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Related Concept Videos

Tooth Anatomy01:21

Tooth Anatomy

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The human tooth enables us to eat a variety of foods, speak clearly, and even aid in shaping our faces. Teeth are composed of various elements that work together. Here's a detailed look at the anatomy of a human tooth.
The Crown, Neck, and Root
The visible part of the tooth is referred to as the crown. It's covered by enamel, the hardest substance in the human body. The crown is uniquely shaped for each type of tooth, allowing for different functions such as cutting, tearing, or...
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Related Experiment Video

Updated: Mar 31, 2026

Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment
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AI-enhanced 3D tooth movement forecasting in clear aligner therapy using deep morphometric modelling: A prospective

Rattan Khurana1, Archana2, Kanish Aggarwal3

  • 1Department of Orthodontics and Dentofacial Orthopaedics, Gian Sagar Dental College and Hospital, Ram Nagar (Rajpura), Patiala, Punjab, India.

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|March 30, 2026
PubMed
Summary

This study developed an AI model to predict early clear aligner tracking deviations. The deep morphometric AI accurately forecasts issues, improving treatment planning for better orthodontic outcomes.

Keywords:
Orthodontic alignersartificial intelligenceclinical decision-makingorthodonticstreatment outcome prediction

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Area of Science:

  • Orthodontics and Dental Technology
  • Artificial Intelligence in Healthcare
  • Medical Imaging Analysis

Background:

  • Early tracking deviations in clear aligner therapy reduce treatment efficiency.
  • Predictive tools are needed to identify patients at risk for these deviations.
  • Baseline and early intraoral scans offer potential data for prediction.

Purpose of the Study:

  • To develop and validate a deep morphometric artificial intelligence (AI) model.
  • To forecast early clear aligner tracking deviations using 3D intraoral scans.
  • To assess the model's accuracy in predicting clinically significant deviations.

Main Methods:

  • A prospective sample of 40 adult patients undergoing clear aligner therapy was analyzed.
  • A graph-convolutional neural network was employed for the AI model.
  • Geometric mesh features were extracted from baseline and first-week 3D intraoral scans.

Main Results:

  • The AI model achieved 85% accuracy in predicting early aligner tracking deviations.
  • The model demonstrated a Root Mean Square Error (RMSE) of 0.19 mm.
  • The results indicate strong performance in identifying patients at risk for deviation.

Conclusions:

  • AI-driven morphometric analysis is a promising approach for early risk detection in clear aligner therapy.
  • This technology can significantly improve treatment planning and efficiency.
  • The validated model offers a novel tool for orthodontic practitioners.