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Computer-aided position planning of miniplates to treat facial bone defects.

ABSTRACT: In this contribution, a software system for computer-aided position planning of miniplates to treat facial bone defects is proposed. The intra-operatively used bone plates have to be passively adapted on the underlying bone contours for adequate bone fragment stabilization. However, this procedure can lead to frequent intra-operatively performed material readjustments especially in complex surgical cases. Our approach is able to fit a selection of common implant models on the surgeon's desired position in a 3D computer model. This happens with respect to the surrounding anatomical structures, always including the possibility of adjusting both the direction and the position of the used osteosynthesis material. By using the proposed software, surgeons are able to pre-plan the out coming implant in its form and morphology with the aid of a computer-visualized model within a few minutes. Further, the resulting model can be stored in STL file format, the commonly used format for 3D printing. Using this technology, surgeons are able to print the virtual generated implant, or create an individually designed bending tool. This method leads to adapted osteosynthesis materials according to the surrounding anatomy and requires further a minimum amount of money and time.

SUBMITTER: Egger J

PROVIDER: S-EPMC5560576 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Project description:BackgroundCollectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt's quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls.ObjectiveThe aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning-based framework for the automated differentiation of DeepGestalt's output on such images.MethodsFrontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists.ResultsWe included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt's high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt's syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt's top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; P<.001). A linear SVM running on DeepGestalt's result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; P<.001).ConclusionsDeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt's results and may help enhance it and similar computer-aided facial phenotyping tools.

Project description:BackgroundReconstruction of soft tissue defects following surgical tumor resection is important for quality of life in cancer patients with oral and oropharyngeal squamous cell carcinoma (SCC). This study presents a novel computer-aided reconstruction of soft tissue (CARST) technology employed with these patients.MethodsWe first described the CARST technology in detail in a report of a 34-year-old male patient with locally invasive right-sided tongue SCC following a nearly total glossectomy and reported the postoperative outcomes. This digital technology was applied to construct a 3D model from CT images, which was used to delineate surgical resection boundaries and design a personalized reconstruction of the soft tissue defect. A nonuniform rational B-spline (NURBS) was generated and applied to transform the 3D model into a 2D flap-cutting guide printed out using a 3D printer. We then reported a case-series study on oral and oropharyngeal SCC patients who were randomly assigned to receive the CARST (n = 15) or a traditional soft tissue reconstruction (n = 15). Clinicopathological features and short- and long-term postoperative outcomes between the two groups were compared.ResultsThe patient with the tongue SCC had a successful CARST following surgical tumor resection without any complications. His speech and swallowing functions recovered well after surgery and he experienced no significant changes to his appearance following recovery. There was no recurrence within a 3-year follow-up period. Results of the case-series study showed that the CARST group had significantly shorter operative and post-operation hospital-stay time, a higher flap utilization rate, and a trend of less and milder postoperative complications, and they experienced no significant difference in intraoperative blood loss and long-term outcomes compared to the traditional group.ConclusionCARST is a safer and more efficient personalized technology of soft tissue reconstruction following surgical tumor resection in patients with oral and oropharyngeal SCC.

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Computer-aided position planning of miniplates to treat facial bone defects.

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