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Artificial neural network in orthodontic therapeutic extractions
M S Rohith1, Shashanka P Kumar2, Anusha G Hegde3
1Department of Orthodontics and Dentofacial Orthopaedics, Vydehi Institute of Dental Sciences and Research Institute, Bangalore, India.
Bioinformation
|June 12, 2026
Summary
Artificial intelligence accurately predicts orthodontic extractions. A neural network model achieved 92.38% accuracy in deciding whether to extract teeth and which ones, offering a reliable diagnostic tool.
Area of Science:
- Dentistry
- Artificial Intelligence
- Machine Learning
Background:
- Orthodontic tooth extractions are irreversible procedures with significant implications.
- Accurate diagnosis is crucial to prevent potential complications arising from unsubstantiated extraction decisions.
Purpose of the Study:
- To develop and evaluate an artificial intelligence (AI) decision-making model for diagnosing orthodontic extractions.
- To assess the reliability of neural network machine learning in predicting tooth extractions.
Main Methods:
- A dataset of 455 patients was analyzed.
- Input data included 12 cephalometric variables and two additional indexes.
- Data were manually traced and digitized using NemoCeph 2D software for analysis.
Main Results:
- The binary classifier model (extraction decision) achieved an accuracy of 92.38%.
- The multi-classifier model (which tooth to extract) also demonstrated 92.38% accuracy.
- The artificial neural network model proved effective in predicting orthodontic extractions.
Conclusions:
- Artificial neural networks provide a reliable and valuable method for predicting orthodontic extractions.
- AI-driven decision-making can enhance diagnostic accuracy in orthodontics.
- This approach supports more informed treatment planning for irreversible dental procedures.