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Growth Prediction in Orthodontics: ASystematic Review of Past Methods up to Artificial Intelligence
Ioannis Lyros1, Heleni Vastardis1, Ioannis A Tsolakis2,3
1Department of Orthodontics, School of Dentistry, National and Kapodistrian University of Athens, 11527 Athens, Greece.
Children (Basel, Switzerland)
|August 28, 2025
Summary
Accurate growth prediction in orthodontics is crucial for treatment success. While traditional methods like skeletal age assessment are useful, combining them with digital technology may improve future predictions with less patient distress.
Area of Science:
- Orthodontics
- Human Growth and Development
- Diagnostic Imaging
Background:
- Accurate growth prediction is vital for orthodontic diagnosis and treatment planning in growing individuals.
- Failure to assess skeletal maturity can lead to treatment instability and failure.
- This review systematically examines methods for predicting human growth.
Purpose of the Study:
- To systematically review and explain current methods for predicting human growth.
- To discuss traditional and modern digital techniques for growth prediction.
Main Methods:
- A systematic literature search was conducted on PubMed and gray literature up to May 2025.
- 69 studies focusing on maxillofacial growth prediction without orthodontic intervention were included.
Main Results:
- Significant variability exists in growth prediction studies.
- Lateral cephalometric radiography has questionable diagnostic value in young orthodontic patients.
- Skeletal age assessment using cervical vertebrae and hand-wrist X-rays is feasible.
- Computer software enhances traditional methods; biochemical and electromyographic methods are proposed for research.
Conclusions:
- Future growth prediction in young patients should minimize patient distress and radiation exposure.
- Comprehensive prediction capabilities are currently limited.
- Integrating established methods with digital technology holds promise for future advancements.
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