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Automatic dental age calculation from panoramic radiographs using deep learning: a two-stage approach with object

Kazuma Kokomoto1, Rina Kariya2, Aya Muranaka2

  • 1Division for Medical Informatics, Osaka University Dental Hospital, 1-8 Yamada-oka, Suita, Osaka, 565-0871, Japan. kokomoto.kazuma.dent@osaka-u.ac.jp.

BMC Oral Health
|January 30, 2024
PubMed
Summary

This study introduces an automatic dental age calculation method using deep learning on panoramic radiographs, achieving clinically acceptable accuracy. The novel approach significantly reduces manual effort in dental age estimation for pediatric dentistry.

Keywords:
Artificial intelligenceDental informaticsMachine learningMedical informatics applicationsOrthodonticsPediatric dentistry

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Area of Science:

  • Dentistry
  • Radiology
  • Artificial Intelligence

Background:

  • Dental age estimation is vital for pediatric and orthodontic treatment planning.
  • Current radiological methods rely on manual, time-consuming processes.
  • A need exists for automated, efficient dental age calculation.

Purpose of the Study:

  • To develop and validate a novel, fully automatic method for dental age calculation.
  • To utilize deep learning techniques on panoramic radiographs for dental age estimation.
  • To compare the accuracy of the automated method against manual calculations by human experts.

Main Methods:

  • A two-stage deep learning model was employed, using Scaled-YOLOv4 for dental germ detection and EfficientNetV2 M for developmental stage classification.
  • The models were trained on over 8,000 panoramic radiographs.
  • Different methods (single selection, weighted average, expected value) were evaluated to convert classification probabilities to dental age.

Main Results:

  • Dental germ detection achieved a mean average precision of 98.26%.
  • Classification of single-root and multi-root dental germs reached Top-3 accuracies of 98.46% and 98.36%, respectively.
  • The weighted average method demonstrated a mean absolute error of 0.261, indicating accuracy within less than one developmental stage.

Conclusions:

  • The proposed two-stage deep learning approach enables automatic dental age calculation from panoramic radiographs.
  • The method achieves clinically acceptable accuracy, comparable to or exceeding manual estimations.
  • This automated system offers a promising, efficient alternative for dental age assessment.