A tooth‐supported titanium mesh bending and positioning module for alveolar bone augmentation and improving accuracy

alexandrossfakianakis shared this article with you from Inoreader A tooth‐supported titanium mesh bending and positioning module for alveolar bone augmentation and improving accuracy via Journal of Esthetic and Restorative Dentistry Abstract Objective Guided bone regeneration with titanium mesh is a commonly used bone augmentation technique. However, deformation and sliding may occur during the installation of titanium mesh, which may lead to poor accuracy of bone augmentation. This article presented three cases, which describe a tooth-supported titanium mesh bending and positioning module aiming to improve the precision of bone augmentation. Clinical Considerations After designing the ideal bone increment volume digitally, print out the difference bone module between the ideal and existing bone mass and one or two wings. The wings are supported by the adjacent teeth to show the ideal bone mass in the patients' mouth. Finally, the titanium mesh is bent and installed in the ideal position by the module. Conclusions A favorable outcome has been preliminarily confirmed in these cases, the average vertical bone gain was 4.16 mm and the average horizontal gain was 7.48 mm after 6 months. Using the module in the treatment of patients with bone augmentation can effectively improve the accuracy, the maximum deviation was 1.5 mm and the mean was 0.6 mm. Clinical Significance This study improves the bone augmentation technology with titanium mesh. The titanium mesh is fixed in the ideal position, which facilitates subsequent implantation and denture repair. View on Web

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Automated Detection of GlotticLaryngeal Carcinomain Laryngoscopic Images from a Multicenter Databaseusing a Convolutional Neural Network

alexandrossfakianakis shared this article with you from Inoreader Automated Detection of GlotticLaryngeal Carcinomain Laryngoscopic Images from a Multicenter Databaseusing a Convolutional Neural Network via Clinical Otolaryngology ABSTRACT OBJECTIVE Little is known about the efficacy of using artificial intelligence to identify laryngeal carcinoma from images of vocal lesions taken in different hospitals with multiple laryngoscope systems. This multicenter study aimed to establish an artificial intelligence system and provide a reliable auxiliary tool to screen for laryngeal carcinoma. Study Design Multicenter case-control study Setting Six tertiary care centers Participants Laryngoscopy images were collected from 2179 patients with vocal fold lesions. Outcome Measures An automatic detection system of laryngeal carcinoma was established and used to distinguish malignant and benign vocal lesions in 2179 laryngoscopy images acquired from 6 hospitals with 5 types of laryngoscopy systems. Pathological examination was the gold standard for identifying malignant and benign vocal lesions. Results Out of 89 cases in the malignant group, the classifier was able to correctly identify laryngeal carcinoma in 66 patients (74.16%, sensitivity). Out of 640 cases in the benign group, the classifier was able to accurately assess the laryngeal lesion in 503 cases (78.59%, specificity). Furthermore, the region-based convolutional neural network(R-CNN) classifier achieved an overall accuracy of 78.05%, with a 95.63% negative predictive and a 32.51% positive predictive value for the testing dataset. Conclusion This automatic diagnostic system has the potential to assist clinical laryngeal carcinoma diagnosis which may improve and standardize the diagnostic capacity of laryngologists using different laryngoscopes. This article is protected by copyright. All rights reserved. View on Web

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