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

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Endocranial Morphology in Metopism.

Silviya Nikolova¹, Diana Toneva¹, Gennady Agre²

¹Department of Anthropology and Anatomy, Institute of Experimental Morphology Pathology and Anthropology with Museum, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria.

|July 29, 2025

Summary

Metopism, a cranial vault feature, alters endocranial morphology. This study found significant differences in cranial fossa dimensions and landmark positioning in metopic skulls, with 85% classification accuracy using machine learning.

Keywords:

digital morphometry endocranial surface machine learning metopic suture µCT imaging

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Area of Science:

Anthropology
Anatomy
Radiology

Background:

Metopism is associated with cranial vault shape but its endocranial morphology is understudied.
Comparative data on endocranial space in metopism is lacking.

Purpose of the Study:

To compare endocranial space in metopic and control crania.
To utilize morphometric analysis and machine learning for classification.

Main Methods:

Micro-CT scanning of 230 adult male crania (46 metopic, 184 control).
Collection of 3D endocranial landmark coordinates (47 landmarks).
Calculation of 1081 Euclidean distances, dataset reduction, and machine learning model training.

Main Results:

Metopic crania exhibit significantly longer anterior and middle cranial fossae segments.
The endobregma landmark is significantly closer to the anterior and middle cranial base in metopic skulls.
A Naive Bayes model achieved 85% classification accuracy using 69 selected attributes.

Conclusions:

Metopism is linked to distinct endocranial spatial configurations.
Machine learning effectively differentiates metopic from non-metopic crania based on endocranial morphology.