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Dental age estimation using a convolutional neural network algorithm on panoramic radiographs: A pilot study in

Arofi Kurniawan1, Michael Saelung2, Beta Novia Rizky1

  • 1Department of Forensic Odontology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia.

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Summary

This study developed an automated dental age estimation method using a convolutional neural network (CNN) algorithm. The AI-powered approach achieved 74% accuracy, offering a more precise and objective alternative to traditional forensic odontology techniques.

Keywords:
Age Determination by TeethArtificial IntelligenceForensic DentistryHuman RightsTooth Eruption

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Area of Science:

  • Forensic Odontology
  • Artificial Intelligence
  • Radiology

Background:

  • Accurate dental age estimation is crucial in forensic odontology.
  • Traditional methods can be subjective and time-consuming.
  • Automated approaches using AI offer potential for improved accuracy and efficiency.

Purpose of the Study:

  • To develop and validate a convolutional neural network (CNN) model for automated dental age estimation.
  • To utilize the London Atlas of Tooth Development and Eruption for standardized training.
  • To achieve accurate dental age predictions from panoramic radiographs.

Main Methods:

  • A dataset of 801 panoramic radiographs from individuals aged 5-15 years was analyzed.
  • A 16-layer CNN architecture was implemented using Python, TensorFlow, and Scikit-learn.
  • Performance was evaluated using a confusion matrix, assessing accuracy, precision, recall, and F1 score.

Main Results:

  • The CNN model achieved an overall accuracy of 74% on the validation set.
  • The highest F1 scores were observed for the 10 and 12-year age groups.
  • The 6-year age group showed the highest misclassification rate, indicating challenges in younger individuals.

Conclusions:

  • Convolutional neural network (CNN) integration marks a significant advancement in forensic odontology.
  • AI-driven dental age estimation enhances precision and efficiency over traditional methods.
  • The developed model provides more reliable and objective age assessments.