Search

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.

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