Sex determination method using the third cervical vertebral body by head and neck CT

IF 2.6 Q1 DENTISTRY, ORAL SURGERY & MEDICINE
Akihiro Ochiai , Atsushi Iwawaki , Yusei Otaka , Takeru Ishii , Kota Ozawa , Yuko Otomo , Shinji Kito , Hideki Saka
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引用次数: 0

Abstract

Objectives

This study aimed to measure the volume of the third cervical vertebra using head and neck multi-detector computed tomography (MDCT) and establish a sex determination model based on sex differences in volume.

Methods

Head and neck CT images of 85 patients were obtained for dental diagnostic and therapeutic purposes. Digital Imaging and Communications in Medicine (DICOM) data obtained from head and neck CT were constructed using a three-dimensional image analysis software. A region of interest was created that included the entire third cervical vertebra and its volume was measured. Descriptive statistics were calculated for the measurements, and the means, standard deviations, and medians of the measurements were calculated separately for men and women. To create a sex determination model, binomial logistic regression analysis was performed using the training data group, with vertebral volume and sex as the explanatory and objective variables, respectively.

Results

The mean score of men was significantly higher than that of women. The sex determination model using the volume of the third cervical vertebral body resulted in an 80% correct sex classification rate.

Conclusion

The sex determination model can be used with other regional methods to aid sex determination.
第三颈椎椎体通过头颈部CT确定性别的方法。
目的:本研究旨在利用头颈部多探测器计算机断层扫描(MDCT)测量第三颈椎体积,并建立基于体积性别差异的性别判定模型。方法:对85例患者的头颈部CT图像进行临床诊断和治疗。使用三维图像分析软件构建头颈部CT的医学数字成像与通信(DICOM)数据。创建了一个包括整个第三颈椎的感兴趣区域,并测量了其体积。对测量结果进行描述性统计,并分别计算男性和女性测量结果的平均值、标准差和中位数。为了建立性别决定模型,使用训练数据组进行二项逻辑回归分析,分别以椎体体积和性别作为解释变量和客观变量。结果:男性的平均得分明显高于女性。使用第三颈椎体积的性别决定模型的性别分类正确率为80%。结论:该性别决定模型可与其他区域性方法一起辅助性别决定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Oral Biosciences
Journal of Oral Biosciences DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
4.40
自引率
12.50%
发文量
57
审稿时长
37 days
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