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Cranial Bones: Lateral View

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Analysis of Craniomaxillofacial Malformations in Mice Using Three-dimensional Microcomputed Tomography

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Ancestry Estimation Using Cranial and Postcranial Macromorphoscopic Traits.

Micayla C Spiros¹, Joseph T Hefner¹

¹Department of Anthropology, Michigan State University, 655 Auditorium Drive, East Lansing, MI, 48824.

Journal of Forensic Sciences

|November 15, 2019

Summary

This summary is machine-generated.

This study shows that combining cranial and postcranial macromorphoscopic (MMS) traits significantly improves ancestry estimation accuracy. Machine learning models, particularly artificial neural networks, offer advanced tools for forensic anthropology casework.

Keywords:

ancestry forensic anthropology forensic science graphical user interface machine learning macromorphoscopic traits postcranial morphology shiny

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Area of Science:

Forensic Anthropology
Biological Anthropology
Human Osteology

Background:

Macromorphoscopic (MMS) traits are crucial for ancestry estimation in forensic anthropology.
Current methods predominantly rely on cranial morphology, potentially limiting accuracy.

Purpose of the Study:

To evaluate the utility of postcranial MMS traits in ancestry estimation.
To demonstrate the benefits of a combined cranial and postcranial MMS trait approach.
To compare traditional methods with machine learning models for improved ancestry estimation.

Main Methods:

Utilized eight cranial and eleven postcranial MMS traits from the Terry and Bass Skeletal Collections.
Employed quadratic discriminant function analysis.
Applied machine learning classification models: artificial neural networks (aNN), random forest, and support vector machine.

Main Results:

Classification models incorporating both cranial and postcranial traits achieved 88-92% accuracy.
This represents a nearly 15% improvement over cranial-only models.
Machine learning models demonstrated significant potential for enhancing ancestry estimation accuracy.

Conclusions:

A multivariate statistical framework integrating cranial and postcranial data is essential for accurate ancestry estimation.
Machine learning models offer powerful, adaptable tools for forensic anthropological analysis.
The artificial neural network model is available via the ComboMaMD Analytical web application for practical casework implementation.