Quantitative Sex Estimation Based on Cranial Traits Using R Functions

Area of Science:

• Forensic Anthropology
• Bioinformatics
• Biometrics

Background:

• Accurate sex estimation is crucial in forensic anthropology.
• Traditional methods can be time-consuming and require specialized expertise.
• Automated, quantitative approaches offer potential for increased efficiency and objectivity.

Purpose of the Study:

• To develop and validate an R script for automated sex estimation based on cranial shape.
• To quantify specific cranial traits using digital image analysis.
• To assess the accuracy and efficiency of the proposed method on modern Greek skeletal assemblages.

Main Methods:

• Utilized an R script incorporating the Canny edge detector for cranial outline determination.
• Calculated discriminant variables from digital photographs of the lateral skull view.
• Quantified shape features of the glabella/frontal bone, mastoid process, and external occipital protuberance.
• Tested functions on two modern Greek skeletal collections (Athens and Crete).

Main Results:

• Achieved cross-validated classification accuracies ranging from 84.2% to 87.3% for pooled sexes in the Athens Collection.
• Accuracy increased to 93.9% when using a training/prediction split.
• Obtained correct classification rates of 80-90% for the Cretan material with optimal variable combinations.
• Demonstrated a straightforward and time-efficient application of the developed method.

Conclusions:

• The R script provides a robust and efficient tool for automated sex estimation from cranial morphology.
• The method offers a significant advantage in terms of speed and ease of application compared to traditional techniques.
• This approach has potential applications in forensic anthropology and bioarchaeology for rapid sex determination.