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Machine Learning-Based Model for Predicting Short- and Long-Term Growth in Untreated Class III Malocclusion.

Maria Denisa Statie1, Michele Nieri1, Valentina Rutili1

  • 1Department of Experimental and Clinical Medicine, Università degli Studi di Firenze, Firenze, Italy.

Orthodontics & Craniofacial Research
|May 18, 2026
PubMed
Summary
This summary is machine-generated.

A machine learning model accurately predicts Caucasian growth in Class III malocclusion. However, mandibular landmarks showed lower accuracy in both short-term and long-term predictions.

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Area of Science:

  • Orthodontics and Dental Anthropology
  • Biomedical Engineering
  • Machine Learning in Healthcare

Background:

  • Class III malocclusion presents complex growth patterns in Caucasian individuals.
  • Predicting skeletal growth is crucial for effective orthodontic treatment planning.
  • Existing methods for growth prediction have limitations in accuracy and scope.

Purpose of the Study:

  • To develop and evaluate a Machine Learning (ML)-based model for predicting the craniofacial growth of Caucasian subjects with untreated Class III malocclusion.
  • To assess the model's predictive accuracy in both short-term and long-term growth phases.
  • To identify specific cephalometric landmarks with varying prediction accuracies.

Main Methods:

  • Utilized a longitudinal sample of 144 Caucasian subjects with untreated Class III malocclusion.
  • Employed a Graph Neural Network (GNN) model trained on 80% of the data.
  • Analyzed cephalograms using 16 digitized cephalometric landmarks in an X-Y coordinate system.
  • Validated predictions using a one-sample t-test and calculating Euclidean distances between predicted and observed values on a 20% test set.

Main Results:

  • The ML model demonstrated statistically significant predictions for several cephalometric points (SX, PgY, BY, PNSY, NY, SY in short-term; MeX, GnX, PgX, BX, BY, PtY in long-term).
  • In short-term predictions, high mean Euclidean distances (indicating lower accuracy) were observed for mandibular landmarks: Go (2.6 mm), Me (1.9 mm), Gn (1.9 mm), Pg (2.0 mm), and B (2.0 mm).
  • Long-term predictions also showed high mean Euclidean distances for mandibular landmarks: Go (3.1 mm), Me (4.3 mm), Gn (4.1 mm), Pg (4.5 mm), and B Point (4.1 mm).

Conclusions:

  • The developed ML model shows promising accuracy for predicting craniofacial growth in Class III malocclusion across most landmarks.
  • Prediction accuracy is notably lower for key mandibular landmarks (Go, Me, Gn, Pg, B Point) in both short-term and long-term analyses.
  • Further refinement of ML models may be necessary to improve the prediction of mandibular growth in this patient cohort.