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March 12, 2026International Journal of Morphology0 citationsOpen Access

Sex Estimation Using Cephalometric Data in a Turkish Population: A Logistic and ROC-Based Analysis

YAYarenkür Alkan

Key Points

  • The study aims to develop a sex estimation model using cephalometric measurements from living individuals in a Turkish population.
  • Collected 14 standardized cephalometric measurements from 244 adult individuals.
  • Applied univariate and multivariate logistic regression models for sex estimation accuracy.
  • Used ROC analysis to assess model performance and optimal classification thresholds.
  • The final model achieved an overall classification accuracy of 89.8%.
  • Sensitivity was recorded at 89.84% and specificity at 93.10%.
  • The AUC (Area Under Curve) of the model was 0.965, indicating strong discriminative power.

Abstract

Sex estimation is a fundamental task in biological anthropology; however, most previous studies rely on skeletal collections or radiological data.This study is distinctive in its use of cephalometric measurements collected from living individuals to develop a population-specific sex estimation model based solely on head dimensions in a contemporary Turkish cohort.This approach offers a soft-tissue-based, population-specific model that may be useful in certain forensic screenings or clinical anthropological applications where skeletal data or imaging are not available.Although advanced imaging technologies such as CT and 3D scanning have become increasingly popular for metric analysis, they are often inaccessible in many contexts.Fourteen standardized cephalometric measurements were taken from a total of 244 adult individuals (128 males, 116 females).Both univariate and multivariate logistic regression models were constructed to evaluate sex estimation accuracy, while Receiver Operating Characteristic (ROC) analysis was used to assess model performance and identify optimal classification thresholds.The final multivariate model, which included maximum head breadth, total facial height, maximum head length, head circumference, nasal aperture breadth, and bigonial breadth, achieved an overall classification accuracy of 89.8 %, with a sensitivity of 89.84 %, specificity of 93.10 %, and an AUC of 0.965.These results demonstrate the strong discriminative power of the model and highlight the effectiveness of integrating cephalometric data with logistic regression and ROC analysis.By establishing population-specific threshold values, this study offers a robust and replicable framework for sex estimation applicable in both forensic and archaeological contexts.The findings also emphasize the value of accessible and cost-effective methods, particularly for use in resource-limited environments.Further research with larger and more diverse samples is encouraged to validate and broaden the applicability of these standards.

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Cite This Study

Yarenkür Alkan (2025) studied this question.

synapsesocial.com/papers/69b2581996eeacc4fcec7574https://doi.org/10.4067/s0717-95022025000601987
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Sex Determination Using Craniometric Parameters: A Computed Tomography- Based Assessment2025
  2. 2Sex determination from human cranium 3D CT based evaluation in an Indian population.2025
  3. 3Sex Estimation from Craniofacial Measurements in a Northeastern Thai Skeletal Sample: A Comparative Evaluation of Statistical Classifiers Under Verified Assumption Conditions2026
  4. 4Sex Estimation Based on the Cranial Base of Three-Dimensional Skull Models from the Bosnia and Herzegovina Population Using Geometric Morphometrics2026
  5. 5Sex Estimation from CT-Derived Craniofacial Measurements in Thai Adults: Comparative Performance of Discriminant Function Analysis, Support Vector Machine, and Random Forest with Forensic Case Application Examples2026