Sex and Age Determination by Measurement of Foramen Magnum Dimensions on Computer Tomography Scanning Study in the Iranian Population

Morphometric analysis of the foramen magnum in sex estimation: An additional 3DCT study from Nepal on a larger sample

Background

Estimation of sex of the skeletal remains plays a vital part in the identification of an individual. This study is focused on the morphometric measurements of the foramen magnum region and examining the accuracy of sexual dimorphism in the Nepalese population.

Methods

Measurements were obtained from 3D computed tomography (CT) scan of 261 Nepalese adult cranial bases with known age and sex. Length and breadth of the foramen magnum, length and breadth of right and left occipital condyles and maximum and minimum intercondylar distance were measured on the base of the skull CT images.

Results

The mean values for all parameters were higher in males than females except for the maximum intercondylar distance. Sex prediction done with discriminant function analysis could classify the skull with an overall accuracy of 70.5%-71%.

Conclusions

It can be concluded from the results that the morphometric study of the foramen magnum is less reliable for sex estimation in the Nepalese population.

Sex estimation using foramen magnum measurements, discriminant analyses and artificial neural networks on an eastern Turkish population sample

BACKGROUND

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.

METHODOLOGY

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.

RESULTS

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.

CONCLUSION

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.