000097324 001__ 97324
000097324 005__ 20230622083321.0
000097324 0247_ $$2doi$$a10.29007/hwz8
000097324 0248_ $$2sideral$$a121380
000097324 037__ $$aART-2020-121380
000097324 041__ $$aeng
000097324 100__ $$aPitocchi, J.
000097324 245__ $$aAutomatic muscle elongation measurement during shoulder arthroplasty planning
000097324 260__ $$c2020
000097324 5060_ $$aAccess copy available to the general public$$fUnrestricted
000097324 5203_ $$aAdequate 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.
000097324 536__ $$9info:eu-repo/grantAgreement/EC/H2020/722535/EU/Predictive models and simulations in bone regeneration: a multiscale patient-specific approach/CuraBone$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 722535-CuraBone
000097324 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000097324 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000097324 700__ $$aPlessers, K.
000097324 700__ $$aWesseling, M.
000097324 700__ $$aVan Lenthe, G.H.
000097324 700__ $$0(orcid)0000-0002-2901-4188$$aPerez, M.A.$$uUniversidad de Zaragoza
000097324 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000097324 773__ $$g4 (2020), 237-239$$pEPiC ser. health sci.$$tEPiC series in health sciences$$x2398-5305
000097324 8564_ $$s669765$$uhttps://zaguan.unizar.es/record/97324/files/texto_completo.pdf$$yVersión publicada
000097324 8564_ $$s361551$$uhttps://zaguan.unizar.es/record/97324/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000097324 909CO $$ooai:zaguan.unizar.es:97324$$particulos$$pdriver
000097324 951__ $$a2023-06-21-15:03:05
000097324 980__ $$aARTICLE