Dental age estimation using a convolutional neural network algorithm on panoramic radiographs: A pilot study in Indonesia
- Author:
Arofi KURNIAWAN
1
;
Michael SAELUNG
;
Beta Novia RIZKY
;
An’nisaa CHUSIDA
;
Beshlina Fitri Widayanti Roosyanto PRAKOESWA
;
Giselle NEFERTARI
;
Ariana Fragmin PRADUE
;
Mieke Sylvia MARGARETHA
;
Aspalilah ALIAS
;
Anand MARYA
Author Information
- Publication Type:Original Article
- From:Imaging Science in Dentistry 2025;55(1):28-36
- CountryRepublic of Korea
- Language:English
-
Abstract:
Purpose:This study employed a convolutional neural network (CNN) algorithm to develop an automated dental age estimation method based on the London Atlas of Tooth Development and Eruption. The primary objectives were to create and validate CNN models trained on panoramic radiographs to achieve accurate dental age predictions using a standardized approach.
Materials and Methods:A dataset of 801 panoramic radiographs from outpatients aged 5 to 15 years was used. A CNN model for dental age estimation was developed using a 16-layer CNN architecture implemented in Python with TensorFlow and Scikit-learn, guided by the London Atlas of Tooth Development. The model included 6 convolutional layers for feature extraction, each followed by a pooling layer to reduce the spatial dimensions of the feature maps. A confusion matrix was used to evaluate key performance metrics, including accuracy, precision, recall, and F1 score.
Results:The proposed model achieved an overall accuracy, precision, recall, and F1 score of 74% on the validation set. The highest F1 scores were observed in the 10-year and 12-year age groups, indicating superior performancein these categories. In contrast, the 6-year age group demonstrated the highest misclassification rate, highlightingpotential challenges in accurately estimating age in younger individuals.
Conclusion:Integrating a CNN algorithm for dental age estimation represents a significant advancement in forensic odontology. The application of AI improves both the precision and efficiency of age estimation processes, providing
results:that are more reliable and objective than those obtained via traditional methods.
