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眶部和鼻部参数的人体测量学分析用于性别二态性:通过回顾性观察研究在个人识别领域的新解剖学证据。

Anthropometric analysis of orbital and nasal parameters for sexual dimorphism: New anatomical evidences in the field of personal identification through a retrospective observational study.

机构信息

Department of Law, Institute of Legal Medicine (AgEstimation Project), University of Macerata, Macerata, Italy.

Faculty of Medicine, Department of Orthodontic, University of Prishtina, Alma Mater Europaea, Campus Rezonanca, Prishtina, Kosovo.

出版信息

PLoS One. 2023 May 3;18(5):e0284219. doi: 10.1371/journal.pone.0284219. eCollection 2023.

Abstract

Orbital and nasal parameters among modern humans show considerable variation, which affects facial shape, and these characteristics vary according to race, region, and period in evolution. The aim of the study was to ascertain whether there are sex differences in the orbital and/or nasal indexes and/or the single measurements used to calculate these in a Kosovar population. The following parameters were taken into consideration: orbital height (OH), orbital width (OW), nasal height (NH), and nasal width (NW). The ratios between orbital index/nasal index (RONI) were calculated. All measurements were obtained from a population sample comprising 408 individuals. The accuracy in sex prediction was 52.86% (CI95% = 45.05%-60.67%) for NW and 64.96% for NH (CI95% = 57.50%- 72.42%). The difference between male and female indexes was statistically significant (P < 0.05). The anthropometric study revealed that only NW and NH are configured as predictors of sexual dimorphism. It could be useful to increase the number of samples to test the discriminant function in other population groups.

摘要

现代人的眼眶和鼻腔参数存在相当大的差异,这些差异会影响面部形状,而这些特征会因种族、地区和进化时期的不同而有所不同。本研究旨在确定在科索沃人群中,眼眶和/或鼻腔指数以及用于计算这些指数的单一测量值是否存在性别差异。考虑了以下参数:眼眶高度(OH)、眼眶宽度(OW)、鼻腔高度(NH)和鼻腔宽度(NW)。计算了眼眶指数/鼻腔指数(RONI)的比值。所有测量值均来自一个由 408 人组成的人群样本。NW 的性别预测准确率为 52.86%(95%CI = 45.05%-60.67%),NH 的准确率为 64.96%(95%CI = 57.50%-72.42%)。男女指数之间的差异具有统计学意义(P < 0.05)。人体测量学研究表明,只有 NW 和 NH 可作为性别二态性的预测指标。增加样本数量以测试其他人群的判别函数可能会有所帮助。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a77/10155994/a50abc44c219/pone.0284219.g001.jpg

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