A chin deformity does not rarely accompany dentofacial deformities. A correction of the chin (genioplasty) is sometimes indicated in addition to orthognathic surgery. Computer-assisted surgery allows virtual planning in orthognathic surgery in three-dimensions (3D). The virtually planned movements will be transferred to the patients through 3D-printed devices, such as occlusal splints for the maxillomandibular complex. Computer-assisted surgery also enables the evaluation and quantification of the surgical result in 3D, through comparison of the virtual surgical planning and postoperative imaging. The OrthoGnathicAnalyser is a software tool that enables semi-automatic quantification of the surgical result for repositioning of the mandible, maxilla and rami in orthognathic surgery. The goal of this master thesis was to validate the application of 3D-printed guidance systems for the execution of genioplasties in the context of orthognathic surgery with a new version of OrthoGnathicAnalyser. A new version of OrthoGnathicAnalyser was developed, to allow for analysis of the genioplasty. The effect of two factors (calculation of chin analysis and registration technique) on the precision and accuracy has been evaluated. This has led to two important conclusions which are implemented in the software: 1) The difference between the postoperative chin and the planned chin with respect to the realized mandible (instead of the planned mandible) should be calculated, to isolate the mandibular positioning error from the chin error. 2) Surface-based matching resulted in more accurate pitch values and was therefore implemented in the newest version.To present and validate the newly developed software, a multicenter study was executed. A total of 25 patients were included in the study. The inter-observer and intra-observer reliability were evaluated. It was concluded that the reported results demonstrated an excellent reproducibility (ICC >0.92) of the quantification of the skeletal movements between two image sets by the OrthoGnathicAnalyser 2.0. By implementing the chin analysis in the software tool, the complete orthognathic surgery result could be quantified and compared to the virtual plan. The results of this study will be submitted to a scientific journal.  To validate the 3D-printed guidance system for genioplasties, a multicenter randomized controlled intervention study was initiated. To acquire ethical approval of the local ethics committee, extensive preparations were required. Approval to start the study has been acquired within this graduation project and patient inclusion will start when COVID-19 circumstances allow continuation of clinical studies and regular patient care. To validate 3D-printed guidance systems for the execution of genioplasties different projects were undertaken. A new version of the OrthoGnathicAnalyser was developed and validated. Advantageous of the OrthoGnathicAnalyser 2.0 was the implementation of the chin analysis, independence on any planning software and reduced manual input. Possible improvements were the implementation of automatic 3D landmarking and adapting the voxel-based matching algorithm to allow exclusion of the fixation material. A multicenter intervention study to evaluate the accuracy of the 3D-printed guidance system for genioplasties with the newly developed software was initiated. This study will enable a definitive conclusion about the effect of the 3D-printed guidance system on the accuracy of chin repositioning.

Tooth removal is one of the most performed surgical procedures worldwide. Despite the high amount of tooth removal procedures carried out each year, scientific understanding of these procedures is not present. Knowledge of force and torque behaviour is limited and knowledge about movements has never been subject to scientific research before. This study is an initial attempt to describe the factors that influence tooth removal in terms of forces, torques and movements. In-vitro measurements were performed that resulted in a dataset containing force, torque and movement time series of 181 human demonstrations of tooth extractions. This report showed how feature engineering and classification modelling were employed to find tooth removal explaining parameters in the dataset. The feature engineering process led to numerical features describing the force and movement (rotation) time series. The rotation features were found to be most descriptive in describing differences in tooth removal procedures. This introduced five distinct rotation strategies that grouped the human demonstrations based on similarity of extraction strategy. These groups have been used as classification labels in the supervised learning process. A Naive Bayes algorithm and a Logistic Regression algorithm were implemented as prediction models. These models showed that while the rotation features contributed the most to the prediction performance, there was need for additional force features to reach maximum prediction performance. The results showed how feature engineering and classification modelling are the first steps in understanding the procedure of tooth removal.

Whilst the extraction of teeth (exodontia) remains one of the oldest and most performed surgeries on earth, very little is understood about the procedure itself. Especially in the area of the required movements, torques and forces to remove specific teeth and how these interact with existing tissue. This knowledge gap has been hypothesized to contribute to an increasing referral rate towards Oral and Maxillofacial Surgery (OMFS) practices in the Netherlands for simple extractions due to low confidence in young dentists. The objective of this project is to apply techniques used in imitation-learning in the field of robotics to deconstruct complex movement into smaller fundamental building blocks called movement primitives (MPs) to deepen the understanding of exodontia. To achieve this an existing dataset was used consisting of high resolution force-, torque-, position- and rotation-data of extractions. This dataset was collected using a measurement setup consisting of fresh frozen cadaver material, a force torque sensor and a robotic arm in gravity compensation mode. In this paper a novel iterative two staged method for identifying movement primitives is introduced and employed to extract movement primitive information from these extractions in the dataset.