Abstract: Background This study evaluated the accuracy of computer-assisted surgery (CAS)-driven DCIA (deep circumflex iliac artery) flap mandibular reconstruction by traditional morphometric methods and geometric morphometric methods (GMM). Methods Reconstruction accuracy was evaluated by measuring distances and angles between bilateral anatomical landmarks. Additionally, the average length of displacements vectors between landmarks was computed to evaluate factors assumed to influence reconstruction accuracy. Principal component analysis (PCA) was applied to unveil main modes of dislocation. Results High reconstruction accuracy could be demonstrated for a sample consisting of 26 patients. The effect of the number of segments and length of defect on reconstruction accuracy were close to the commonly used significance threshold (p = 0.062/0.060). PCA demonstrated displacement to result mainly from sagittal and transversal shifts. Conclusions CAS is a viable approach to achieve high accuracy in mandibular reconstruction and GMM can facilitate the evaluation of factors influencing reconstruction accuracy and unveil main modes of dislocation in this context

Abstract: Purpose Morphological variability of the skull is an important consideration for cranioplasty and implant design. Differences in morphology of the skull based on the ethnicity are known. In a previous study we could show the accuracy and benefits of virtual reconstructions based on a statistical shape model (SSM) for neurocranial defects. As the SSM is trained on European data, the question arises how well this model fares when dealing with patients with a different ethnic background. In this study we aim to evaluate the accuracy and applicability of our proposed method when deploying a cranial SSM generated from European data to estimate missing parts of the neurocranium in a Chinese population. Methods We used the same data and methods as in our previous study and compared the outcomes when applied to Chinese individuals. A large unilateral defect on the right side and a bilateral defect were created. The outer surface of the cranial table was reconstructed from CT scans, meshed with triangular elements, and registered to a template. Principal component analysis together with Thin Plate Spines (TPS) deformation was applied to quantify modes of variation. The mesh to mesh distances between the original defects ́ surfaces and the reconstructed surface were computed. Results Comparing the Chinese test group with the European control group, regarding the entire defect the analysis shows no significant difference for unilateral defects (test vs. control group/0.46 mm ± vs. 0.44 mm). Reconstruction of bilateral defects exhibited only in slightly higher prediction errors than those of unilateral defects (0.49 mm ± vs. 0.45 mm). Conclusion The proposed method shows a high accuracy that seems to be ethnical independent - with low error margins for virtual skull reconstruction and implant design

Abstract: Background Computer Assisted Design and Computer Assisted Manufacturing (CAD/CAM) have revolutionized oncologic surgery of the head and neck. A multitude of benefits of this technique has been described, but there are only few reports of donor site comorbidity following CAD/CAM surgery. Methods This study investigated comorbidity of the hip following deep circumflex iliac artery (DCIA) graft raising using CAD/CAM techniques. A cross-sectional examination was performed to determine range of motion, muscle strength and nerve disturbances. Furthermore, correlations between graft volume and skin incision length with postoperative donor site morbidity were assessed using Spearman's rank correlation, linear regression and analysis of variance (ANOVA). Results Fifteen patients with a mean graft volume of 21.2 ± 5.7 cm3 and a mean incision length of 228.0 ± 30.0 mm were included. Patients reported of noticeable physical limitations in daily life activities (12.3 ± 11.9 weeks) and athletic activities (38.4 ± 40.0 weeks in mean) following surgery. Graft volume significantly correlated with the duration of the use of walking aids (R = 0.57; p = 0.033) and impairment in daily life activities (R = 0.65; p = 0.012). The length of the scar of the donor-site showed a statistically significant association with postoperative iliohypogastric nerve deficits (F = 4.4, p = 0.037). Patients with anaesthaesia of a peripheral cutaneous nerve had a larger mean scar length (280 ± 30.0 mm) than subjects with hypaesthesia (245 ± 10.1 mm) or no complaints (216 ± 27.7 mm). Conclusions Despite sophisticated planning options in modern CAD/CAM surgery, comorbidity of the donor site following iliac graft harvesting is still a problem. This study is the first to investigate comorbidity after DCIA graft raising in a patient group treated exclusively with CAD/CAM techniques. The results indicate that a minimal invasive approach in terms of small graft volumes and small skin incisions could help to reduce postoperative symptomatology. Trial registration Retrospectively registered at the German Clinical Trials Register (DRKS-ID: DRKS00029066); registration date: 23/05/2022

Abstract: Fused filament fabrication (FFF) represents a straightforward additive manufacturing technique applied in the medical sector for personalized patient treatment. However, frequently processed biopolymers lack sufficient thermal stability to be used as auxiliary devices such as surgical guides. The aim of this study was to evaluate the dimensional accuracy of experimental biocopolyester blends with improved thermal characteristics after printing, annealing and sterilization. A total of 160 square specimens and 40 surgical guides for oral implant placement were printed. One subgroup of each material (n = 10) underwent thermal annealing before both subgroups were subjected to steam sterilization (134 °C; 5 min). Specimens were digitized and the deviation from the original file was calculated. The thermal behavior was analyzed using differential scanning calorimetry and thermogravimetric analysis. A one-way ANOVA and t-tests were applied for statistical analyses (p

Abstract: Objectives This study evaluated the accuracy of deep neural patchworks (DNPs), a deep learning-based segmentation framework, for automated identification of 60 cephalometric landmarks (bone-, soft tissue- and tooth-landmarks) on CT scans. The aim was to determine whether DNP could be used for routine three-dimensional cephalometric analysis in diagnostics and treatment planning in orthognathic surgery and orthodontics. Methods: Full skull CT scans of 30 adult patients (18 female, 12 male, mean age 35.6 years) were randomly divided into a training and test data set (each n = 15). Clinician A annotated 60 landmarks in all 30 CT scans. Clinician B annotated 60 landmarks in the test data set only. The DNP was trained using spherical segmentations of the adjacent tissue for each landmark. Automated landmark predictions in the separate test data set were created by calculating the center of mass of the predictions. The accuracy of the method was evaluated by comparing these annotations to the manual annotations. Results: The DNP was successfully trained to identify all 60 landmarks. The mean error of our method was 1.94 mm (SD 1.45 mm) compared to a mean error of 1.32 mm (SD 1.08 mm) for manual annotations. The minimum error was found for landmarks ANS 1.11 mm, SN 1.2 mm, and CP_R 1.25 mm. Conclusion: The DNP-algorithm was able to accurately identify cephalometric landmarks with mean errors