Related Experiment Video
Updated: Apr 27, 2026
09:10
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
Published on: August 5, 2021
2.0K
Automated dental age estimation model for panoramic radiographs
M Akshaya1, K R Vijayalakshmi1, T N R Kumar2
1Department of Oral Medicine & Radiology, Government Dental College & Research Institute, Bangalore, Karnataka, 560002, India.
Journal of Forensic and Legal Medicine
|April 25, 2026
Summary
This study developed an automated system using a convolutional neural network (CNN) to assess dental development stages and classify age groups from panoramic X-rays. The model shows high accuracy, particularly for well-represented age groups, offering a foundation for future forensic applications.
Area of Science:
- Forensic Odontology
- Radiology
- Pediatric Dentistry
- Orthodontic Dentistry
Background:
- Accurate dental development assessment is vital for various dental specialties.
- Manual interpretation of dental panoramic radiographs is time-consuming and variable.
- Automated methods are needed to improve efficiency and consistency.
Purpose of the Study:
- To evaluate a convolutional neural network (CNN) model for automated detection of Nolla's developmental stages.
- To assess the model's capability for subsequent dental age group classification using digital panoramic radiographs.
- To analyze the performance of the model in individuals aged 3-30 years.
Main Methods:
- Utilized Nolla's comprehensive staging system for permanent teeth development.
- Trained and validated a YOLOv8-based CNN model on 4073 annotated panoramic radiographs.
- Tested the model on an independent dataset of 1450 anonymized and 644 repository images.
Main Results:
- The model achieved high precision (0.912), recall (0.927), and F1 score (0.919) for developmental stage classification.
- Age group classification yielded precision (0.83), recall (0.79), and F1 score (0.81).
- Performance was optimal in mid-age ranges, with lower accuracy in children under 6 years.
Conclusions:
- The CNN model accurately identifies permanent teeth developmental stages and estimates age from panoramic radiographs.
- Demonstrates the feasibility of real-time object detection for dental age assessment.
- Provides a proof-of-concept for integrating automated tools into dental developmental assessment, paving the way for validated forensic models.
