Determination of Gender from Human Cranial Measurements Using Computed Tomography Scans.

OBJECTIVE: To determine gender using cranial measurements from CT scans and to evaluate their predictive accuracy for gender classification. STUDY DESIGN: A cross-sectional study. Place and Duration of the Study: Department of Forensic Medicine and Toxicology, Ziauddin University, Karachi, Pakistan, from July to December 2025. METHODOLOGY: A total of 280 CT scans of adult male and female were used for fourteen cranial measurements, including foramen magnum length (sagittal diameter; FML) and foramen magnum breadth (transverse diameter; FMB), right and left frontal sinus height (RTFSH, LTFSH), right and left frontal sinus breadth (RTFSB, LTFSB), right and left frontal sinus depth (RTFSD, LTFSD), frontal bone inclination angle (FI), maximum cranial length (MCL) and maximum cranial breadth (MCB), interorbital breadth (IOB), lateral wall interorbital breadth (LWIOB), and bizygomatic breadth (BZB). Right and left frontal sinus index (RTFSI, LTFSI) and cephalic index (CI) were calculated. SPSS version 27 was used to calculate mean ± SD, compare means (independent samples t-test or Mann-Whitney U test), and estimate gender prediction accuracies using binary logistic regression (BLR), decision tree (DT), and ROC curve analyses. RESULTS: MCL, MCB, RTFSB, LTFSB, RTFSD, LTFSD, FI, FML, FMB, LWIOB, BZB, and LTFSI showed significant gender differences. MCL, MCB, FML, LWIOB, BZB, and FI were the significant gender predictors in the BLR model. ROC curve analysis with male gender as the state variable showed an AUC >0.5 for 13 measurements, except for FI. BZB was the most important classifier in DT analysis, followed by MCL. Assuming male gender as the positive class, the BLR model showed higher specificity (0.814 > 0.714) but lower sensitivity (0.764 <0.835) than the DT model. CONCLUSION: All the cranial measurements showed significant gender differentiation except IOB, RTFSH, and LTFSH. BLR and DT models both can be employed for gender prediction. KEY WORDS: Craniometry, Gender determination, CT images, Binary logistic regression, Receiver operating characteristic curve analysis, Decision tree.