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DOI 10.3390/children12081023

Медицинская библиотека · Children (Basel, Switzerland) · 2025

Growth Prediction in Orthodontics: ASystematic Review of Past Methods up to Artificial Intelligence.

Lyros Ioannis, Vastardis Heleni, Tsolakis Ioannis A, Kotantoula Georgia, Lykogeorgos Theodoros, Tsolakis Apostolos I

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Аннотация

BACKGROUND/

OBJECTIVES

Growth prediction may be used by the clinical orthodontist in growing individuals for diagnostic purposes and for treatment planning. This process appraises chronological age and determines the degree of skeletal maturity to calculate residual growth. In developmental deviations, overlooking such diagnostic details might culminate in erroneous conclusions, unstable outcomes, recurrence, and treatment failure. The present review aims to systematically present and explain the available means for predicting growth in humans. Traditional, long-known, popular methods are discussed, and modern digital applications are described.

MATERIALS AND

METHODS

A search on PubMed and the gray literature up to May 2025 produced 69 eligible studies on future maxillofacial growth prediction without any orthodontic intervention.

RESULTS

Substantial variability exists in the studies on growth prediction. In young orthodontic patients, the study of the lateral cephalometric radiography and the subsequent calculation of planes and angles remain questionable for diagnosis and treatment planning. Skeletal age assessment is readily accomplished with X-rays of the cervical vertebrae and the hand-wrist region. Computer software is being implemented to improve the reliability of classic methodologies. Metal implants have been used in seminal growth studies. Biochemical methods and electromyography have been suggested for clinical prediction and for research purposes.

CONCLUSIONS

In young patients, it would be of importance to reach conclusions on future growth with minimal distress to the individual and, also, reduced exposure to ionizing radiation. Nevertheless, the potential for comprehensive prediction is still largely lacking. It could be accomplished in the future by combining established methods with digital technology.

Для цитирования: Lyros Ioannis, Vastardis Heleni, Tsolakis Ioannis A, Kotantoula Georgia, Lykogeorgos Theodoros, Tsolakis Apostolos I. Growth Prediction in Orthodontics: ASystematic Review of Past Methods up to Artificial Intelligence.. Children (Basel, Switzerland). 2025. DOI: 10.3390/children12081023.
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