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Teeth

Teeth

The formation of teeth, also known as odontogenesis, is a complex process that begins in utero, around the sixth week of embryonic development. There are three stages to this process: the bud stage, the cap stage, and the bell stage.
In the bud stage, the tooth germ (an aggregation of cells) starts to form in the developing jawbone. During the cap stage, the tooth germ differentiates into enamel organ, dental papilla, and dental sac, which will later develop into the tooth's enamel, dentin...

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Related Experiment Video

Updated: Aug 8, 2025

Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment

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Can we predict orthodontic extraction patterns by using machine learning?

Landon Leavitt¹, James Volovic¹, Lily Steinhauer²

¹Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, Indiana, USA.

Orthodontics & Craniofacial Research

|February 27, 2023

Summary

This summary is machine-generated.

Machine learning accurately predicts some orthodontic extraction patterns, particularly for first premolars. However, predicting other patterns like second premolar extractions remains challenging, with molar relationship and crowding being key indicators.

Keywords:

clinical decision-making machine learning orthodontic extraction

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Area of Science:

Orthodontics
Machine Learning
Data Science

Background:

Orthodontic extractions are common but predicting patterns can be complex.
Machine learning (ML) offers potential for improving prediction accuracy.

Purpose of the Study:

To evaluate the effectiveness of ML algorithms in predicting orthodontic extraction patterns.
To identify key predictors for various extraction scenarios in a diverse patient population.

Main Methods:

Retrospective analysis of 366 orthodontic patient records.
Utilized 55 cephalometric and demographic features to train Random Forest, Logistic Regression, and Support Vector Machine models.
Models were trained and tested on data stratified by race/ethnicity and gender.

Main Results:

High accuracy was achieved in predicting upper and lower 1st premolar (U/L4s) and upper 1st premolar only (U4s) extractions.
Lower accuracy was observed for patterns involving second premolars (U4/L5s, U5/L4s, U/L5s).
Random Forest demonstrated the highest overall accuracy (54.55%).

Conclusions:

Supervised ML models effectively predict certain orthodontic extraction patterns (U/L4s, U4s).
Prediction accuracy is limited for extraction patterns involving second premolars.
Molar relationship, mandibular crowding, and overjet are significant predictors of extraction patterns.