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

Kartal E., Etli Y., Asirdizer M., Hekimoglu Y., Keskin S., Demir U., ...Daha Fazla

Legal Medicine, cilt.59, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 59
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.legalmed.2022.102143
  • Dergi Adı: Legal Medicine
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CINAHL, Criminal Justice Abstracts, EMBASE, MEDLINE
  • Anahtar Kelimeler: Foramen magnum, Sex estimation, Discriminant function analysis, Artificial neural networks, Linear discriminant function analysis, Stepwise discriminant analysis
  • Van Yüzüncü Yıl Üniversitesi Adresli: Evet

Özet

© 2022 Elsevier B.V.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.