Sex Estimation Using Cephalometric Data in a Turkish Population: A Logistic and ROC-Based Analysis
Yazarlar (1)
Dr. Öğr. Üyesi Yarenkür ALKAN Kırşehir Ahi Evran Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | International Journal of Morphology (Q4) | ||
| Dergi ISSN | 0717-9502 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | Scopus | ||
| Makale Dili | İngilizce | Basım Tarihi | 12-2025 |
| Cilt / Sayı / Sayfa | 43 / 6 / 1987–1994 | DOI | – |
| Makale Linki | https://intjmorphol.com/ | ||
| Özet |
| 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 … |
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