Browsing by Author "Gillingham, Ryan"
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- ItemRetrospective study on mandible morphology towards improving implant design(Stellenbosch : Stellenbosch University, 2019-04) Gillingham, Ryan; Van der Merwe, Johan; Mutsvangwa, Timothy; Stellenbosch University. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering (CRSES)ENGLISH ABSTRACT: Pre-surgery planning is crucial to the success of orthognathic surgery. With the advancement in 3D imaging modalities, modern methods in predicting a pathological mandible’s ideal geometry have improved. As a result, the design of patient-specific implants has become more commonplace. Before this, standard sized implants were inevitably used. Despite these enhanced virtual reconstruction techniques, limitations in these methods still exist. The most effective approach during virtual reconstruction is to replace the pathological area with the unaffected region on the opposite half of the mandible. This mirroring method becomes futile in scenarios where the disturbance overlaps the mandibular midline. Therefore, the aim of this study was to develop a virtual mandibular reconstruction technique for the purpose of aiding surgeons during implant design, whilst accounting for this limitation. It was proposed that this could be achieved by performing a retrospective investigation on the population’s mandibular structure and developing prediction models based on statistical methods. Two prediction models were formulated: a sparse prediction model (SPM) and a statistical shape model (SSM). The SPM offers a prediction of important unknown mandibular measurements when receiving the values of known measurements as an input, whilst the SSM provides an estimate for the full mandibular geometry after receiving mandibular coordinates as an input. The effectiveness of these techniques was tested by predicting missing anatomical features on subjects not part of the dataset used to create the models. The tests took place for two scenarios: the first being for when the plane of symmetry is available and the second for when it’s not. For the first scenario of testing, the mirroring method was also implemented, where the resulting accuracy served as the baseline. For both testing scenarios, the SSM clearly outperformed the SPM. Thus, there is no clear benefit in using the SPM over the SSM for virtual reconstruction scenarios. For the first scenario of testing, the SSM compared similarly to the mirroring method, where no significant difference was found between their respective accuracies (p<0.05). The difference between these two methods lies in their restriction of use. Whilst the mirroring method is constrained to situations such as the first scenario, the SSM has no such restriction. For the second scenario, the SSM produced estimations with accuracies similar to the first scenario, thus producing consistent accuracies in geometry prediction regardless of the area being reconstructed. It was therefore concluded that a SSM of the mandible presents itself as a modular virtual reconstruction technique that successfully accounts for the limitations found in current methods.