Sex classification using exocranial surfaces in a multi-population sample.

Area of Science:

• Forensic Anthropology
• Bioarchaeology
• Human Osteology

Background:

• Accurate sex estimation from skeletal remains is crucial in forensic science.
• Skull morphology offers significant potential for sex determination.
• Population-specific variations in sexual dimorphism impact the reliability of existing methods.

Purpose of the Study:

• To verify an innovative classification model for sex estimation using the exocranial skull surface.
• To assess the model's accuracy and reliability across diverse, multi-population samples.
• To identify factors influencing the model's performance in different geographic and ethnic groups.

Main Methods:

• Utilized an innovative classification model analyzing the exocranial skull surface.
• Tested the model on multi-population skeletal samples from Central Europe (Czech, Slovak), Southern Europe (French), North Africa (Egyptian), and Northern Europe (Danish).
• Performed cross-validation on combined Central and Southern European datasets.

Main Results:

• Achieved high accuracy rates for Czech (96%), Slovak (92%), and French (90%) samples.
• Cross-validation accuracy for combined European populations reached 91.74%.
• Accuracy dropped significantly for Egyptian (82%) and Danish (80%) populations, indicating reduced reliability in geographically distant groups.

Conclusions:

• The sex estimation model is highly reliable and accurate for Central European populations.
• Population specificity, particularly variations in sexual dimorphism and distinct morphological differences, limits the classifier's effectiveness in diverse populations.
• Further refinement is needed to enhance the model's robustness and reliability across a broader range of human populations.