Suppr超能文献

基于锥形束计算机断层扫描的嗅窝解剖学评估

Cone Beam Computed Tomography-Based Anatomical Assessment of the Olfactory Fossa.

作者信息

Costa Andre Luiz Ferreira, Paixão Aline Kataki, Gonçalves Bianca Costa, Ogawa Celso Massahiro, Martinelli Thiago, Maeda Fernando Akio, Trivino Tarcila, Lopes Sérgio Lucio Pereira de Castro

机构信息

Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo, SP, Brazil.

Department of Diagnosis and Surgery, São José dos Campos Dental School, São Paulo State University (UNESP), São José dos Campos, SP, Brazil.

出版信息

Int J Dent. 2019 Apr 1;2019:4134260. doi: 10.1155/2019/4134260. eCollection 2019.

Abstract

This study aimed to investigate the olfactory fossa according to the Keros classification using cone beam computed tomography. This cross-sectional study analysed cone beam computed tomography images selected from a database belonging to a radiology centre. The scans of 174 healthy patients were analysed by using the Xoran software. Gender, age, and side were correlated with the Keros classification. The mean age of the 174 patients was 45.3 years. The most prevalent Keros classification was type II (65.52%), followed by type III (20.69%) and type I (13.79%). No significant differences were found between Keros classification and the variables age, right side ( value = 0.4620), and left side ( value = 0.5709). There were also no significant differences between gender and the variables right side ( value = 0.1421) and left side ( value = 0.2136). Based on these results, we suggest that cone beam computed tomography can be recommended for analysis of the anterior skull base. Keros type II was the most prevalent type in our sample.

摘要

本研究旨在使用锥形束计算机断层扫描根据凯罗斯分类法对嗅窝进行研究。这项横断面研究分析了从一个放射学中心的数据库中选取的锥形束计算机断层扫描图像。使用Xoran软件对174例健康患者的扫描图像进行分析。将性别、年龄和左右侧与凯罗斯分类法进行关联分析。174例患者的平均年龄为45.3岁。最常见的凯罗斯分类为II型(65.52%),其次是III型(20.69%)和I型(13.79%)。在凯罗斯分类与年龄、右侧( 值 = 0.4620)和左侧( 值 = 0.5709)变量之间未发现显著差异。在性别与右侧( 值 = 0.1421)和左侧( 值 = 0.2136)变量之间也未发现显著差异。基于这些结果,我们建议锥形束计算机断层扫描可用于分析前颅底。在我们的样本中,凯罗斯II型是最常见的类型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8052/6470455/8915b574e6bc/IJD2019-4134260.001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验