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Updated: Sep 29, 2025

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Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment
Published on: February 23, 2024
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Unplanned molar intrusion after Invisalign treatment
Laura Talens-Cogollos1, Arturo Vela-Hernández2, María Aurora Peiró-Guijarro2
1Department of Stomatology, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain.
Summary
Clear aligners cause molar intrusion in most patients, averaging nearly 1 mm. Specific cephalometric measurements and patient age can predict the degree of this molar intrusion.
Area of Science:
- Orthodontics
- Dental Imaging
- Biometrics
Background:
- Clear aligners are increasingly used for orthodontic treatment.
- Molar intrusion is a common outcome of clear aligner therapy.
- Understanding factors influencing molar intrusion is crucial for treatment planning.
Purpose of the Study:
- To analyze and quantify molar intrusion following clear aligner treatment.
- To investigate the relationship between molar intrusion and patient age, treatment duration, and initial cephalometric measurements.
Main Methods:
- Retrospective descriptive-analytical study of 58 patients (aged 18-60) treated with Invisalign.
- Cephalometric analysis of lateral x-rays comparing baseline (T0) and post-treatment.
- Statistical analysis including parametric/nonparametric tests, Pearson correlations, and multivariate linear regression.
Main Results:
- 74.2% of patients exhibited molar intrusion.
- Average intrusion: 0.98 ± 0.54 mm (maxillary), 0.84 ± 0.29 mm (mandibular).
- Statistically significant reductions in L6_MP and U6_SN distances were observed. Maxillary and mandibular molar intrusion showed a negative correlation (r = -0.270). Treatment duration did not correlate with intrusion.
Conclusions:
- Clear aligners effectively induce molar intrusion in a majority of patients.
- Initial cephalometric variables (L6_MP T0, mandibular plane angle T0, facial axis T0) and patient age are significant predictors of maxillary and mandibular molar intrusion.
- These findings allow for prediction of intrusion magnitude based on baseline patient characteristics.

