97324 20230622083321.0 doi 10.29007/hwz8 sideral 121380 ART-2020-121380 eng Pitocchi, J. Automatic muscle elongation measurement during shoulder arthroplasty planning 2020 Access copy available to the general public Unrestricted Adequate deltoid and rotator cuff lengthening in total shoulder arthroplasty (TSA) is crucial to maximize the postoperative functional outcome and to avoid complications (La¨dermann et al., 2014). 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 (Kaptein & van der Helm, 2004; Marra et al., 2015), the definition of a fast and accurate workflow is still a challenge, due to the large variability in bone shapes. Statistical shape modelling (SSM) has recently been used to automatically indicate landmark on target bones (Plessers et al., 2018). This method is less dependent on shape variability and could overcome the aforementioned limitation in accuracy. Therefore, the goal of this study is to develop and evaluate the accuracy of a novel automatic method for measuring deltoid and rotator cuff elongation during preoperative planning of shoulder arthroplasty, based on a statistical shape modelling approach. info:eu-repo/grantAgreement/EC/H2020/722535/EU/Predictive models and simulations in bone regeneration: a multiscale patient-specific approach/CuraBone This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 722535-CuraBone info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Plessers, K. Wesseling, M. Van Lenthe, G.H. Perez, M.A. Universidad de Zaragoza (orcid)0000-0002-2901-4188 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 4 (2020), 237-239 EPiC ser. health sci. EPiC series in health sciences 2398-5305 669765 http://zaguan.unizar.es/record/97324/files/texto_completo.pdf Versión publicada 361551 http://zaguan.unizar.es/record/97324/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:97324 articulos driver 2023-06-21-15:03:05 ARTICLE