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Related Experiment Video

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Deep learning for virtual orthodontic bracket removal: tool establishment and application.

Ruomei Li1, Cheng Zhu1, Fengting Chu1

  • 1Department of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 500 Quxi Road, Shanghai, 200011, China.

Clinical Oral Investigations
|January 27, 2024
PubMed
Summary
This summary is machine-generated.

A new deep learning tool enables virtual orthodontic bracket removal with high accuracy and speed, even without original tooth data. This technology aids in assessing bracket positioning and potentially designing orthodontic devices before debonding.

Keywords:
Artificial IntelligenceDirect bonding techniqueNeural networksOrthodontic bracket position evaluationOrthodontic(s)

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Dentistry
  • Orthodontics

Background:

  • Accurate bracket placement is crucial for successful orthodontic treatment.
  • Current methods for bracket assessment can be time-consuming and may require physical manipulation.
  • The need for advanced digital tools in orthodontics is growing.

Purpose of the Study:

  • To develop a deep learning-based tool for virtual orthodontic bracket removal.
  • To assess the tool's accuracy and efficiency in feature extraction from bonded teeth.
  • To demonstrate its application in evaluating bracket position and deviation.

Main Methods:

  • A segmentation network was trained using a dataset of 978 bonded teeth.
  • The tool's performance was validated on a separate dataset of 118 bonded teeth.
  • Features like clinical crown center and bracket orientation were extracted for analysis.

Main Results:

  • Virtual bracket removal was achieved in 2.9 ms per tooth.
  • High accuracies of 98.93% and 97.42% were reported for the datasets.
  • The tool successfully analyzed bracket angulation and distribution, revealing orthodontist preferences.

Conclusions:

  • The developed tool is efficient, precise, and operates without requiring original tooth data.
  • It effectively visualizes bonding deviations for bracket position assessment.
  • Potential applications include avoiding unnecessary bracket removal and pre-fabricating orthodontic devices.