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Accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning.

Joon Im1, Ju-Yeong Kim2, Hyung-Seog Yu1

  • 1BK21 FOUR Project, Department of Orthodontics, Institute of Craniofacial Deformity, Yonsei University College of Dentistry, 50-1 Yonseiro, Seodaemun-gu, Seoul, 03722, Korea.

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Summary

Deep learning-based automatic tooth segmentation of digital dental models achieves high accuracy and efficiency. This advanced method significantly outperforms traditional techniques in success rate and speed for orthodontic applications.

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Area of Science:

  • Dentistry
  • Computer Science
  • Medical Imaging

Background:

  • Accurate tooth segmentation is crucial for orthodontic diagnosis and treatment planning.
  • Traditional segmentation methods can be time-consuming and prone to inaccuracies.
  • Deep learning offers a potential solution for automating and improving this process.

Purpose of the Study:

  • To evaluate the accuracy and efficiency of a deep learning-based automatic tooth segmentation algorithm for digital dental models.
  • To compare the performance of the deep learning algorithm against established landmark-based and designation/segmentation software.
  • To assess the clinical utility of automatic tooth segmentation in orthodontics.

Main Methods:

  • A dynamic graph convolutional neural network (DGCNN) algorithm was developed for automatic tooth segmentation and classification.
  • The DGCNN algorithm was applied to 516 digital dental models.
  • Segmentation accuracy (success rate, mesiodistal width, clinical crown height) and time were compared against landmark-based segmentation (LS) and designation/segmentation (DS) methods using 30 models.

Main Results:

  • The deep learning-based automatic segmentation (AS) achieved a 97.26% success rate, significantly higher than the 87.86% for DS (p < 0.001).
  • AS demonstrated superior efficiency, with a mean segmentation time of 57.73 seconds, compared to 424.17 seconds for LS and 150.73 seconds for DS (p < 0.001).
  • While minor differences in mesiodistal width and clinical crown height were observed, the deep learning method proved highly accurate and efficient.

Conclusions:

  • Deep learning-based automatic tooth segmentation provides a highly accurate and efficient solution for digital dental models.
  • The DGCNN algorithm shows significant advantages over traditional methods in terms of success rate and speed.
  • This technology holds great promise for enhancing orthodontic diagnosis and streamlining appliance fabrication.