Abstract. Objectives. The fidelity of a 3D model created using image segmentation must be precisely quantified and evaluated for the model to be trusted for use in subsequent biomechanical studies such as finite element analysis. The bones within the ankle joint vary significantly in size and shape. The purpose of this study was to test the hypothesis that the accuracy and reliability of a segmented bone geometry is independent of the particular bone being measured. Methods. Computed tomography (CT) scan data (slice thickness 1 mm, pixel size 808±7 µm) from three anonymous patients was used for the development of the ankle geometries (consisting of the tibia, fibula, talus, calcaneus, and navicular bones) using Simpleware Scan IP software (Synopsys, Exeter, UK). Each CT scan was segmented 4 times by an inexperienced undergraduate, resulting in a total of 12 geometry assemblies. An experienced researcher segmented each scan once, and this was used as the ‘gold standard’ to quantify the accuracy. The solid bone geometries were imported into CAD software (Inventor 2023, Autodesk, CA, USA) for measurement of the surface area and volume of each bone, and the distances between bones (tibia to talus, talus to navicular, talus to calcaneus, and tibia to fibula) were carried out. The intra-class coefficient (ICC) was used to assess intra-observer reliability. Bland Altman plots were employed as a statistical measure for criteria validity (accuracy) [1]. Results. The average ICC score was 0.93, which is regarded as a high reliability score for an inexperienced user. The talus to navicular and talus to tibia separations, which had the smallest distances, showed a slight decrease in reliability and this was observed for all separations shorter than 2 mm. According to the Bland-Altman plots, more than 95% of the data points were inside the borders of agreement, which is an excellent indication of accuracy. The bias percentage (average error percentage) varied between 1% and 4% and was constant across all parameters, with the proportion rising for short distance separations. Conclusions. The current study demonstrates that an inexperienced undergraduate, with access to software manuals, can segment an ankle CT scan with excellent reliability. The present study also concluded that all five bones were segmented with high levels of accuracy, and this was not influenced by bone volume or type. The only factor found to influence the reliability was the magnitude of distance between bones, where if this was smaller than 2 mm it reduced the reliability, indicating the influence of CT scan resolution on the segmentation reliability. Declaration of Interest. (b) declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported:I declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project

Patients with advanced cancer can develop bone metastases in the femur which are often painful and increase the risk of pathological fracture. Accurate segmentation of bone metastases is, amongst others, important to improve patient-specific computer models which calculate fracture risk, and for radiotherapy planning to determine exact radiation fields. Deep learning algorithms have shown to be promising to improve segmentation accuracy for metastatic lesions, but require reliable segmentations as training input. The aim of this study was to investigate the inter- and intra-operator reliability of manual segmentation of femoral metastatic lesions and to define a set of lesions which can serve as a training dataset for deep learning algorithms. F. CT-scans of 60 advanced cancer patients with a femur affected with bone metastases (20 osteolytic, 20 osteoblastic and 20 mixed) were used in this study. Two operators were trained by an experienced radiologist and then segmented the metastatic lesions in all femurs twice with a four-week time interval. 3D and 2D Dice coefficients (DCs) were calculated to quantify the inter- and intra-operator reliability of the segmentations. We defined a DC>0.7 as good reliability, in line with a statistical image segmentation study. Mean first and second inter-operator 3D-DCs were 0.54 (±0.28) and 0.50 (±0.32), respectively. Mean intra-operator I and II 3D-DCs were 0.56 (±0.28) and 0.71 (±0.23), respectively. Larger lesions (>60 cm. 3. ) scored higher DCs in comparison with smaller lesions. This study reveals that manual segmentation of metastatic lesions is challenging and that the current manual segmentation approach resulted in dissatisfying outcomes, particularly for lesions with small volumes. However, segmentation of larger lesions resulted in a good inter- and intra-operator reliability. In addition, we were able to select 521 slices with good segmentation reliability that can be used to create a training dataset for deep learning algorithms. By using deep learning algorithms, we aim for more accurate automated lesion segmentations which might be used in computer modelling and radiotherapy planning

Abstract. Objectives. Total hip arthroplasty (THA) procedures are physically demanding for surgeons. Repetitive mallet swings to impact a surgical handle (impactions), can lead to muscle fatigue, discomfort and injuries. The use of an automated surgical hammer may reduce fatigue and increase surgical efficiency. The aim of this study was to develop a method to quantify user's performance, by recording surface electromyography (sEMG), for automated and manual impactions. Methods. sEMG signals were recorded from eight muscle compartments (arm and back muscles) of an orthopaedic surgeon during repetitions of manual and automated impaction tasks, replicating femoral canal preparation (broaching) during a THA. Each task was repeated, randomly, four times manually and four times with the automated impaction device. The mechanical outcomes (broaching efficiency and broach advancement) were quantified by tracking the kinematics of the surgical instrumentation. Root mean square (RMS) values and median frequency (MDF) were calculated for each task to, respectively, investigate which muscles were mostly involved (higher RMS) in each task and to quantify the decrease in MDF, which is an indicator of muscle fatigue. Results. RMS for arm muscles was significantly higher (p-value=0.002) during manual impactions than during automated impactions and muscle fatigue was significantly reduced (p-value=0.011), for the same muscles, when the same tasks were performed with the automated surgical hammer. The time required to achieve the same mechanical outcome, in terms of broaching efficiency and broach advancement, was significantly reduced with the automated surgical hammer (p=0.019). Conclusions. Results from this study showed how with this methodology it was possible to discern muscle performance and fatigue, between impaction modalities. Moreover, the reduction in exposure time to automated impactions, could be a factor in muscle fatigue decrease. These results could therefore provide useful insights into the study of surgical ergonomic improvements, to reduce surgeons muscle fatigue and, potentially, injuries. Declaration of Interest. (a) fully declare any financial or other potential conflict of interest

Introduction. The objective of this study was to compare the performance of the Explant Acetabular Cup Removal System (Zimmer), which has been the favored system for many surgeons during hip revision surgery, and the new EZout Powered Acetabular Revision System (Stryker). Methods. 54mm Stryker Trident® acetabular shells were inserted into the foam acetabula of 24 composite hemi-pelvises (Sawbones). The hemi-pelvises were mounted on a supporting apparatus enclosing three load cells. Strain gauges were placed on the hemipelvis, on the posterior and the anterior wall, and on the internal ischium in proximity to the acetabular fossa. A thermocouple was fixed onto the polar region of the acetabular component. One experienced orthopaedic surgeon and one resident performed mock revision surgery 6 times each per system. Results. Statistical analysis was conducted using Tukey's range test (HSD). The maximum force transferred to the implant was more than 4X lower with the EZout System regardless the surgeon experience (p=1.0E-08). Overall, recorded strains were lower for the EZout System with the higher decrease in strain (5X) observed at the posterior wall region(p=2E-08). The temperature at the interface was higher for the EZout System but never more than 37°C. Total removal time was on average reduced by a third with the EZout System (p=0.01). The calculated torque was lower for the EZout System. The amount of foam left on the cup after removal, which mimics the compromised bone, was 2.5X higher on average for the Explant System with most of the foam concentrated in the polar region. Lastly, it was observed that the polar region of each implant was reached by rotating the EZout System handpiece within a very narrow cylinder of space centered along the axis of the acetabular component compared to the Explant System, which required movement of the pivoting osteotomes within a large cone-shaped operating envelope. Discussion. Quantitatively, the EZout System required lower force, producing lower strains in the surrounding composite bone. Higher impact forces and associated increased strains may increase fracture risk. Qualitatively, the Explant System required a greater cone of movement than the EZout System requiring more space for the surgeon to leverage the handle of the tool. In addition, both surgeon and resident felt substantially greater exhaustion after using the Explant System vs. the EZout System. The resident compensated for the increased workload of the Explant with time, the experienced surgeon with force. The learning curve for both experienced surgeon and resident was also much shorter with the EZout System as shown by the close force values between the experienced surgeon and resident. Conclusion. Based on the results of this in vitro model, the EZout Powered Acetabular Removal System may be a reasonable alternative to manual removal techniques

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]

µCT images are commonly analysed to assess changes in bone density and architecture in preclinical murine models. Several platforms provide automated analysis of bone architecture parameters from volumetric regions of interest (ROI). However, segmentation of the regions of subchondral bone to create the volumetric ROIs remains a manual and time-consuming task. This study aimed to develop and evaluate automated pipelines for trabecular bone architecture analysis of mouse proximal tibia subchondral bone. A segmented dataset involving 62 knees (healthy and arthritic) from 10-week male C57BL/6 mice were used to train a U-Net type architecture, with µCT scans (downsampled) input that output segmentation and bone volume density (BV/TV) of the subchondral trabecular bone. Segmentations were upsampled and used in tandem with the original scans (10µ) as input for architecture analysis along with the thresholded trabecular bone. The analysis considered the manually and U-Net segmented ROIs using two available pipelines: the ITKBoneMorphometry library and CTan (SKYSCAN). The analyses included: bone volume (BV), total volume (TV), BV/TV, trabecular number (TbN), trabecular thickness (TbTh), trabecular separation (TbSp), and bone surface density (BSBV). There was good agreement for bone measures between the manual and U-Net pipelines utilizing ITK (R=0.88-0.98) and CTan (R=0.91-0.98). ITK and CTan showed good agreement for BV, TV, BV/TV, TbTh and BSBV (R=0.9-0.98). However, a limited agreement was seen between TbN (R=0.73) and TbSb (R=0.59) due to methodological differences in how spacing is evaluated. This U-Net/ITK pipeline seamlessly automated both segmentation and quantification of the proximal tibia subchondral bone. This automated pipeline allows the analysis of large volumes of data, and its open-source nature may enable the standardization of stereologic analysis of trabecular bone across different research groups

The most important outcome predictor of Legg-Calvé-Perthes disease (LCPD) is the shape of the healed femoral head. However, the deformity of the femoral head is currently evaluated by non-reproducible, categorical, and qualitative classifications. In this regard, recent advances in computer vision might provide the opportunity to automatically detect and delineate the outlines of bone in radiographic images for calculating a continuous measure of femoral head deformity. This study aimed to construct a pipeline for accurately detecting and delineating the proximal femur in radiographs of LCPD patients employing existing algorithms. To detect the proximal femur, the pretrained stateof-the-art object detection model, YOLOv5, was trained on 1580 manually annotated radiographs, validated on 338 radiographs, and tested on 338 radiographs. Additionally, 200 radiographs of shoulders and chests were added to the dataset to make the model more robust to false positives and increase generalizability. The convolutional neural network architecture, U-Net, was then employed to segment the detected proximal femur. The network was trained on 80 manually annotated radiographs using real-time data augmentation to increase the number of training images and enhance the generalizability of the segmentation model. The network was validated on 60 radiographs and tested on 60 radiographs. The object detection model achieved a mean Average Precision (mAP) of 0.998 using an Intersection over Union (IoU) threshold of 0.5, and a mAP of 0.712 over IoU thresholds of 0.5 to 0.95 on the test set. The segmentation model achieved an accuracy score of 0.912, a Dice Coefficient of 0.937, and a binary IoU score of 0.854 on the test set. The proposed fully automatic proximal femur detection and segmentation system provides a promising method for accurately detecting and delineating the proximal femoral bone contour in radiographic images, which is necessary for further image analysis

To be able to assess the biomechanical and functional effects of ankle injury and disease it is necessary to characterise healthy ankle kinematics. Due to the anatomical complexity of the ankle, it is difficult to accurately measure the Tibiotalar and Subtalar joint angles using traditional marker-based motion capture techniques. Biplane Video X-ray (BVX) is an imaging technique that allows direct measurement of individual bones using high-speed, dynamic X-rays. The objective is to develop an in-vivo protocol for the hindfoot looking at the tibiotalar and subtalar joint during different activities of living. A bespoke raised walkway was manufactured to position the foot and ankle inside the field of view of the BVX system. Three healthy volunteers performed three gait and step-down trials while capturing Biplane Video X-Ray (125Hz, 1.25ms, 80kVp and 160 mA) and underwent MR imaging (Magnetom 3T Prisma, Siemens) which were manually segmented into 3D bone models (Simpleware Scan IP, Synopsis). Bone position and orientation for the Talus, Calcaneus and Tibia were calculated by manual matching of 3D Bone models to X-Rays (DSX Suite, C-Motion, Inc.). Kinematics were calculated using MATLAB (MathWorks, Inc. USA). Pilot results showed that for the subtalar joint there was greater range of motion (ROM) for Inversion and Dorsiflexion angles during stance phase of gait and reduced ROM for Internal Rotation compared with step down. For the tibiotalar joint, Gait had greater inversion and internal rotation ROM and reduced dorsiflexion ROM when compared with step down. The developed protocol successfully calculated the in-vivo kinematics of the tibiotalar and subtalar joints for different dynamic activities of daily living. These pilot results show the different kinematic profiles between two different activities of daily living. Future work will investigate translation kinematics of the two joints to fully characterise healthy kinematics

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

Objectives. This study reports on a secondary exploratory analysis of the early clinical outcomes of a randomised clinical trial comparing robotic arm-assisted unicompartmental knee arthroplasty (UKA) for medial compartment osteoarthritis of the knee with manual UKA performed using traditional surgical jigs. This follows reporting of the primary outcomes of implant accuracy and gait analysis that showed significant advantages in the robotic arm-assisted group. Methods. A total of 139 patients were recruited from a single centre. Patients were randomised to receive either a manual UKA implanted with the aid of traditional surgical jigs, or a UKA implanted with the aid of a tactile guided robotic arm-assisted system. Outcome measures included the American Knee Society Score (AKSS), Oxford Knee Score (OKS), Forgotten Joint Score, Hospital Anxiety Depression Scale, University of California at Los Angeles (UCLA) activity scale, Short Form-12, Pain Catastrophising Scale, somatic disease (Primary Care Evaluation of Mental Disorders Score), Pain visual analogue scale, analgesic use, patient satisfaction, complications relating to surgery, 90-day pain diaries and the requirement for revision surgery. Results. From the first post-operative day through to week 8 post-operatively, the median pain scores for the robotic arm-assisted group were 55.4% lower than those observed in the manual surgery group (p = 0.040). At three months post-operatively, the robotic arm-assisted group had better AKSS (robotic median 164, interquartile range (IQR) 131 to 178, manual median 143, IQR 132 to 166), although no difference was noted with the OKS. At one year post-operatively, the observed differences with the AKSS had narrowed from a median of 21 points to a median of seven points (p = 0.106) (robotic median 171, IQR 153 to 179; manual median 164, IQR 144 to 182). No difference was observed with the OKS, and almost half of each group reached the ceiling limit of the score (OKS > 43). A greater proportion of patients receiving robotic arm-assisted surgery improved their UCLA activity score. Binary logistic regression modelling for dichotomised outcome scores predicted the key factors associated with achieving excellent outcome on the AKSS: a pre-operative activity level > 5 on the UCLA activity score and use of robotic-arm surgery. For the same regression modelling, factors associated with a poor outcome were manual surgery and pre-operative depression. Conclusion. Robotic arm-assisted surgery results in improved early pain scores and early function scores in some patient-reported outcomes measures, but no difference was observed at one year post-operatively. Although improved results favoured the robotic arm-assisted group in active patients (i.e. UCLA ⩾ 5), these do not withstand adjustment for multiple comparisons. Cite this article: M. J. G. Blyth, I. Anthony, P. Rowe, M. S. Banger, A. MacLean, B. Jones. Robotic arm-assisted versus conventional unicompartmental knee arthroplasty: Exploratory secondary analysis of a randomised controlled trial. Bone Joint Res 2017;6:631–639. DOI: 10.1302/2046-3758.611.BJR-2017-0060.R1

Malalignment is often postulated as the main reason for the high failure rate of total ankle replacements (TARs). Only a few studies have been performed to correlate radiographic TAR malalignment to the clinical outcome, but no consistent trends between TAR alignment parameters and the clinical outcome were found. No standard TAR alignment measurement method is present, so reliable comparison between studies is difficult. Standardizing TAR alignment measurements and increasing measurable parameters on radiographs in the clinic might lead to a better insight into the correlation between malalignment and the clinical outcome. This study aims to develop and validate a tool to semi-automatic measure TAR alignment, and to improve alignment measurement on radiographs in the clinic. A tool to semi-automatically measure TAR alignment on anteroposterior and lateral radiographs was developed and used by two observers to measure TAR alignment parameters of ten patients. The Intraclass Coefficient (ICC) was calculated and accuracy was compared to the manual measurement method commonly used in the clinic. The tool showed an accuracy of 76% compared to 71% for the method used during follow-up in the clinic. ICC values were 0.94 (p<0.01) and higher for both inter-and intra-observer reliability. The tool presents an accurate, consistent, and reliable method to measure TAR alignment parameters. Three-dimensional alignment parameters are obtained from two-dimensional radiographs, and as the tool can be applied to any TAR design, it offers a valuable addition in the clinic and for research purposes

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. A long nail is often recommended for treatment of complex trochanteric fractures but requires longer surgical and fluoroscopy times. A possible solution could be a nail with an appropriate length which can be locked in a minimally invasive manner by the main aiming device. We aimed to determine if such a nail model* offers similar structural stability on biomechanical testing on artificial bone as a standard long nail when used to treat complex trochanteric fractures. Method. An artificial osteoporotic bone model was chosen. As osteosynthesis material two cephalomedullary nails (CMN) were chosen: a superior locking nail (SL-Nail) which can be implanted with a singular targeting device, and a long nail (long-nail) with distal locking using free-hand technique. AO31-A2.2 fractures were simulated in a standardized manner. The insertion of the nail was strictly in accordance with the IFU and surgical manual of the manufacturer. The nail was locked dynamically proximally and statically distally. Axial height of the construct, varus collapse, and rotational deformity directly after nail insertion were simulated. A Universal Testing Machine was used. Measurements were made with a stereo-optic tracking system. Reactive movements were recorded and evaluated in all six degrees of freedom. A comparative analysis provided information about the stability and deformation of the assemblies to be compared. Result. There was a detectable difference in the axial fracture movement resulting in narrowing of the fracture gap. The load displacement was 1.7mm higher for the SL-Nail. There was no difference in varus collapse or rotational deformity between the nail variants. Conclusion. We conclude that there are small differences which are clinically insignificant and that a superior locking nail can safely be used to manage complex trochanteric fractures. *DCN SL nail, SWEMAC, Linköping, Sweden. Funding: no funding

Mechanical loading of joints with osteoarthritis (OA) results in pain-related functional impairment, altered joint mechanics and physiological nociceptor interactions leading to an experience of pain. However, the current tools to measure this are largely patient reported subjective impressions of a nociceptive impact. A direct measure of nociception may offer a more objective indicator. Specifically, movement-induced physiological responses to nociception may offer a useful way to monitor knee OA. In this study, we gathered preliminary data on healthy volunteers to analyse whether integrated biomechanical and physiological sensor datasets could display linked and quantifiable information to a nociceptive stimulus. Following ethical approval, 15 healthy volunteers completed 5 movement and stationary activities in 2 conditions; a control setting and then repeated with an applied quantified thermal pain stimulus to their right knee. An inertial measurement unit (IMU) and an electromyography (EMG) lower body marker set were tested and integrated with ground reaction force (GRF) data collection. Galvanic skin response electrodes for skin temperature and conductivity and photoplethysmography (PPG) sensors were manually timestamped to the integrated system. Pilot data showed EMG, GRF and IMU fluctuations within 0.5 seconds of each other in response to a thermal trigger. Preliminary analysis on the 15 participants tested has shown skin conductance, PPG, EMG, GRFs, joint angles and kinematics with varying increases and fluctuations during the thermal condition in comparison to the control condition. Preliminary results suggest physiological and biomechanical data outputs can be linked and identified in response to a defined nociceptive stimulus. Study data is currently founded on healthy volunteers as a proof-of-concept. Further exploratory statistical and sensor readout pattern analysis, alongside early and late-stage OA patient data collection, can provide the information for potential development of wearable nociceptive sensors to measure disease progression and treatment effectiveness

Biplane video X-ray (BVX) – with models segmented from magnetic resonance imaging (MRI) – is used to directly track bones during dynamic activities. Investigating tibiofemoral kinematics helps to understand effects of disease, injury, and possible interventions. Develop a protocol and compare in-vivo kinematics during loaded dynamic activities using BVX and MRI. BVX (60 FPS) was captured whilst three healthy volunteers performed three repeats of lunge, stair ascent and gait. MRI scans were performed (Magnetom 3T Prisma, Siemens). 3D bone models of the tibia and femur were segmented (Simpleware Scan IP, Synopsis). Bone poses were obtained by manually matching bone models to X-rays (DSX Suite, C-Motion Inc.). Mean range of motion (ROM) of the contact points on the medial and lateral tibial plateau were calculated using custom MATLAB code (MathWorks). Results were filtered using an adaptive low pass Butterworth filter (Frequency range: 5-29Hz). Gait and Stair ascent activities from one participant's data showed increased ROM for medial-lateral (ML) translation in the medial compartment but decreased ROM in anterior-posterior (AP) translation when comparing against the same translations on the lateral compartment of the tibial plateau. Lunge activity showed increased ROM for both ML and AP translation in the medial compartment when compared with the lateral compartment. These results highlight the variability in condylar translations between different activities. Understanding healthy in-vivo kinematics across different activities allows the determination of suitable activities to best investigate the kinematic changes due to disease or injury and assess the efficacy of different interventions. Acknowledgements: This research was supported by the Engineering and Physical Sciences Research Council (EPSRC) doctoral training grant (EP/T517951/1)

To investigate temporal changes in synovial lymphatic system (SLS) drainage function after Anterior cruciate ligament (ACL) injury, a non-invasive ACL rupture model was used to induce the PTOA phenotype without altering the SLS structure. We have created a non-invasive ACL rupture model in the right knee (single overload impact) of 12- week-old C57bl/6 male mice to mimic the ACL rupture-induced PTOA development. 70 kDa-TxRedDextran were injected into the right knee of the mice at 0, 1, 2, and 4 wks post modeling (n=5/group), and the fluorescence signal distribution and intensity were measured by the IVIS system at 1 and 6 hrs post-injection. After 24 hrs, the drainage lymph nodes and whole knee joint were harvested and subjected to ex vivo IVIS imaging and immunofluorescence detection respectively. Manual ACL rupture was induced by 12N overloaded force and validated by a front drawer test. Intraarticular clearance of TxRed-Dextran detected by the IVIS was significantly reduced at 1, and 2 wks at a level of 43% and 55% respectively but was not significantly different from baseline levels at 4 wks (89%). TxRed-Dextran signal in draining lymph nodes was significantly reduced at 1 week at the level of but not for 2 and 4 wks compared to baseline levels (week 1–29%, week 2–50%, week 4–94%). TxRed-Dextran particle was significantly enriched in the synovium at 1, 2 wks but was not significantly different from baseline levels at 4 wks rupture-post ACL rupture (Particle numbers: Sham Ctrl-34 ±14, week 1, 113 ± 17; week 2, 89 ± 13; week 4, 46 ± 18; mean ± SD). We observed the drainage function of SLS significantly decreased at 1 and 2 wks after the ACL rupture, and was slowly restored at 4 wks post-injury in a non-invasive ACL rupture model. Early impairment of SLS drainage function may lead to accumulation of inflammatory factors and promote PTOA progression

Helical plates potentially bypass the medial neurovascular structures of the thigh. Recently, two plate designs (90°- and 180°-helix) proved similar biomechanically behaviour compared to straight plates. Aims of this study were: (1) Feasibility of MIPO-technique with 90°- and 180°-helical plates on the femur, (2) Assessment of distances to adjacent anatomical structures at risk, (3) Comparison of these distances to using medial straight plates instead, (4) Correlation of measurements performed in anatomic dissection with CT-angiography. MIPO was performed in ten cadaveric femoral pairs using either a 90°-helical 14-hole-LCP (Group1) or a 180°-helical 15-hole-LCP-DF (Group2). CT angiography was used to evaluate the distances between the plates and the femoral arteries as well as the distances between the plates and the perforators. Subsequently, the specimens were dissected, and the distances were determined again manually. Finally, all helical plates were removed, and all measurements were repeated after application of straight medial plates (Group3). Closest overall distances between plates and femoral arteries were 15 mm (11 − 19 mm) in Group1, 22 mm (15 − 24 mm) in Group2 and 6 mm (1 − 8 mm) in Group3 with a significant difference between Group1 and Group3 (p < 0.001). Distances to the nearest perforators were 24 mm (15 − 32 mm) in Group1 and 2 mm (1 − 4 mm) in Group2. Measurement techniques (visual after surgery and CT-angiography) demonstrated a strong correlation of r. 2. = 0.972 (p < 0.01). MIPO with 90°- and 180°-helical plates is feasible and safe. Attention must be paid to the medial neurovascular structures with 90°-helical implants and to the proximal perforators with 180°-helical implants. Helical implants can avoid medial neurovascular structures compared to straight plates although care must be taken during their distal insertion. Measurements during anatomical dissection correlate with CT-angiography

According to the latest report from the German Arthroplasty Registry, aseptic loosening is the primary cause of implant failure following primary hip arthroplasty. Osteolysis of the proximal femur due to the stress-shielding of the bone by the implant causes loss of fixation of the proximal femoral stem, while the distal stem remains fixed. Removing a fixed stem is a challenging process. Current removal methods rely on manual tools such as chisels, burrs, osteotomes, drills and mills, which pose the risk of bone fracture and cortical perforation. Others such as ultrasound and laser, generate temperatures that could cause thermal injury to the surrounding tissues and bone. It is crucial to develop techniques that preserve the host bone, as its quality after implant removal affects the outcome of a revision surgery. A gentler removal method based on the transcutaneous heating of the implant by induction is proposed. By reaching the glass transition temperature (T. G. ) of the periprosthetic cement, the cement is expected to soften, enabling the implant to be gently pulled out. The in-vivo environment comprises body fluids and elevated temperatures, which deteriorate the inherent mechanical properties of bone cement, including its T. G. We aimed to investigate the effect of fluid absorption on the T. G. (ASTM E2716-09) and Vicat softening temperature (VST) (ISO 306) of Palacos R cement (Heraeus Medical GmbH) when dry and after storage in Ringer's solution for up to 8 weeks. Samples stored in Ringer's solution exhibited lower T. G. and VST than those stored in air. After 8 weeks, the T. G. decreased from 95.2°C to 81.5°C in the Ringer's group, while the VST decreased from 104.4°C to 91.9°C. These findings will be useful in the ultimate goal of this project which is to design an induction-based system for implant removal. Acknowledgements: Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB/TRR-298-SIIRI – Project-ID 426335750

Introduction. Functional Spine Units (FSUs) play a vital role in understanding biomechanical characteristics of the spine, particularly bone fracture risk assessment. While established models focus on simulating axial compression of individual bones to assess fracture load, recent models underscore the importance of understanding fracture load within FSUs, offering a better representation of physiological conditions. Despite the limited number of FSU fracture studies, they predominantly rely on a linear material model with an annulus fibrosus Young's modulus set at 500 MPa, significantly higher than stiffness values (ca. 4 MPa) utilized in other FSU and spine section biomechanical models. Thus, this study aims to study the effect of varying annulus fibrosus stiffness on FSU fracture load, aiming to identify physiologically relevant biomechanical parameters. Method. Subject-specific geometry and material properties of bones were derived from computed tomography (CT) image data of five human cadaveric FSU specimens. The annulus fibrosus and nucleus pulposus were manually recreated and assigned linear elastic material properties. By subjecting the model to axial compression, the fracture load of the FSU was deduced from the peak of the force-displacement graph. To explore the effect of stiffness of the annulus fibrosus on simulated fracture load, we conducted a parameter study, varying stiffness values from the high 500 MPa to a more physiologically relevant 25 MPa, aiming to approximate values applied in FSU kinematic models while achieving bone fracture. Result. Significant reductions in fracture load were observed, ranging from 23% to 46%, as annulus stiffness decreased from 500MPa to 25MPa. Additionally, a discernible, gradual decline in fracture load was observed with a decrease in stiffness values. Conclusion. The stiffness of the annulus fibrosus significantly influences the simulated fracture load of an FSU. Future investigations should prioritize biomechanically accurate modeling of the intervertebral disc, ensuring alignment with experimental findings regarding FSU fracture load while maintaining biomechanical fidelity

Plantar fasciitis (PF) is one of the widespread conditions causing hindfoot pain. The most common presenting symptoms are functional limitation and pain (first step and activity) on plantar surface of the foot. The non-operative treatments provide complete resolution of pain in 90% of patients, but functional limitation still remains as a risk factor for recurrency of PF. Although the number of non-operative treatment options showing efficacy on pain and functional limitation are excessive, the evidences are limited for functional limitation. Additionally, Mulligan mobilization with movement (MMWM) in Chronic Plantar Fasciitis has been poorly studied in the literature. According to these findings, the study was aimed to determine effectiveness of Mulligan mobilization with movement on Chronic Plantar Fasciitis. A total of 25 patients (40 feet) with chronic PF were included in the study. The patients were randomly divided into Mulligan concept rehabilitation group (PF-M, n=20 feet) and Home Rehabilitation group (PF-H, n=20 feet). (MMWM), Foot and ankle exercises program were applied to PF-M, twice a week totally 8 week (16 sessions) and foot- ankle exercises as a home program were given for PF-H, 8 weeks. The range of motion (ROM) for dorsiflexion and plantar flexion was measured by using a manual goniometer. Pain, disability and activity restriction were assessed by Foot Function Index (FFI) . The first step morning pain was evaluated by Visual Analogue Scale (VAS) and Kinesiophobia was also reported by using Tampa Scale (TSK). Patients were evaluated at baseline and 8 weeks. FFI, VAS, TSK, ROM values improved in all groups (intragroup variability) at 8th week (P < .05). The other result indicated that ROM values for DF and PF and TSK scores in PF-M had more significant improvement than PF-H (p<.05). To the best of our knowledge this is the first randomised controlled trial for investigating Mulligan Concept efficiancy on chronic PF. Both Mulligan mobilization with movement (MMWM) and exercise protocols are effective for chronic PF. Furthermore, The Mulligan concept seems more effective treatment option in reducing kinesiophobia and improving functional capacity