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Updated: Jul 21, 2025

A Morphometric and Cellular Analysis Method for the Murine Mandibular Condyle

A Morphometric and Cellular Analysis Method for the Murine Mandibular Condyle

Published on: January 11, 2018

Sex estimation based on mandibular measurements.

Diana Toneva¹, Silviya Nikolova¹, Gennady Agre²

¹Department of Anthropology and Anatomy, Institute of Experimental Morphology, Pathology and Anthropology with Museum, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 25, 1113 Sofia, Bulgaria.

Anthropologischer Anzeiger; Bericht Uber Die Biologisch-Anthropologische Literatur

|July 27, 2023

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Machine learning models accurately estimate sex from mandibular measurements using CT scans. Support Vector Machines achieved 95.3% accuracy, enhancing forensic anthropology applications.

Area of Science:

Forensic Anthropology
Medical Imaging
Machine Learning

Background:

Medical imaging and machine learning offer valuable tools for physical and forensic anthropology.
These technologies are crucial for developing sex identification models from skeletal remains.

Purpose of the Study:

To develop and evaluate machine learning models for sex estimation using mandibular measurements.
To compare the performance of various machine learning algorithms on different mandibular measurement datasets.

Main Methods:

Utilized head CT scans from 239 adult Bulgarians to obtain 3D coordinates of 45 mandibular landmarks.
Created two datasets: one with 51 linear, projective, and angular measurements, and another with 990 interlandmark distances.
Applied seven machine learning algorithms (SVM, Neural Network, Naïve Bayes, Random Forest, J48, JRip, Logistic Regression) with 10x5-cross-validation and attribute selection.

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Last Updated: Jul 21, 2025

A Morphometric and Cellular Analysis Method for the Murine Mandibular Condyle

A Morphometric and Cellular Analysis Method for the Murine Mandibular Condyle

Published on: January 11, 2018

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

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Main Results:

Sub-symbolic algorithms generally outperformed symbolic ones, with Logistic Regression as an exception.
The Support Vector Machines algorithm achieved the highest classification accuracy of 95.3% using 19 interlandmark distances.
Advanced attribute selection improved classification accuracy across all tested algorithms for both datasets.

Conclusions:

Machine learning, particularly Support Vector Machines with interlandmark distances, provides a highly accurate method for sex estimation from mandibles.
The study demonstrates the effectiveness of advanced attribute selection in enhancing the performance of sex estimation models.
These findings have significant implications for forensic anthropology and the analysis of skeletal remains.