Introduction. This study aimed to evaluate the effectiveness of a novel intraoperative navigation platform for total knee arthroplasty (TKA) in restoring native knee joint kinematics and strains in the medial collateral ligament (MCL) and lateral collateral ligament (LCL) during squatting motions. Method. Six cadaver lower limbs underwent computed tomography scans to design patient-specific guides. Using these scans, bony landmarks and virtual single-line collateral ligaments were identified to provide intraoperative real-time feedback, aided in bone resection, implant alignment, tibiofemoral kinematics, and collateral ligament elongations, using the navigation platform. The specimens were subjected to squatting (35°-100°) motions on a physiological ex vivo knee simulator, maintaining a constant 110N vertical ankle load regulated by active quadriceps and bilateral hamstring actuators. Subsequently, each knee underwent a medially-stabilized TKA using the mechanical alignment technique, followed by a retest under the same conditions used preoperatively. Using a dedicated wand, MCL and LCL insertions—anterior, middle, and posterior bundles—were identified in relation to bone-pin markers. The knee kinematics and collateral ligament strains were analyzed from 3D marker trajectories captured by a six-camera optical system. Result. Both native and TKA conditions demonstrated similar patterns in tibial valgus orientation (Root Mean Square Error (RMSE=1.7°), patellar flexion (RMSE=1.2°), abduction (RMSE=0.5°), and rotation (RMSE=0.4°) during squatting (p>0.13). However, a significant difference was found in tibial internal rotation between 35° and 61° (p<0.045, RMSE=3.3°). MCL strains in anterior (RMSE=1.5%), middle (RMSE=0.8%), and posterior (RMSE=0.8%) bundles closely matched in both conditions, showing no statistical differences (p>0.05). Conversely, LCL strain across all bundles (RMSE<4.6%) exhibited significant differences from mid to deep flexion (p<0.048). Conclusion. The novel intraoperative navigation platform not only aims to achieve planned knee alignment but also assists in restoring native knee kinematics and collateral ligament behavior through real-time feedback. Acknowledgment. This study was funded by Medacta International (Castel San Pietro, Switzerland)

Introduction. The current methods for measuring femoral torsion have limitations, including variability and inaccuracies. Existing 3D methods are not reliable for abnormal femoral anteversion measurement. A new 3D method is needed for accurate measurement and planning of proximal femoral osteotomies. Currently available software for viewing and modelling CT data lacks measurement capabilities. The MSK Hip planner aims to address these limitations by combining measurement, planning, and analysis functionalities into one tool. We aim to answer 5 key questions: Is there a difference between 2D measurement methods? Is there a difference between 3D measurement methods? Is there a difference between 2D and 3D measurement methods? Are any of the measurement methods affected by the presence of osteoarthritis or a CAM deformity?. Method. After segmentation was carried out on 42 femoral CT scans using Osirix, 3D bone models were landmarked in the MSK lab hip planning software. Murphy's, Reikeras’, McBryde, and the novel MSK lab method were used to measure femoral anteversion. Result. Murphy's method had the lowest mean femoral neck anteversion (FNA) at 24.98°, while the MSK method had the highest at 28.55°. Bland-Altman plots showed systematic errors between 2D (1.201°) and 3D (1.074°) methods. All methods demonstrated good intra- and inter-user reliability. Significant differences were found between measurement methods and between patient groups. Conclusion. The MSK Hip Planner software proved useful and convenient to measure FNA. Statistically significant differences in FNA were observed between the measurement methods, as well as between patient groups when split by presence of osteoarthritis and cam deformity. Complex joint pathology and altered femoral morphology should be considered by clinicians when deciding which method to use when measuring FNA

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. 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. 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

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.

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While residual fixed flexion deformity (FFD) in unicompartmental knee arthroplasty (UKA) has been associated with worse functional outcomes, limited evidence exists regarding FFD changes. The objective of this study was to quantify FFD changes in patients with medial unicompartmental knee arthritis undergoing UKA, and investigate any correlation with clinical outcomes.

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

Reverse shoulder arthroplasty (RSA) has become the most common type of shoulder arthroplasty used in the UK, and a better understanding of the outcomes after revision of a failed RSA is needed. The aim of this study was to review the current evidence systematically to determine patient-reported outcome measures and the rates of re-revision and complications for patients undergoing revision of a RSA.

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Developmental cervical spinal stenosis (DcSS) is a well-known predisposing factor for degenerative cervical myelopathy (DCM) but there is a lack of consensus on its definition. This study aims to define DcSS based on MRI, and its multilevel characteristics, to assess the prevalence of DcSS in the general population, and to evaluate the presence of DcSS in the prediction of developing DCM.

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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.

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The prevalence of obesity is increasing substantially around the world. Elevated BMI increases the risk of complications following total hip arthroplasty (THA). We sought to evaluate trends in BMI and complication rates of obese patients undergoing primary THA over the last 30 years.

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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.

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We aimed to compare reoperations following distal radial fractures (DRFs) managed with early fixation versus delayed fixation following initial closed reduction (CR).

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This study aimed to investigate the optimal sagittal positioning of the uncemented femoral component in total knee arthroplasty to minimize the risk of aseptic loosening and periprosthetic fracture.

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This study aims to describe a new method that may be used as a supplement to evaluate humeral rotational alignment during intramedullary nail (IMN) insertion using the profile of the perpendicular peak of the greater tuberosity and its relation to the transepicondylar axis. We called this angle the greater tuberosity version angle (GTVA).

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The aims were to assess whether joint-specific outcome after total knee arthroplasty (TKA) was influenced by implant design over a 12-year follow-up period, and whether patient-related factors were associated with loss to follow-up and mortality risk.

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The sensitivity and specificity of electrodiagnostic parameters in diagnosing carpal tunnel syndrome (CTS) have been reported differently, and this study aims to address this gap.

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

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This study examined the relationship between obesity (OB) and osteoporosis (OP), aiming to identify shared genetic markers and molecular mechanisms to facilitate the development of therapies that target both conditions simultaneously.