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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

Adequate soft-tissue dimensions have been shown to be crucial for the long-term success of dental implants. To date, there is evidence that placement of dental implants should only be conducted in an area covered with attached gingiva. Modern implant planning software does not visualize soft-tissue dimensions. This study aims to calculate the course of the mucogingival borderline (MG-BL) using statistical shape models (SSM). Visualization of the MG-BL allows the practitioner to consider the soft tissue supply during implant planning. To deploy an SSM of the MG-BL, healthy individuals were examined and the intra-oral anatomy was captured using an intra-oral scanner (IOS). The empirical anatomical data was superimposed and analyzed by principal component analysis. Using a Leave-One-Out Cross Validation (LOOCV), the prediction of the SSM was compared with the original anatomy extracted from IOS. The median error for MG-BL reconstruction was 1.06 mm (0.49–2.15 mm) and 0.81 mm (0.38–1.54 mm) for the maxilla and mandible, respectively. While this method forgoes any technical work or additional patient examination, it represents an effective and digital method for the depiction of soft-tissue dimensions. To achieve clinical applicability, a higher number of datasets has to be implemented in the SSM.

Purpose Computer-assisted techniques play an important role in craniomaxillofacial surgery. As segmentation of three-dimensional medical imaging represents a cornerstone for these procedures, the present study was aiming at investigating a deep learning approach for automated segmentation of head CT scans. Methods The deep learning approach of this study was based on the patchwork toolbox, using a multiscale stack of 3D convolutional neural networks. The images were split into nested patches using a fixed 3D matrix size with decreasing physical size in a pyramid format of four scale depths. Manual segmentation of 18 craniomaxillofacial structures was performed in 20 CT scans, of which 15 were used for the training of the deep learning network and five were used for validation of the results of automated segmentation. Segmentation accuracy was evaluated by Dice similarity coefficient (DSC), surface DSC, 95% Hausdorff distance (95HD) and average symmetric surface distance (ASSD). Results Mean for DSC was 0.81 ± 0.13 (range: 0.61 [mental foramen] – 0.98 [mandible]). Mean Surface DSC was 0.94 ± 0.06 (range: 0.87 [mental foramen] – 0.99 [mandible]), with values > 0.9 for all structures but the mental foramen. Mean 95HD was 1.93 ± 2.05 mm (range: 1.00 [mandible] – 4.12 mm [maxillary sinus]) and for ASSD, a mean of 0.42 ± 0.44 mm (range: 0.09 [mandible] – 1.19 mm [mental foramen]) was found, with values Conclusion In this study, high accuracy of automated segmentation of a variety of craniomaxillofacial structures could be demonstrated, suggesting this approach to be suitable for the incorporation into a computer-assisted craniomaxillofacial surgery workflow. The small amount of training data required and the flexibility of an open source-based network architecture enable a broad variety of clinical and research applications.

Purpose Modern virtual implant planning is a time-consuming procedure, requiring a careful assessment of prosthetic and anatomical factors within a three-dimensional dataset. In order to facilitate the planning process and provide additional information, this study examines a statistical shape model (SSM) to compute the course of dental roots based on a surface scan. Material and methods Plaster models of orthognathic patients were scanned and superimposed with three-dimensional data of a cone-beam computer tomography (CBCT). Based on the open-source software “R”, including the packages Morpho, mesheR, Rvcg and RvtkStatismo, an SSM was generated to estimate the tooth axes. The accuracy of the calculated tooth axes was determined using a leave-one-out cross-validation. The deviation of tooth axis prediction in terms of angle or horizontal shift is described with mean and standard deviation. The planning dataset of an implant surgery patient was additionally analyzed using the SSM. Results 71 datasets were included in this study. The mean angle between the estimated tooth-axis and the actual tooth-axis was 7.5 ± 4.3° in the upper jaw and 6.7 ± 3.8° in the lower jaw. The horizontal deviation between the tooth axis and estimated axis was 1.3 ± 0.8 mm close to the cementoenamel junction, and 0.7 ± 0.5 mm in the apical third of the root. Results for models with one missing tooth did not differ significantly. In the clinical dataset, the SSM could give a reasonable aid for implant positioning. Conclusions With the presented SSM, the approximate course of dental roots can be predicted based on a surface scan. There was no difference in predicting the tooth axis of existent or missing teeth. In clinical context, the estimation of tooth axes of missing teeth could serve as a reference for implant positioning. However, a higher number of training data must be achieved to obtain increasing accuracy.

Purpose: The known preformed osteosynthesis plates for the midface are helpful tools for a precise and fast fixation of repositioned fractures. The purpose of the current study is to analyze the precision of newly developed prototypes of preformed osteosynthesis plates for the mandible. Methods: Four newly designed preformed osteosynthesis plates, generated by a statistical shape model based on 115 CT scans, were virtually analyzed. The used plates were designed for symphyseal, parasymphyseal, angle, and condyle fractures. Each type of plate has three different sizes. For analysis, the shortest distance between the plate and the bone surface was measured, and the sum of the plate-to-bone distances over the whole surface was calculated. Results: A distance between plate and bone of less than 1.5 mm was defined as sufficient fitting. The plate for symphyseal fractures showed good fitting in 90% of the cases for size M, and in 84% for size L. For parasymphyseal fractures, size S fits in 80%, size M in 68%, and size L in 65% of the cases. Angle fractures with their specific plate show good fitting for size S in 53%, size M in 60%, and size L in 47%. The preformed plate for the condyle part fits for size S in 75%, for size M in 85%, and for size L in 74% of the cases. Conclusion: The newly developed mandible plates show sufficient clinical fitting to ensure adequate fracture reduction and fixation.

Defence to pathogens must be specific. In the past, we have dissected early signalling deployed by bacterial elicitors in a grapevine cell system. In the current work, we asked, how defence of fungi differs. Fungal diseases of grapevine pose great challenges for global viticulture and require massive plant protection measures. Plant cells are able to sense chitin, a central component of fungal cell walls and respond by activation of basal defence. We, therefore mapped early defence responses evoked by chitosan, a chitin fragment able to bind to chitin receptors. We found an activation of calcium influx, monitored by extracellular alkalinisation due to a co-transport of protons, remodelling of actin (but not of microtubules), and the activation of transcripts for phytoalexin synthesis, jasmonate-signalling, salicylate signalling, and chitinase. Interestingly, Gadolinium, an inhibitor of calcium influx, can inhibit extracellular alkalinisation in response to chitosan, while the induction of the phytoalexin synthesis transcripts was specifically promoted. In contrast, both DMSO and benzyl alcohol, compounds known to modulate membrane fluidity, partially inhibited the transcript responses to chitosan. We discuss these data with a model, where chitosan deploys signalling culminating in activation of defence related transcripts, but at the same time activates calcium influx that negatively feeds back on the same signal chain, which might be a mechanism to achieve a temporal signature that is rapid, but transient.

Virtual reconstruction of large cranial defects is still a challenging task. The current reconstruction procedures depend on the surgeon’s experience and skills in planning the reconstruction based on mirroring and manual adaptation. The aim of this study is to propose and evaluate a computer-based approach employing a statistical shape model (SSM) of the cranial vault. An SSM was created based on 131 CT scans of pathologically unaffected adult crania. After segmentation, the resulting surface mesh of one patient was established as template and subsequently registered to the entire sample. Using the registered surface meshes, an SSM was generated capturing the shape variability of the cranial vault. The knowledge about this shape variation in healthy patients was used to estimate the missing parts. The accuracy of the reconstruction was evaluated by using 31 CT scans not included in the SSM. Both unilateral and bilateral bony defects were created on each skull. The reconstruction was performed using the current gold standard of mirroring the intact to the affected side, and the result was compared to the outcome of our proposed SSM-driven method. The accuracy of the reconstruction was determined by calculating the distances to the corresponding parts on the intact skull. While unilateral defects could be reconstructed with both methods, the reconstruction of bilateral defects was, for obvious reasons, only possible employing the SSM-based method. Comparing all groups, the analysis shows a significantly higher precision of the SSM group, with a mean error of 0.47 mm compared to the mirroring group which exhibited a mean error of 1.13 mm. Reconstructions of bilateral defects yielded only slightly higher estimation errors than those of unilateral defects. The presented computer-based approach using SSM is a precise and simple tool in the field of computer-assisted surgery. It helps to reconstruct large-size defects of the skull considering the natural asymmetry of the cranium and is not limited to unilateral defects.