Caroline Paula Alves , Claudio Costa , Edgard Michel-Crosato , Maria Gabriela Haye Biazevic
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(2018), the area (S) and the maximum frontal sinus height and width (AB and EF, respectively) were measured using computer-aided design software; and the ratio between AB and EF was taken as the frontal sinus index (R). The discriminant function developed by the authors was then applied to evaluate sexual dimorphism in the Brazilian population. Descriptive statistics were performed for the variables according to gender, as well as Student's t-test and the Mann-Whitney test to see if there was a difference between the variables. A new discriminant formula was calculated with the study data and machine learning techniques, neural networks and decision trees, were used to improve the prediction of sex. The variables showed significant differences in relation to gender, and with the exception of R, where the male mean was 2.00 and the female mean was 2.40, all the means were higher for males. The original formula of the study had low accuracy, with a level of accuracy of only 8.33% for females. However, the formula calculated for Brazilians presented an accuracy of 70.20%; of the machine learning techniques, only the neural network presented a higher value than the one already obtained, of 73.30%. In conclusion, the new formula showed an accuracy of 70.20% and can be applied as an auxiliary method in the assessment of frontal sinus sexual dimorphism in Brazilian adults.</p></div>","PeriodicalId":40763,"journal":{"name":"Forensic Imaging","volume":"33 ","pages":"Article 200548"},"PeriodicalIF":0.8000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Use of the frontal sinus to evaluate sexual dimorphism in a Brazilian sample\",\"authors\":\"Caroline Paula Alves , Claudio Costa , Edgard Michel-Crosato , Maria Gabriela Haye Biazevic\",\"doi\":\"10.1016/j.fri.2023.200548\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The frontal sinuses are commonly used in sexual estimation due to the considerable variation in size, shape and number. Previous studies have shown average accuracy measuring frontal sinus area, height and width; however, authors have associated such measurements with the frontal sinus index, obtaining better results. Therefore, the aim of the present study was to evaluate sexual dimorphism of the frontal sinus in Brazilian adults. The sample consisted of 255 lateral cephalometric radiographs of subjects between 20 and 40 years of age, 132 females and 123 males. Based on the methodology of Luo et al. (2018), the area (S) and the maximum frontal sinus height and width (AB and EF, respectively) were measured using computer-aided design software; and the ratio between AB and EF was taken as the frontal sinus index (R). The discriminant function developed by the authors was then applied to evaluate sexual dimorphism in the Brazilian population. Descriptive statistics were performed for the variables according to gender, as well as Student's t-test and the Mann-Whitney test to see if there was a difference between the variables. A new discriminant formula was calculated with the study data and machine learning techniques, neural networks and decision trees, were used to improve the prediction of sex. The variables showed significant differences in relation to gender, and with the exception of R, where the male mean was 2.00 and the female mean was 2.40, all the means were higher for males. The original formula of the study had low accuracy, with a level of accuracy of only 8.33% for females. However, the formula calculated for Brazilians presented an accuracy of 70.20%; of the machine learning techniques, only the neural network presented a higher value than the one already obtained, of 73.30%. 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引用次数: 0
摘要
额窦在大小、形状和数量上有很大的差异,因此通常用于性别评估。先前的研究表明,测量额窦面积、高度和宽度的准确度平均;然而,作者将这种测量与额窦指数联系起来,获得了更好的结果。因此,本研究的目的是评估巴西成年人额窦的两性异形。样本包括255张20至40岁受试者、132名女性和123名男性的侧位头影测量照片。基于Luo et al。(2018),使用计算机辅助设计软件测量面积(S)和最大额窦高度和宽度(分别为AB和EF);以AB与EF之比作为额窦指数(R)。然后,作者开发的判别函数被应用于评估巴西人群的两性异形。根据性别对变量进行描述性统计,以及Student t检验和Mann-Whitney检验,以查看变量之间是否存在差异。利用研究数据计算了一个新的判别公式,并使用机器学习技术、神经网络和决策树来改进性别预测。这些变量显示出与性别相关的显著差异,除了R(男性平均值为2.00,女性平均值为2.40)外,所有男性的平均值都更高。该研究的原始公式准确率较低,女性的准确率仅为8.33%。然而,为巴西人计算的公式的准确率为70.20%;在机器学习技术中,只有神经网络的准确率高于已经获得的73.30%。总之,新公式的准确率为70.20%,可以作为评估巴西成年人额窦性异形的辅助方法。
Use of the frontal sinus to evaluate sexual dimorphism in a Brazilian sample
The frontal sinuses are commonly used in sexual estimation due to the considerable variation in size, shape and number. Previous studies have shown average accuracy measuring frontal sinus area, height and width; however, authors have associated such measurements with the frontal sinus index, obtaining better results. Therefore, the aim of the present study was to evaluate sexual dimorphism of the frontal sinus in Brazilian adults. The sample consisted of 255 lateral cephalometric radiographs of subjects between 20 and 40 years of age, 132 females and 123 males. Based on the methodology of Luo et al. (2018), the area (S) and the maximum frontal sinus height and width (AB and EF, respectively) were measured using computer-aided design software; and the ratio between AB and EF was taken as the frontal sinus index (R). The discriminant function developed by the authors was then applied to evaluate sexual dimorphism in the Brazilian population. Descriptive statistics were performed for the variables according to gender, as well as Student's t-test and the Mann-Whitney test to see if there was a difference between the variables. A new discriminant formula was calculated with the study data and machine learning techniques, neural networks and decision trees, were used to improve the prediction of sex. The variables showed significant differences in relation to gender, and with the exception of R, where the male mean was 2.00 and the female mean was 2.40, all the means were higher for males. The original formula of the study had low accuracy, with a level of accuracy of only 8.33% for females. However, the formula calculated for Brazilians presented an accuracy of 70.20%; of the machine learning techniques, only the neural network presented a higher value than the one already obtained, of 73.30%. In conclusion, the new formula showed an accuracy of 70.20% and can be applied as an auxiliary method in the assessment of frontal sinus sexual dimorphism in Brazilian adults.