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Updated: Feb 16, 2026

Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model
Published on: November 4, 2025
A quantitative approach for sex estimation based on cranial morphology
Efthymia Nikita1, Efrossyni Michopoulou2
1Science and Technology in Archaeology Research Center, The Cyprus Institute, 2121 Aglantzia, Nicosia, Cyprus.
Objectives:
This paper proposes a method for the quantification of the shape of sexually dimorphic cranial traits, namely the glabella, mastoid process and external occipital protuberance.
Materials And Methods:
The proposed method was developed using 165 crania from the documented Athens Collection and tested on 20 Cretan crania. It is based on digital photographs of the lateral view of the cranium, drawing of the profile of three sexually dimorphic structures and calculation of variables that express the shape of these structures.
Results:
The combinations of variables that provide optimum discrimination between sexes are identified by means of binary logistic regression and discriminant analysis. The best cross-validated results are obtained when variables from all three structures are combined and range from 75.8 to 85.1% and 81.1 to 94.6% for males and females, respectively. The success rate is 86.3-94.1% for males and 83.9-93.5% for females when half of the sample is used for training and the rest for prediction. Correct classification for the Cretan material based upon the standards developed for the Athens sample was 80-90% for the optimum combinations of discriminant variables.
Discussion:
The proposed method provides an effective way to capture quantitatively the shape of sexually dimorphic cranial structures; it gives more accurate results relative to other existing methods and it does not require specialized equipment. Equations for sex estimation based on combinations of variables are provided, along with instructions on how to use the method and Excel macros for calculation of discriminant variables with automated implementation of the optimum equations.
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