Aims. The first metatarsal pronation deformity of hallux valgus feet is widely recognized. However, its assessment relies mostly on 3D standing CT scans. Two radiological signs, the first metatarsal round head (RH) and inferior tuberosity position (ITP), have been described, but are seldom used to aid in diagnosis. This study was undertaken to determine the reliability and validity of these two signs for a more convenient and affordable preoperative assessment and postoperative comparison. Methods. A total of 200 feet were randomly selected from the radiograph archives of a foot and ankle clinic. An anteroposterior view of both feet was taken while standing on the same x-ray platform. The intermetatarsal angle (IMA), metatarsophalangeal angle (MPA), medial sesamoid position, RH, and ITP signs were assessed for statistical analysis. Results. There were 127 feet with an IMA > 9°. Both RH and ITP severities correlated significantly with IMA severity. RH and ITP were also significantly associated with each other, and the pronation deformities of these feet are probably related to extrinsic factors. There were also feet with discrepancies between their RH and ITP severities, possibly due to intrinsic torsion of the first metatarsal. Conclusion. Both RH and ITP are reliable first metatarsal pronation signs correlating to the metatarsus primus varus deformity of hallux valgus feet. They should be used more for preoperative and postoperative assessment. Cite this article: Bone Jt Open 2024;5(11):1037–1040

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. 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. The arch of the foot has been described as a truss where the plantar fascia (PF) acts as the tensile element. Its role in maintaining the arch has likely been underestimated because it only rarely torn in patients with progressive collapsing foot deformity (PCFD). We hypothesized that elongation of the plantar fascia would be a necessary and sufficient precursor of arch collapse. Method. We used a validated finite element model of the foot reconstructed from CT scan of a female cadaver. Isolated and combined simulated ligament transection models were created for each combination of the ligaments. A collapsed foot model was created by simulated transection of all the arch supporting ligaments and unloading of the posterior tibial tendon. Foot alignment angles, changes in force and displacement within each of the ligaments were compared between the intact, isolated ligament transection, and complete collapse conditions. Result. Isolated release of the PF did not cause deformity, but lead to increased force in the long (142%) and short plantar (156%), deltoid (45%), and spring ligaments (60%). The PF was the structure most able to prevent arch collapse and played a secondary role in preventing hindfoot valgus and forefoot abduction deformities. Arch collapse was associated with substantial attenuation of the spring (strain= 41%) and interosseous talocalcaneal ligaments (strain= 27%), but only a small amount in the plantar fascia (strain= 10%). Conclusion. Isolated PF release did not cause arch collapse, but arch collapse could not occur without at least 10% elongation of the PF. Simulated transection of the PF led to substantial increase in the force in the other arch supporting ligaments, putting the foot at risk of arch collapse over time. Chronic degeneration of the PF leading to plantar fasciitis may be an early sign of impending PCFD

Introduction. The human wrist is a highly complex joint, offering extensive motion across various planes. This study investigates scapholunate ligament (SLL) injuries’ impact on wrist stability and arthritis risks using cadaveric experiments and the finite element (FE) method. It aims to validate experimental findings with FE analysis results. Method. The study utilized eight wrist specimens on a custom rig to investigate Scapho-Lunate dissociation. Contact pressure and flexion were measured using sensors. A CT-based 3D geometry reconstruction approach was used to create the geometries needed for the FE analysis. The study used the Friedman test with pairwise comparisons to assess if differences between testing conditions were statistically significant. Result. The study found significant variations in scaphoid and lunate bone movement based on ligament condition. Full tears increased scapholunate distance in the distal-proximal direction and decreased in the medial-lateral direction. Lunate angles shifted from flexion to extension with fully torn ligaments. Conversely, the scaphoid shifted significantly from extension to flexion with full tears. A proximal movement was observed in the distal-proximal direction in all groups, with significant differences in the partial tear group. Lateral deviation of the scaphoid and lunate occurred with ligament damage, being more pronounced in the partial tear group. All groups exhibited statistically significant movement in the volar direction, with the full tear group showing the least movement. Also, radiocarpal joint and finger contact pressure and contact area were studied. Whereas the differences in contact area were not significant, scapholunate ligament tears resulted in significantly decreased finger contact pressures. FEA confirmed these findings, showing notable peak radiocarpal contact pressure differences between intact and fully torn ligaments. Conclusion. Our study found that SLL damage alters wrist stability, potentially leading to early arthritis. The FEA model confirmed these findings, indicating the potential for the clinical use of computer models from CT scans for treatment planning

Introduction. Kienböck's disease is generally defined as the collapse of the lunate bone, and this may lead to early wrist osteoarthritis. Replacing the collapsed lunate with an implant has regained renewed interest with the advancing technology of additive manufacturing, enabling the design of patient-specific implants. The aims of this project are (1) to determine how accurate it is to use the contralateral lunate shape as a template for patient-specific lunate implants, and (2) to study the effects of shape variations wrist kinematics using 4D-computed tomography (CT) scanning. Methods. A 3D statistical shape model (SSM) of the lunate was built based on bilateral CT scans of 54 individuals. Using SMM, shape variations of the lunate were identified and the intra- and inter-subject shape variations were compared by performing an intraclass correlation analysis. A radiolucent motor-controlled wrist-holder was designed to guide flexion/extension and radial/ulnar deviation of ex vivo wrist specimens under 4D-CT scanning. In this pilot, three shape mode variations were tested per specimen in two specimens were. After post-processing each CT, the scapholunate angle (SLA) and capitolunate angle (CLA) were measured. Results. The shape of the lunate was not symmetrical, defined as exceeding the intra-subject variation in five different shape modes. The FE tests show a generalized increase in scapholunate and capitolunate angle when using lunate implants, and comparing variation of shape modes showed that shape mode 3 has a significant effect on the measured angles (p<0.05). Discussion. The design of patient-specific lunate implants may prove to be challenging using a ‘mirror’-design as it will lead to a degree of shape asymmetry. The pilot study, to determine the effects of those shape variations on wrist kinematics suggest that the degree of shape variation observed indeed may alter the wrist kinematics, although this needs to be further investigated in study using more specimens

Introduction. Cartilage damage is a critical aspect of osteoarthritis progression, but effective imaging strategies remain limited. Consequently, multimodal imaging approaches are receiving increased attention. Gold nanomaterials, renowned for their therapeutic and imaging capabilities, hold promise in drug development. However, their potential for cartilage imaging is rarely discussed. Here, we developed a versatile nanomaterial, AuNC@BSA-Gd-I, for cartilage detection. By leveraging electrostatic interactions with sulfated glycosaminoglycans (sGAG), the AuNC@BSA-Gd-I can effectively penetrate damaged cartilage while accumulating minimally in healthy cartilage. This probe can be visualized or detected using CT, MRI, IVIS, and a gamma counter, providing a comprehensive approach to cartilage imaging. Additionally, we compared the imaging abilities, cartilage visualization capacities, and versatility of currently disclosed multimodal gold nanomaterials with those of AuNC@BSA-Gd-I. Method. The physicochemical properties of nanomaterials were measured. The potential for cartilage visualization of these nanomaterials was assessed using an in vitro porcine model. The sGAG content in cartilage was determined using the dimethylmethylene blue (DMMB) assay to establish the correlation between sGAG concentration and imaging intensity acquired at each modality. Results. The cartilage imaging abilities of AuNC@BSA-Gd-I for CT, MRI, and optical imaging were verified, with each imaging intensity demonstrating a strong correlation with the sGAG content (MRI; R2=0.93, CT; R2=0.83, IVIS; R2=0.79). Furthermore, AuNC@BSA-Gd-. 131. I effectively accumulated in defective cartilage tissue compared to healthy cartilage (23755.38 ± 5993.61 CPM/mg vs. 11699.97 ± 794.93 CPM/mg). Additionally, current gold nanomaterials excelled in individual imaging modalities but lacked effective multimodal imaging ability. Conclusion. Compared to current multimodal gold nanomaterials, AuNC@BSA-Gd-I demonstrates the potential to image cartilage across multiple medical instruments, providing investigators with a more powerful, visible, and convenient approach to detect cartilage defects. Acknowledgements. This work was financially supported by the National Health Research Institute, Taiwan (NHRI-EX112-11029SI), the National Science and Technology Council (NSTC 112-2314-B-182A-105-MY3), and Chang Gung Memorial Hospital, Taiwan (CMRPG8N0781 and CMRPG8M1281-3)

Introduction. Anterior shoulder instability results in labral and osseous glenoid injuries. With a large osseous defect, there is a risk of recurrent dislocation of the joint, and therefore the patient must undergo surgical correction. An MRI evaluation of the patient helps to assess the soft tissue injury. Currently, the volumetric three-dimensional (3D) reconstructed CT image is the standard for measuring glenoid bone loss and the glenoid index. However, it has the disadvantage of exposing the patient to radiation and additional expenses. This study aims to compare the values of the glenoid index using MRI and CT. Method. The present study was a two-year cross-sectional study of patients with shoulder pain, trauma, and dislocation in a tertiary hospital in Karnataka. The sagittal proton density (PD) section of the glenoid and enface 3D reconstructed images of the scapula were used to calculate glenoid bone loss and the glenoid index. The baseline data were analyzed using descriptive statistics, and the Chi-square test was used to test the association of various complications with selected variables of interest. Result. The glenoid index calculated in the current study using 3D volumetric CT images and MR sagittal PD images was 0.95±0.01 and 0.95±0.01, respectively. The CT and MRI glenoid bone loss was 5.41±0.65% and 5.38±0.65%, respectively. When compared, the glenoid index and bone loss calculated by MRI and CT revealed a high correlation and significance with a p-value of <0.001. Conclusions. The study concluded that MRI is a reliable method for glenoid measurement. The sagittal PD sequence combined with an enface glenoid makes it possible to identify osseous defects linked to glenohumeral joint damage and dislocation. The values derived from 3D CT are identical to the glenoid index and bone loss determined using the sagittal PD sequence in MRI

Introduction. The surgical treatment of critical-sized bone defects with complex three-dimensional (3D) geometries is a challenge for the treating surgeon. Additive manufacturing such as 3D printing enables the production of highly individualized bone implants meeting the shape of the patient's bone defect and including a tunable internal structure. In this study, we showcase the design process for patient-specific implants with critical-sized tibia defects. Methods. Two clinical cases of patients with critical tibia defects (size 63×20×21 mm and 50×24×17 mm) were chosen. Brainlab software was used for segmentation of CT data generating 3D models of the defects. The implant construction involves multiple stages. Initially, the outer shell is precisely defined. Subsequently, the specified volume is populated with internal structures using Voronoi, Gyroid, and NaCl crystal structures. Variation in pore size (1.6 mm and 1.0 mm) was accomplished by adjusting scaffold size and material thickness. Results. An algorithmic design process in Rhino and Grasshopper was successfully applied to generate model implants for the tibia from Ct data. By integrating a precise mesh into an outer shell, a scaffold with controlled porosity was designed. In terms of the internal design, both Voronoi and Gyroid form macroscopically homogeneous properties, while NaCl, exhibits irregularities in density and consequently, in the strength of the structure. Data implied that Voronoi and Gyroid structures adapt more precisely to complex and irregular outer shapes of the implants. Conclusion. In proof-of-principle studies customized tibia implants were successfully generated and printed as model implants based on resin. Further studies will include more patient data sets to refine the workflows and digital tools for a broader spectrum of bone defects. The algorithm-based design might offer a tremendous potential in terms of an automated design process for 3D printed implants which is essential for clinical application

Introduction. Assessment of the humeral head translation with respect to the glenoid joint, termed humeral head migration (HHM), is crucial in total shoulder arthroplasty pre-operative planning. Its assessment informs current classification systems for shoulder osteoarthritis as well as the evaluation of surgical correction. In current clinical practice, HHM assessment relies on computed-tomography (CT) imaging. However, the associated supine position might undermine its functional relevance as it does not reflect the weight-bearing condition with active muscle engagement associated with the upright standing position of most daily activities. Therefore, we assessed to what extent HHM in a supine position is associated with HHM in a range of functional arm positions. Method. 26 shoulder osteoarthritis patients and 12 healthy volunteers were recruited. 3D shapes of the humerus and scapula were reconstructed from their respective CT scans using an image processing software. 3. , and their CT-scan-based HHMs were measured. Furthermore, all subjects underwent low-dose biplanar radiography . 4. in four quasi-static functional arm positions while standing: relaxed standing, followed by 45 degrees of shoulder extension, flexion, and abduction. Using a previously validated method implemented in the programming platforms. 5. , 3D shapes were registered to the pairs of biplanar images for each arm position and the corresponding functional HHM was measured. Bivariate correlations were assessed between the CT-based HHM and each functional arm position. Result. HHM in 45 degrees of flexion and extension both showed significant and strong correlations (r>0.66 and P<0.01) with HHM assessed in the supine position. However, such a high correlation was not found for relaxed standing and 45 abduction. Conclusion. Although HHM in a supine position correlates with HHM in 45-degree extension and flexion, it is poorly associated with the HHM in abduction and relaxed standing. These results may suggest the inclusion of more functionally-relevant patient positioning toward better-informed shoulder arthroplasty planning. Acknowledgement. Funding from PRosPERos-II Project

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.

Periprosthetic joint infection represents a devastating complication after total elbow arthroplasty. Several measures can be implemented before, during, and after surgery to decrease infection rates, which exceed 5%. Debridement with antibiotics and implant retention has been reported to be successful in less than one-third of acute infections, but still plays a role. For elbows with well-fixed implants, staged retention seems to be equally successful as the more commonly performed two-stage reimplantation, both with a success rate of 70% to 80%. Permanent resection or even amputation are occasionally considered. Not uncommonly, a second-stage reimplantation requires complex reconstruction of the skeleton with allografts, and the extensor mechanism may also be deficient. Further developments are needed to improve our management of infection after elbow arthroplasty.

Cite this article: Bone Joint J 2024;106-B(11):1321–1326.

Aims

Venous thromboembolism (VTE) is a potential complication of foot and ankle surgery. There is a lack of agreement on contributing risk factors and chemical prophylaxis requirements. The primary outcome of this study was to analyze the 90-day incidence of symptomatic VTE and VTE-related mortality in patients undergoing foot and ankle surgery and Achilles tendon (TA) rupture. Secondary aims were to assess the variation in the provision of chemical prophylaxis and risk factors for VTE.

Aims

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.

Aims

The survival of humeral hemiarthroplasties in patients with relatively intact glenoid cartilage could theoretically be extended by minimizing the associated postoperative glenoid erosion. Ceramic has gained attention as an alternative to metal as a material for hemiarthroplasties because of its superior tribological properties. The aim of this study was to assess the in vitro wear performance of ceramic and metal humeral hemiarthroplasties on natural glenoids.

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. Methods. A statistical shape model of the entire humerus and scapula was fitted into ten shoulder CT scans randomly selected from 162 patients who underwent rTSA. Six shoulder surgeons independently planned a rTSA in each model using prototype development software with the ability to adjust for scapulothoracic orientation, the starting position of the humerus, as well as kinematic planes in a global reference system simulating previously described posture types A, B, and C. ROM with and without posture adjustment was calculated and compared in all movement planes. Results. All movement planes showed significant differences when comparing protocols with and without adjustment for posture. The largest mean difference was seen in external rotation, being 62° (SD 16°) without adjustment compared to 25° (SD 9°) with posture adjustment (p < 0.001), with the highest mean difference being 49° (SD 15°) in type C. Mean extension was 57° (SD 18°) without adjustment versus 24° (SD 11°) with adjustment (p < 0.001) and the highest mean difference of 47° (SD 18°) in type C. Mean abducted internal rotation was 69° (SD 11°) without adjustment versus 31° (SD 6°) with posture adjustment (p < 0.001), showing the highest mean difference of 51° (SD 11°) in type C. Conclusion. The present study demonstrates that accounting for scapulothoracic orientation has a significant impact on simulated ROM for rTSA in all motion planes, specifically rendering vastly lower values for external rotation, extension, and high internal rotation. The substantial differences observed in this study warrant a critical re-evaluation of all previously published studies that examined component choice and placement for optimized ROM in rTSA using conventional preoperative planning software. Cite this article: Bone Joint J 2024;106-B(11):1284–1292

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. Methods. We used an active appearance model-based approach to segment the preoperative CT of 21 osteoarthritic patients, which identified the osteophyte-free surfaces and estimated cartilage from the segmented bones; these geometries were used to construct patient-specific musculoskeletal models of the pre-diseased knee. Subsequently, implantations were simulated using the MA method, and a previously developed optimization technique was employed to find the optimal implant position that minimized the root mean square deviation between pre-diseased and postoperative ligament strains and kinematics. Results. There were evident biomechanical differences between the simulated patient models, but also trends that appeared reproducible at the population level. Optimizing the implant position significantly reduced the maximum observed strain root mean square deviations within the cohort from 36.5% to below 5.3% for all but the anterolateral ligament; and concomitantly reduced the kinematic deviations from 3.8 mm (SD 1.7) and 4.7° (SD 1.9°) with MA to 2.7 mm (SD 1.4) and 3.7° (SD 1.9°) relative to the pre-diseased state. To achieve this, the femoral component consistently required translational adjustments in the anterior, lateral, and proximal directions, while the tibial component required a more posterior slope and varus rotation in most cases. Conclusion. These findings confirm that MA-induced biomechanical alterations relative to the pre-diseased state can be reduced by optimizing the implant position, and may have implications to further advance pre-planning in robotic-assisted surgery in order to restore pre-diseased knee function. Cite this article: Bone Joint J 2024;106-B(11):1231–1239