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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.
Imaging Science in Dentistry
|April 7, 2025
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.
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.
