Computed tomographic analysis of maxillary sinus anatomy relevant to sinus lift procedures in edentulous ridges in Taiwanese patients

Shun-Jen Yu,Yi-Hao Lee,Ching-Ping Lin,Aaron Yu-Jen Wu 대한치주과학회 2019 Journal of Periodontal & Implant Science Vol.49 No.4

Purpose: To analyze the maxillary sinus anatomy over edentulous ridges in the bilateral posterior maxillary area in Taiwanese patients using cone-beam computed tomography (CBCT). Methods: In total, 101 anatomical sites from 61 patients, including 32 premolar and 69 molar regions, were analyzed using CBCT. Measurements were made of the width and height of edentulous ridges, the thickness of the lateral wall of the maxillary sinus, and the presence of a sinus septum and the posterior superior alveolar artery (PSAA). A statistical analysis of the measurements was performed, and correlations among the measurements were assessed. Results: The average ridge width was 10.26±3.16 mm, with a significantly greater ridge width in the second molar region than in the premolar region. The mean residual ridge height was 8.55±4.09 mm, and ridge height showed an opposite trend from ridge width for the premolar and molar regions. A sinus septum was present at 5.9% of the sites, and the PSAA was observed in 24.5%. The average thickness of the lateral wall of the maxillary sinus was 2.08±0.94 mm, with no significant difference between the tooth position and lateral wall thickness. Conclusions: This study presents the anatomical features of the maxillary sinus, which should be considered in sinus lift procedures for implant placement, in the Taiwanese population. The use of CBCT is recommended to avoid intraoperative complications.

Computed tomographic analysis of maxillary sinus anatomy relevant to sinus lift procedures in edentulous ridges in Taiwanese patients

Yu, Shun-Jen,Lee, Yi-Hao,Lin, Ching-Ping,Wu, Aaron Yu-Jen Korean Academy of Periodontology 2019 Journal of Periodontal & Implant Science Vol.49 No.4

Purpose: To analyze the maxillary sinus anatomy over edentulous ridges in the bilateral posterior maxillary area in Taiwanese patients using cone-beam computed tomography (CBCT). Methods: In total, 101 anatomical sites from 61 patients, including 32 premolar and 69 molar regions, were analyzed using CBCT. Measurements were made of the width and height of edentulous ridges, the thickness of the lateral wall of the maxillary sinus, and the presence of a sinus septum and the posterior superior alveolar artery (PSAA). A statistical analysis of the measurements was performed, and correlations among the measurements were assessed. Results: The average ridge width was $10.26{\pm}3.16mm$, with a significantly greater ridge width in the second molar region than in the premolar region. The mean residual ridge height was $8.55{\pm}4.09mm$, and ridge height showed an opposite trend from ridge width for the premolar and molar regions. A sinus septum was present at 5.9% of the sites, and the PSAA was observed in 24.5%. The average thickness of the lateral wall of the maxillary sinus was $2.08{\pm}0.94mm$, with no significant difference between the tooth position and lateral wall thickness. Conclusions: This study presents the anatomical features of the maxillary sinus, which should be considered in sinus lift procedures for implant placement, in the Taiwanese population. The use of CBCT is recommended to avoid intraoperative complications.

Progressive failure of metal–composite hybrid wheels under impact

Shun-Fa Hwang,Hui-Lun Yu,Yu-Jen Liu,Yuder Chen,Shih-Chieh Chen,Yueh-Chih Hsieh 대한기계학회 2018 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.32 No.1

An 18-in hybrid wheel consisting of an aluminum alloy disk and a composite rim under 13° impact test is considered in this study. The rim is made of carbon fabric/polyurethane composites fabricated by resin transfer molding. Explicit finite element analysis with progressive failure function is used to investigate the failure situation of the hybrid wheel and the suitable braid angle of the braided composite. The effective elastic constants and strengths of the braided composites predicted by basic finite element analyses are adopted, whereas the true stress–strain curve of aluminum is used for the disk. Simulation results indicate that the aluminum alloy disk is safe after the impact test, whereas the rim is damaged. The volume of failed elements after impact simulation is selected as an indicator to obtain a quantity that can be used to represent the damage situation of the rim. Compared with the test results of a real hybrid wheel with [±30°] fiber angle in the rim, the hybrid wheel with fiber angle in the rim that is greater than [±30°] could pass the impact test.

Comparative Analysis of Logistic Regression, Support Vector Machine and Artificial Neural Network for the Differential Diagnosis of Benign and Malignant Solid Breast Tumors by the Use of Three-Dimensional Power Doppler Imaging

Shou-Tung Chen,Yi-Hsuan Hsiao,Yu-Len Huang,Shou-Jen Kuo,Hsin-Shun Tseng,Hwa-Koon Wu,Dar-Ren Chen 대한영상의학회 2009 Korean Journal of Radiology Vol.10 No.5

Objective: Logistic regression analysis (LRA), Support Vector Machine (SVM) and a neural network (NN) are commonly used statistical models in computeraided diagnostic (CAD) systems for breast ultrasonography (US). The aim of this study was to clarify the diagnostic ability of the use of these statistical models for future applications of CAD systems, such as three-dimensional (3D) power Doppler imaging, vascularity evaluation and the differentiation of a solid mass. Materials and Methods: A database that contained 3D power Doppler imaging pairs of non-harmonic and tissue harmonic images for 97 benign and 86 malignant solid tumors was utilized. The virtual organ computer-aided analysis-imaging program was used to analyze the stored volumes of the 183 solid breast tumors. LRA, an SVM and NN were employed in comparative analyses for the characterization of benign and malignant solid breast masses from the database. Results: The values of area under receiver operating characteristic (ROC) curve, referred to as Az values for the use of non-harmonic 3D power Doppler US with LRA, SVM and NN were 0.9341, 0.9185 and 0.9086, respectively. The Az values for the use of harmonic 3D power Doppler US with LRA, SVM and NN were 0.9286, 0.8979 and 0.9009, respectively. The Az values of six ROC curves for the use of LRA, SVM and NN for non-harmonic or harmonic 3D power Doppler imaging were similar. Conclusion: The diagnostic performances of these three models (LRA, SVM and NN) are not different as demonstrated by ROC curve analysis. Depending on user emphasis for the use of ROC curve findings, the use of LRA appears to provide better sensitivity as compared to the other statistical models. Objective: Logistic regression analysis (LRA), Support Vector Machine (SVM) and a neural network (NN) are commonly used statistical models in computeraided diagnostic (CAD) systems for breast ultrasonography (US). The aim of this study was to clarify the diagnostic ability of the use of these statistical models for future applications of CAD systems, such as three-dimensional (3D) power Doppler imaging, vascularity evaluation and the differentiation of a solid mass. Materials and Methods: A database that contained 3D power Doppler imaging pairs of non-harmonic and tissue harmonic images for 97 benign and 86 malignant solid tumors was utilized. The virtual organ computer-aided analysis-imaging program was used to analyze the stored volumes of the 183 solid breast tumors. LRA, an SVM and NN were employed in comparative analyses for the characterization of benign and malignant solid breast masses from the database. Results: The values of area under receiver operating characteristic (ROC) curve, referred to as Az values for the use of non-harmonic 3D power Doppler US with LRA, SVM and NN were 0.9341, 0.9185 and 0.9086, respectively. The Az values for the use of harmonic 3D power Doppler US with LRA, SVM and NN were 0.9286, 0.8979 and 0.9009, respectively. The Az values of six ROC curves for the use of LRA, SVM and NN for non-harmonic or harmonic 3D power Doppler imaging were similar. Conclusion: The diagnostic performances of these three models (LRA, SVM and NN) are not different as demonstrated by ROC curve analysis. Depending on user emphasis for the use of ROC curve findings, the use of LRA appears to provide better sensitivity as compared to the other statistical models.