varus/valgus alignment in pre-operative and post-operative standard AP knee radiographs. Method. We collected a training dataset of 816 pre-operative and 457 one-year post-operative AP knee radiographs of patients who underwent knee replacement surgery. Further, we have collected a separate distinct test dataset with both pre-operative and one-year post-operative radiographs for 376 patients. We manually outlined the distal femur and the proximal tibia/fibula with points to capture the knee joint (including implants in the post-operative images). This included point positions used to permit calculation of the anatomical tibiofemoral angle. We defined varus/valgus as negative/positive deviations from zero. Ground truth measurements were obtained from the manually placed points. We used the training dataset to develop a machine-learning-based automatic system to locate the point positions and derive the automatic measurements. Agreement between the automatic and manual measurements for the test dataset was assessed by intra-class correlation coefficient (ICC), mean absolute difference (MAD) and Bland-Altman analysis. Result. Analysing the agreement between the manual and automated measurements, ICC values were excellent pre-/post-operatively (0.96, CI: 0.94-0.96) / (0.95, CI: 0.95-0.96). Pre-/post-operative MAD values were 1.3°±1.4°SD / 0.7°±0.6°SD. The Bland-Altman analysis showed a pre-/post-operative mean difference (bias) of 0.3°±1.9°SD/-0.02°±0.9°SD, with pre-/post-operative 95% limits of agreement of ±3.7°/±1.8°, respectively. Conclusion. The developed machine-learning-based system demonstrates high accuracy and reliability in automatically measuring anatomical varus/valgus alignment in pre-operative and post-operative knee radiographs. It provides a promising approach for automating the measurement of anatomical alignment without the need for long-leg radiographs. Acknowledgements. This research was funded by the Wellcome Trust [223267/Z/21/Z]

Introduction. Experimental bone research often generates large amounts of histology and histomorphometry data, and the analysis of these data can be time-consuming and trivial. Machine learning offers a viable alternative to manual analysis for measuring e.g. bone volume versus total volume. The objective was to develop a neural network for image segmentation, and to assess the accuracy of this network when applied to ectopic bone formation samples compared to a ground truth. Method. Thirteen tissue slides totaling 114 megapixels of ectopic bone formation were selected for model building. Slides were split into training, validation, and test data, with the test data reserved and only used for the final model assessment. We developed a neural network resembling U-Net that takes 512×512 pixel tiles. To improve model robustness, images were augmented online during training. The network was trained for 3 days on a NVidia Tesla K80 provided by a free online learning platform against ground truth masks annotated by an experienced researcher. Result. During training, the validation accuracy improved and stabilised at approx. 95%. The test accuracy was 96.1 %. Conclusion. Most experiments using ectopic bone formation will yield an inter-observer or inter-method variance of far more than 5%, so the current approach may be a valid and feasible technique for automated image segmentation for large datasets. More data or a consensus-based ground truth may improve training stability and validation accuracy. The code and data of this project are available upon request and will be available online as part of our publication

Introduction. The increased prevalence of osteoporosis in the patient population undergoing reverse shoulder arthroplasty (RSA) results in significantly increased complication rates. Mainly demographic and clinical predictors are currently taken into the preoperative assessment for risk stratification without quantification of preoperative computed tomography (CT) data (e.g. bone density). It was hypothesized that preoperative CT bone density measures would provide objective quantification with subsequent classification of the patients’ humeral bone quality. Methods. Thirteen bone density parameters from 345 preoperative CT scans of a clinical RSA cohort represented the data set in this study. The data set was divided into testing (30%) and training data (70%), latter included an 8-fold cross validation. Variable selection was performed by choosing the variables with the highest descriptive value for each correlation clustered variables. Machine learning models were used to improve the clustering (Hierarchical Ward) and classification (Support Vector Machine (SVM)) of bone densities at risk for complications and were compared to a conventional statistical model (Logistic Regression (LR)). Results. Clustering partitioned this cohort (training data set) into a high bone density subgroup consisting of 96 patients and a low bone density subgroup consisting of 146 patients. The optimal number of clusters (n = 2) was determined based on optimization metrics. Discrimination of the cross validated classification model showed comparable performance for the training (accuracy=91.2%; AUC=0.967) and testing data (accuracy=90.5 %; AUC=0.958) while outperforming the conventional statistical model (Logistic Regression (LR)). Local interpretable model-agnostic explanations (LIME) were created for each patient to explain how the predicted output was achieved. Conclusion. The trained and tested model provides preoperative information for surgeons treating patients with potentially poor bone quality. The use of machine learning and patient-specific calibration showed that multiple 3D bone density scores improved accuracy for objective preoperative bone quality assessment

Introduction. Three-dimensional (3D) morphological understanding of the hip joint, specifically the joint space and surrounding anatomy, including the proximal femur and the pelvis bone, is crucial for a range of orthopedic diagnoses and surgical planning. While deep learning algorithms can provide higher accuracy for segmenting bony structures, delineating hip joint space formed by cartilage layers is often left for subjective manual evaluation. This study compared the performance of two state-of-the-art 3D deep learning architectures (3D UNET and 3D UNETR) for automated segmentation of proximal femur bone, pelvis bone, and hip joint space with single and multi-class label segmentation strategies. Method. A dataset of 56 3D CT images covering the hip joint was used for the study. Two bones and hip joint space were manually segmented for training and evaluation. Deep learning models were trained and evaluated for a single-class approach for each label (proximal femur, pelvis, and the joint space) separately, and for a multi-class approach to segment all three labels simultaneously. A consistent training configuration of hyperparameters was used across all models by implementing the AdamW optimizer and Dice Loss as the primary loss function. Dice score, Root Mean Squared Error, and Mean Absolute Error were utilized as evaluation metrics. Results. Both the models performed at excellent levels for single-label segmentations in bones (dice > 0.95), but single-label joint space performance remained considerably lower (dice < 0.87). Multi-class segmentations remained at lower performance (dice < 0.88) for both models. Combining bone and joint space labels may have introduced a class imbalance problem in multi-class models, leading to lower performance. Conclusion. It is not clear if 3D UNETR provides better performance as the selection of hyperparameters was the same across the models and was not optimized. Further evaluations will be needed with baseline UNET and nnUNET modeling architectures

Introduction. Exercise is recommended as first-line treatment for patients with hip osteoarthritis (OA). Interestingly, content and dose of exercise interventions seem to be important for the effect of exercise interventions, but the optimal content and dose is unknown. This warrants randomized controlled trials providing evidence for the optimal exercise program in Hip OA. The aim of this trial was to investigate whether progressive resistance training (PRT) is superior to neuromuscular exercise (NEMEX) for improving functional performance, hip pain and hip-related quality of life in patients with hip OA. Method. This was a multicenter, cluster-randomized, controlled, parallel-group, assessor-blinded, superiority trial. 160 participants with clinically diagnosed hip OA were recruited from hospitals and physiotherapy clinics and randomly assigned to twelve weeks of PRT or NEMEX. The PRT intervention consisted of 5 high-intensity resistance training exercises targeting muscles at the hip and knee joints. The NEMEX intervention included 10 exercises and emphasized sensorimotor control and functional stability. The primary outcome was change in the 30-second chair stand test (30s-CST). Key secondary outcomes were changes in scores on the pain and hip-related quality of life (QoL) subscales of the Hip Disability and Osteoarthritis Outcome Score (HOOS). Result. The mean changes from baseline to 12-week follow-up in the 30s-CST were 1.5 (95% CI, 0.9 to 2.1) chair stands with PRT and 1.5 (CI, 0.9 to 2.1) chair stands with NEMEX (difference, 0.0 [CI, 0.8 to 0.8] chair stands). For the HOOS pain subscale, mean changes were 8.6 (CI, 5.3 to 11.8) points with PRT and 9.3 (CI, 5.9 to 12.6) points with NEMEX. For the HOOS QoL subscale, mean changes were 8.0 (CI, 4.3 to 11.7) points with PRT and 5.7 (CI, 1.9 to 9.5) points with NEMEX. Conclusion. In patients with hip OA, PRT is not superior to NEMEX for improving functional performance, hip pain, or hip-related QoL

Introduction. The objective assessment of shoulder function is important for personalized diagnosis, therapies and evidence-based practice but has been limited by specialized equipment and dedicated movement laboratories. Advances in AI-driven computer vision (CV) using consumer RGB cameras (red-blue-green) and open-source CV models offer the potential for routine clinical use. However, key concepts, evidence, and research gaps have not yet been synthesized to drive clinical translation. This scoping review aims to map related literature. Method. Following the JBI Manual for Evidence Synthesis, a scoping review was conducted on PubMed and Scholar using search terms including “shoulder,” “pose estimation,” “camera”, and others. From 146 initial results, 27 papers focusing on clinical applicability and using consumer cameras were included. Analysis employed a Grounded Theory approach guided iterative refinement. Result. Studies primarily used Microsoft Kinect (infrared-based depth sensing, RGB camera; discontinued) or monocular consumer cameras with open-source CV-models, sometimes supplemented by LiDAR (laser-based depth sensing), wearables or markers. Technical validation studies against gold standards were scarce and too inconsistent for comparison. Larger range of motion (RoM) movements were accurately recorded, but smaller movements, rotations and scapula tracking remained challenging. For instance, one larger validation study comparing shoulder angles during arm raises to a marker-based gold-standard reported Pearson's R = 0.98 and a standard error of 2.4deg. OpenPose and Mediapipe were the most used CV-models. Recent efforts try to improve model performance by training with shoulder specific movements. Conclusion. Low-cost, routine clinical movement analysis to assess shoulder function using consumer cameras and CV seems feasible. It can provide acceptable accuracy for certain movement tasks and larger RoM. Capturing small, hidden or the entirety of shoulder movement requires improvements such as via training models with shoulder specific data or using dual cameras. Technical validation studies require methodological standardization, and clinical validation against established constructs is needed for translation into practice

Introduction. Shared decision making (SDM) was introduced in hospital Lillebelt in 2019 and research reports that patients are more satisfied with their treatment, if they play an active role in choosing treatment. A Decision-Helper was constructed and introduced in the treatment for Colles fractures. This study aimed to understand how patients experience shared decision-making (SDM) for an acute illness, and how it affects them when making decisions about the treatment of their distal radius fracture. Method. An exploratory, qualitative study design was performed to understand the patient's experience, during the choice of treatment with SDM. 12 were recruited when they came to their first follow-up 5 days after the injury, in the outpatient clinic. 10 were interviewed; 3 face to face and 7 by telephone. All women aged 57-87 years and all had a displaced Colles fracture, which had been reduced in the Emergency Room. Result. Analyzing the interviews three themes emerged: 1) Acute situation. Patients was positive towards SDM, but found it demanding to participate in. Patients was still in crisis, 5 days after suffering from a fracture. Patients were unable to remember the information given in the ER, regarding the use of the Decision helper. Few had prepared themselves for the consult in the outpatient clinic. 2) Influence on treatment choice. It was unclear to the majority of patients, that cast or surgery, resulted in similar clinical outcomes. 3) The treatment decision was based on personal factors, more than the information received during the consult. Conclusion. Patients wants to be included in the treatment decision. It is important to highlight that booth treatments are equal in clinical outcome, before introducing the Decision-Helper. The doctor´s demeanor is of great importance to the patient's experience. Introducing SDM in the clinical setting requires training and repeated observations, to succeed

Aims

For rare cases when a tumour infiltrates into the hip joint, extra-articular resection is required to obtain a safe margin. Endoprosthetic reconstruction following tumour resection can effectively ensure local control and improve postoperative function. However, maximizing bone preservation without compromising surgical margin remains a challenge for surgeons due to the complexity of the procedure. The purpose of the current study was to report clinical outcomes of patients who underwent extra-articular resection of the hip joint using a custom-made osteotomy guide and 3D-printed endoprosthesis.

Aims

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.

Aims

Lower limb fractures are common in low- and middle-income countries (LMICs) and represent a significant burden to the existing orthopaedic surgical infrastructure. In high income country (HIC) settings, internal fixation is the standard of care due to its superior outcomes. In LMICs, external fixation is often the surgical treatment of choice due to limited supplies, cost considerations, and its perceived lower complication rate. The aim of this systematic review protocol is identifying differences in rates of infection, nonunion, and malunion of extra-articular femoral and tibial shaft fractures in LMICs treated with either internal or external fixation.

Aims

This Delphi study assessed the challenges of diagnosing soft-tissue knee injuries (STKIs) in acute settings among orthopaedic healthcare stakeholders.

Understanding spinopelvic mechanics is important for the success of total hip arthroplasty (THA). Despite significant advancements in appreciating spinopelvic balance, numerous challenges remain. It is crucial to recognize the individual variability and postoperative changes in spinopelvic parameters and their consequential impact on prosthetic component positioning to mitigate the risk of dislocation and enhance postoperative outcomes. This review describes the integration of advanced diagnostic approaches, enhanced technology, implant considerations, and surgical planning, all tailored to the unique anatomy and biomechanics of each patient. It underscores the importance of accurately predicting postoperative spinopelvic mechanics, selecting suitable imaging techniques, establishing a consistent nomenclature for spinopelvic stiffness, and considering implant-specific strategies. Furthermore, it highlights the potential of artificial intelligence to personalize care.

Cite this article: Bone Joint J 2024;106-B(11):1206–1215.

Aims

The aim of this study was to review the provision of total elbow arthroplasties (TEAs) in England, including the incidence, the characteristics of the patients and the service providers, the types of implant, and the outcomes.

Aims

The objective of this study was to compare simulated range of motion (ROM) for reverse total shoulder arthroplasty (rTSA) with and without adjustment for scapulothoracic orientation in a global reference system. We hypothesized that values for simulated ROM in preoperative planning software with and without adjustment for scapulothoracic orientation would be significantly different.

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

Aims

Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials.

Aims

The surgical target for optimal implant positioning in robotic-assisted total knee arthroplasty remains the subject of ongoing discussion. One of the proposed targets is to recreate the knee’s functional behaviour as per its pre-diseased state. The aim of this study was to optimize implant positioning, starting from mechanical alignment (MA), toward restoring the pre-diseased status, including ligament strain and kinematic patterns, in a patient population.