Assistant Professor of Forensic Medicine, Head of the Department of Forensic Medicine, Medical Faculty of Van Yuzuncu, Yil University, Van, Turkey.
Specialist of Forensic Medicine, Department of Forensic Medicine, Medical Faculty Hospital of Selcuk University, Konya, Turkey.
Leg Med (Tokyo). 2022 Nov;59:102143. doi: 10.1016/j.legalmed.2022.102143. Epub 2022 Sep 5.
Although many studies have been conducted using the foramen magnum for sex estimation, recent findings have indicated that the discriminant and regression models obtained from the foramen magnum may not be reliable. Artificial Neural Networks, was used as a classification technique in sex estimation studies on some other bones, did not used in sex estimation studies on the foramen magnum until now. The aim of this study was sex estimation on an Eastern Turkish population sample using foramen magnum measurements, discriminant analyses and Artificial Neural Networks.
The study was performed on the CT images of a total of 720 cases, comprising 360 males and 360 females. For sex estimation, discriminant analysis and Artificial Neural Networks were used.
The accuracy rate was 86.7% with discriminant analysis and when sex estimation accuracy was determined according to cases with posterior probabilities above 95%, the accuracy ranged from 0% to 33.3%. With the use of the discriminant formulas of 2 other studies, obtained from different Turkish samples, sex could be determined at a rate of 84.6%. Some formulas were found to be unsuccessful in sex estimation. Sex estimation accuracy of 88.2% was achieved with Artificial Neural Networks.
In this study, it was found that sex could be determined to some extent with discriminant formulas from other samples from the same population, although some formulas were unsuccessful. With the use of image processing techniques and machine learning algorithms, better results can be obtained in sex estimation.
尽管许多研究已经使用枕骨大孔进行性别估计，但最近的发现表明，从枕骨大孔获得的判别和回归模型可能不可靠。人工神经网络在其他一些骨骼的性别估计研究中被用作分类技术，但直到现在还没有在枕骨大孔的性别估计研究中使用。本研究旨在使用枕骨大孔测量值、判别分析和人工神经网络对一个东土耳其人群样本进行性别估计。
该研究共对 720 例病例进行了 CT 图像分析，其中包括 360 例男性和 360 例女性。为了进行性别估计，使用了判别分析和人工神经网络。
判别分析的准确率为 86.7%，当根据后验概率高于 95%的病例确定性别估计准确率时，准确率范围为 0%至 33.3%。使用来自其他土耳其样本的 2 项其他研究的判别公式，性别可确定率为 84.6%。一些公式在性别估计中不成功。人工神经网络的性别估计准确率为 88.2%。
在这项研究中，尽管一些公式不成功，但发现可以从同一人群的其他样本的判别公式中在一定程度上确定性别。通过使用图像处理技术和机器学习算法，可以在性别估计中获得更好的结果。
