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BACKGROUND: Adequate deltoid and rotator cuff elongation in reverse shoulder arthroplasty is crucial to maximize postoperative functional outcomes and to avoid complications. Measurements of deltoid and rotator cuff elongation during preoperative planning can support surgeons in selecting a suitable implant design and position. Therefore, this study presented and evaluated a fully automated method for measuring deltoid and rotator cuff elongation. METHODS: Complete scapular and humeral models were extracted from computed tomography scans of 40 subjects. First, a statistical shape model of the complete humerus was created and evaluated to identify the muscle attachment points. Next, a muscle wrapping algorithm was developed to identify the muscle paths and to compute muscle lengths and elongations after reverse shoulder arthroplasty implantation. The accuracy of the muscle attachment points and the muscle elongation measurements was evaluated for the 40 subjects by use of both complete and artificially created partial humeral models. Additionally, the muscle elongation measurements were evaluated for a set of 50 arthritic shoulder joints. Finally, a sensitivity analysis was performed to evaluate the impact of implant positioning on deltoid and rotator cuff elongation. RESULTS: For the complete humeral models, all muscle attachment points were identified with a median error < 3.5 mm. For the partial humeral models, the errors on the deltoid attachment point largely increased. Furthermore, all muscle elongation measurements showed an error < 1 mm for 75% of the subjects for both the complete and partial humeral models. For the arthritic shoulder joints, the errors on the muscle elongation measurements were

Custom reverse shoulder implants represent a valuable solution for patients with large bone defects. Since each implant has unique patient-specific features, finite element (FE) analysis has the potential to guide the design process by virtually comparing the stability of multiple configurations without the need of a mechanical test. The aim of this study was to develop an automated virtual bench test to evaluate the initial stability of custom shoulder implants during the design phase, by simulating a fixation experiment as defined by ASTM F2028-14. Three-dimensional (3D) FE models were generated to simulate the stability test and the predictions were compared to experimental measurements. Good agreement was found between the baseplate displacement measured experimentally and determined from the FE analysis (Spearman&rsquo, s rank test, p &lt, 0.05, correlation coefficient &rho, s = 0.81). Interface micromotion analysis predicted good initial fixation (micromotion &lt, 150 &micro, m, commonly used as bone ingrowth threshold). In conclusion, the finite element model presented in this study was able to replicate the mechanical condition of a standard test for a custom shoulder implants.

Despite evidence of the biomechanical role of cortical bone, current state of the art finite element models of the proximal femur built from clinical CT data lack a subject-specific representation of the bone cortex. Our main research hypothesis is that the subject-specific modelling of cortical bone layer from CT images, through a deconvolution procedure known as Cortical Bone Mapping (CBM, validated for cortical thickness and density estimates) can improve the accuracy of CT-based FE models of the proximal femur, currently limited by partial volume artefacts. Our secondary hypothesis is that a careful choice of cortical-specific density-elasticity relationship may improve model accuracy. We therefore: (i) implemented a procedure to include subject-specific CBM estimates of both cortical thickness and density in CT-based FE models. (ii) defined alternative models that included CBM estimates and featured a cortical-specific or an independently optimised density-elasticity relationship. (iii) tested our hypotheses in terms of elastic strain estimates and failure load and location prediction, by comparing with a published cohort of 14 femurs, where strain and strength in stance and fall loading configuration were experimentally measured, and estimated through reference FE models that did not explicitly model the cortical compartment. Our findings support the main hypothesis: an explicit modelling of the proximal femur cortical bone layer including CBM estimates of cortical bone thickness and density increased the FE strains prediction, mostly by reducing peak errors (average error reduced by 30%, maximum error and 95th percentile of error distribution halved) and especially when focusing on the femoral neck locations (all error metrics at least halved). We instead rejected the secondary hypothesis: changes in cortical density-elasticity relationship could not improve validation performances. From these improved baseline strain estimates, further work is needed to achieve accurate strength predictions, as models incorporating cortical thickness and density produced worse estimates of failure load and equivalent estimates of failure location when compared to reference models. In summary, we recommend including local estimates of cortical thickness and density in FE models to estimate bone strains in physiological conditions, and especially when designing exercise studies to promote bone strength. ispartof: BONE vol:136 ispartof: location:United States status: published

It is well founded that the mechanical environment may regulate bone regeneration in orthopedic applications. The purpose of this study is to investigate the mechanical contributions of the scaffold and the host to bone regeneration, in terms of subject specificity, implantation site and sensitivity to the mechanical environment. Using a computational approach to model mechano-driven regeneration, bone ingrowth in porous titanium scaffolds was simulated in the distal femur and proximal tibia of three goats and compared to experimental results. The results showed that bone ingrowth shifted from a homogeneous distribution pattern, when scaffolds were in contact with trabecular bone (max local ingrowth 12.47%), to a localized bone ingrowth when scaffolds were implanted in a diaphyseal location (max local ingrowth 20.64%). The bone formation dynamics revealed an apposition rate of 0.37±0.28%/day in the first three weeks after implantation, followed by limited increase in bone ingrowth until the end of the experiment (12 weeks). According to in vivo data, we identified one animal whose sensitivity to mechanical stimulation was higher than the other two. Moreover, we found that the stimulus initiating bone formation was consistently higher in the femur than in the tibia for all the individuals. Overall, the dependence of the osteogenic response on the host biomechanics means that, from a mechanical perspective, the regenerative potential depends on both the scaffold and the host environment. Therefore, this work provides insights on how the mechanical conditions of both the recipient and the scaffold contribute to meet patient and location-specific characteristics. ispartof: BONE vol:144 ispartof: location:United States status: published

Knowledge about bone morphology and bone quality of the scapula throughout the population is fundamental in the design of shoulder implants. In particular, regions with the best bone stock (cortical bone) are taken into account when planning the supporting screws, aiming for an optimal fixation. As an alternative to manual measurements, statistical shape models (SSMs) have been commonly used to describe shape variability within a population. However, explicitly including cortical thickness information in an SSM of the scapula still remains a challenge. Therefore, the goal of this study is to combine scapular bone shape and cortex morphology in an SSM. First, a method to estimate cortical thickness, based on HU (Hounsfield Unit) profile analysis, was developed and validated. Then, based on the manual segmentations of 32 healthy scapulae, a statistical shape model including cortical information was created and evaluated. Generalization, specificity and compactness were calculated in order to assess the quality of the SSM. The average cortical thickness of the SSM was 2.0 ± 0.63 mm. Generalization, specificity and compactness performances confirmed that the combined SSM was able to capture the bone quality changes in the population. In this work we integrated information on the cortical thickness in an SSM for the scapula. From the results we conclude that this methodology is a valuable tool for automatically generating a large population of scapulae and deducing statistics on the cortex. Hence, this SSM can be useful to automate implant design and screw placement in shoulder arthroplasty. ispartof: COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING vol:23 issue:10 pages:642-648 ispartof: location:England status: published

Adequate deltoid and rotator cuff lengthening in total shoulder arthroplasty (TSA) is crucial to maximize the postoperative functional outcome and to avoid complications. Hence surgeons and patients could benefit from including muscle length information in preoperative planning software.Although different methods have been introduced to automatically indicate patient-specific muscle attachment and wrapping points, the definition of a fast and accurate workflow is still a challenge, due to the large variability in bone shapes. Therefore, the goal of this study is to develop and evaluate the accuracy of a novel method to automatically estimate muscle elongation, based on a statistical shape modelling (SSM) approach.Firstly, humerus and scapula SSMs were used to automatically indicate the attachment points of the main shoulder muscles: subscapularis, supraspinatus, infraspinatus, teres minor and deltoid. Secondly, a wrapping algorithm was applied to identify the points where muscles wrap around bones or potential implants. Finally, the accuracy of the automatically indicated landmarks and its effect on the muscle elongation were evaluated by comparing the manually indicated landmarks with the landmarks identified through the SSM for a set of 40 healthy shoulder CT-scans.The low errors on elongation values suggest that the presented automated workflow is a promising tool for allowing surgeons to evaluate patient-specific muscle elongations during preoperative planning. Although the evaluation was limited to healthy joints, this method allows to easily process large datasets and to potentially find a correlation between muscle elongations and postoperative outcome.