BMC oral health · 2025
A hybrid convolutional neural network model for dental age estimation using buccal alveolar bone level for Saudi children.
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Mazloom Nezhad Seyed Matin, Mohd Faizal Abdullah Erma Rahayu, Ibrahim Norliza, Bakhsh Heba H, Ishtiaq Uzair, Rahmat Rabiah Al Adawiyah, AlMugairin Sarah, ElKhateeb Sara M
Аннотация
BACKGROUND: Age estimation is an essential task in medical dentistry and forensic sciences. Dental Age Estimation (DAE) is one of the most common methods for age estimation. Teeth are commonly used for age estimation because the schedules of tooth development and eruption are barely affected by the environment, nutrition, and socio-economic factors. However, conventional DAE methods are manually performed by clinicians, exposing bias and error to the estimated age. Moreover, the potentials of buccal alveolar bone level in DAE are rarely investigated. The aim of this study is to assess the effectiveness of buccal alveolar bone level of mandibular posterior teeth in DAE using Artificial Intelligence (AI) for children.
METHODS: A total of 421 Dental Panoramic Tomography (DPT) of children ranging from 5 to 15 years of age were used to train multiple UNet segmentation models. Segmented images of teeth were extracted and fed into a Localization Convolutional Neural Network (CNN) to train them for measuring buccal alveolar bone level. Moreover, the buccal alveolar bone level measurements were then fed to the machine learning regression models for DAE.
RESULT: The transfer learning based UNet with VGG16 as its backbone achieved the best performance with an IoU score of 0.66 and the best performing Localization CNN achieved Mean Squared Error (MSE) of 0.0009 and [Formula: see text] score of 0.8266. The Support Vector Machine (SVM) regression model achieved the best mean absolute error of 0.99 year.
CONCLUSION: The results revealed the potential of buccal alveolar bone level for dental age estimation in children. Best performing model achieved an acceptable Mean Absolute Error (MAE) and similar results to Demirjian and London Atlas methods performed by human experts, showing promising results.
TRIAL REGISTRATION: Not applicable.
METHODS: A total of 421 Dental Panoramic Tomography (DPT) of children ranging from 5 to 15 years of age were used to train multiple UNet segmentation models. Segmented images of teeth were extracted and fed into a Localization Convolutional Neural Network (CNN) to train them for measuring buccal alveolar bone level. Moreover, the buccal alveolar bone level measurements were then fed to the machine learning regression models for DAE.
RESULT: The transfer learning based UNet with VGG16 as its backbone achieved the best performance with an IoU score of 0.66 and the best performing Localization CNN achieved Mean Squared Error (MSE) of 0.0009 and [Formula: see text] score of 0.8266. The Support Vector Machine (SVM) regression model achieved the best mean absolute error of 0.99 year.
CONCLUSION: The results revealed the potential of buccal alveolar bone level for dental age estimation in children. Best performing model achieved an acceptable Mean Absolute Error (MAE) and similar results to Demirjian and London Atlas methods performed by human experts, showing promising results.
TRIAL REGISTRATION: Not applicable.