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Related Experiment Video

Updated: Oct 12, 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

Analysis of the mandibular canal course using unsupervised machine learning algorithm.

Young Hyun Kim¹, Kug Jin Jeon¹, Chena Lee¹

¹Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea.

|November 19, 2021

Summary

This summary is machine-generated.

Cluster analysis automatically classified three-dimensional mandibular canal (MC) courses from cone-beam computed tomography scans. This machine learning approach reduces expert variability for reliable anatomical structure classification.

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Area of Science:

Medical imaging analysis
Machine learning in anatomy
Dental anatomy research

Background:

Anatomical structure classification is crucial in medicine but suffers from expert interpretation variability.
Standardizing the classification of the mandibular canal (MC) is challenging due to individual anatomical differences.

Purpose of the Study:

To apply cluster analysis, an unsupervised machine learning method, for classifying three-dimensional (3D) mandibular canal (MC) courses.
To visualize and define standard MC courses within the Korean population using data-driven methods.

Main Methods:

Utilized 429 cone-beam computed tomography (CBCT) images.
Measured four parameters (two vertical, two horizontal) at four mandibular sites.
Performed k-means cluster analysis after parameter normalization and optimal cluster determination.

Main Results:

Cluster analysis successfully classified 3D MC courses into three distinct types with statistically significant differences.
Cluster 2, a relatively straight course near the lingual and inferior border, was most common (42.1%), predominantly in males (57.1%).
Cluster 1 (smooth, lingual, steep sagittal slope) and Cluster 3 (lingual, posterior buccal bend, increasing sagittal slope) showed lower distributions (26.0% and 31.9%, respectively).

Conclusions:

Cluster analysis provides an objective and automated method for classifying 3D mandibular canal courses.
This approach minimizes observer variability, offering reliable classification and representative standard anatomical information.
The study successfully defined three standard MC course types in the Korean population.