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Classification of Bones

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The long...

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Machine Learning and Clustering Analysis of Class II and III Malocclusions.

Eva Paddenberg-Schubert¹, Kareem Midlej², Sebastian Krohn¹

¹Department of Orthodontics, University Hospital of Regensburg, University of Regensburg, Regensburg, Germany.

Clinical and Experimental Dental Research

|June 2, 2026

Summary

Machine learning accurately classifies skeletal class II/III malocclusion using minimal cephalometric parameters. Clustering analysis reveals diverse patient characteristics, highlighting the complexity of malocclusion phenotypes.

Keywords:

congenital hereditary malocclusion classification neonatal diseases population characteristics stomatognathic diseases stomatognathic system

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Area of Science:

Orthodontics and Dental Anthropology
Biomedical Engineering
Data Science in Healthcare

Background:

Skeletal malocclusions, specifically Class II and Class III, present diagnostic challenges.
Accurate classification is crucial for effective orthodontic treatment planning.
Machine learning offers potential for improved diagnostic accuracy in orthodontics.

Purpose of the Study:

To classify skeletal Class II/III malocclusion using machine learning algorithms.
To identify key cephalometric parameters for accurate classification.
To explore patient characteristics using k-means clustering.

Main Methods:

Prospective observational study with 379 German orthodontic patients.
Machine learning models (KNN, RF, LDA, SVM, CART) applied for classification.
K-means clustering used to analyze patient subgroups.

Main Results:

KNN model achieved high accuracy (94.72%) with Wits appraisal alone.
Adding SN-Pg angle improved accuracy to 95.86%.
Clustering revealed 3-4 optimal clusters for skeletal Class II/III malocclusion patients, showing significant parameter differences.

Conclusions:

A reduced cephalometric parameter set (Wits appraisal, SN-Pg angle) is effective for skeletal Class II/III classification.
Exploratory clustering highlights the heterogeneity within skeletal Class II/III malocclusion phenotypes.
Machine learning and clustering provide valuable insights into malocclusion complexity.