Statistical Shape and Bone Property Models of Clinical Populations as the Foundation for Biomechanical Surgical Planning: Application to Shoulder Arthroplasty

dc.contributor.authorSharif-Ahmadian, Azita
dc.contributor.authorBeagley, Aren
dc.contributor.authorPearce, Claire
dc.contributor.authorSaliken, David
dc.contributor.authorAthwal, George S.
dc.contributor.authorGiles, Joshua W.
dc.date.accessioned2023-08-11T04:27:11Z
dc.date.available2023-08-11T04:27:11Z
dc.date.copyright2023en_US
dc.date.issued2023-07-12
dc.description.abstractThis work developed, validated, and compared statistical shape, statistical intensity, and statistical shape and intensity models (SSMs, SIMs, SSIMs) of scapulae from a clinical population. SSMs efficiently describe bone shape variation while SIMs describe bone material property variation, and SSIM's combine description of both variables. This work establishes these models' efficacy and whether they can be used in surgical planning. Models were developed using shoulder arthroplasty data of patients with bone erosion, which is challenging to treat and would benefit from improved surgical planning. Models were created using previously validated nonrigid registration and material property assignment processes that were optimized for scapula characteristics. The models were assessed using standard metrics, anatomical measurements, and correlation analyses. The SSM and SIM specificity and generalization error metrics were 3.4 mm and <1 mm and 184 HU and 156 HU, respectively. The SSIM did not achieve the same level of performance as the SSM and SIM in this study (e.g., shape generalization: SSIM—2.2 mm versus SSM—<1 mm). Anatomical correlation analysis showed that the SSM more effectively and efficiently described shape variation compared to the SSIM. The SSM and SIM modes of variation were not strongly correlated (e.g., rmax = 0.56 for modes explaining ≤2.1% of variance). The SSIM is outperformed by the SSM and SIM and the latter two are not strongly correlated; therefore, using the SSM and SIM in conjunction will generate synthetic bone models with realistic characteristics and thus can be used for biomechanical surgical planning applications.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipNatural Science and Engineering Research Council of Canada Discovery (Grant No. RGPIN-2018-03894; Funder ID: 10.13039/501100000038). Michael Smith Health Research British Columbia Scholar (Award No. SCH-2021-1562; Funder ID: 10.13039/501100000245).en_US
dc.identifier.urihttps://doi.org/10.1115/1.4062709
dc.identifier.urihttp://hdl.handle.net/1828/15254
dc.language.isoenen_US
dc.publisherJournal of Biomechanical Engineeringen_US
dc.rightsAttribution-NonCommercial-NoDerivs 2.5 Canada*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/ca/*
dc.subjectstatistical modelingen_US
dc.subjectshoulder arthroplastyen_US
dc.subjectsurgical planningen_US
dc.subjectmorphological analysisen_US
dc.titleStatistical Shape and Bone Property Models of Clinical Populations as the Foundation for Biomechanical Surgical Planning: Application to Shoulder Arthroplastyen_US
dc.typePostprinten_US

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