A hybrid convolutional neural network model for dental age estimation using buccal alveolar bone level for Saudi children
Abstract 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...
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| Format: | Article |
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BMC
2025-08-01
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| Series: | BMC Oral Health |
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| Online Access: | https://doi.org/10.1186/s12903-025-06694-9 |
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| author | Seyed Matin Mazloom Nezhad Erma Rahayu Mohd Faizal Abdullah Norliza Ibrahim Heba H. Bakhsh Uzair Ishtiaq Rabiah Al Adawiyah Rahmat Sarah AlMugairin Sara M. ElKhateeb |
| author_facet | Seyed Matin Mazloom Nezhad Erma Rahayu Mohd Faizal Abdullah Norliza Ibrahim Heba H. Bakhsh Uzair Ishtiaq Rabiah Al Adawiyah Rahmat Sarah AlMugairin Sara M. ElKhateeb |
| author_sort | Seyed Matin Mazloom Nezhad |
| collection | DOAJ |
| description | Abstract 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 $$\:{R}^{2}$$ 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. |
| format | Article |
| id | doaj-art-770bb64fb64f45c5b2feb685ddf3a27f |
| institution | DOAJ |
| issn | 1472-6831 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj-art-770bb64fb64f45c5b2feb685ddf3a27f2025-08-20T03:07:27ZengBMCBMC Oral Health1472-68312025-08-0125111010.1186/s12903-025-06694-9A hybrid convolutional neural network model for dental age estimation using buccal alveolar bone level for Saudi childrenSeyed Matin Mazloom Nezhad0Erma Rahayu Mohd Faizal Abdullah1Norliza Ibrahim2Heba H. Bakhsh3Uzair Ishtiaq4Rabiah Al Adawiyah Rahmat5Sarah AlMugairin6Sara M. ElKhateeb7Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti MalayaDepartment of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti MalayaDepartment of Oral and Maxillofacial Clinical Sciences, Faculty of Dentistry, Universiti MalayaDepartment of Preventive Dental Sciences, College of Dentistry, Princess Nourah bint Abdulrahman UniversityDepartment of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti MalayaDepartment of Oral and Maxillofacial Clinical Sciences, Faculty of Dentistry, Universiti MalayaDepartment of Preventive Dental Sciences, College of Dentistry, Princess Nourah bint Abdulrahman UniversityDepartment of Basic Dental Sciences, College of Dentistry, Princess Nourah bint Abdulrahman UniversityAbstract 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 $$\:{R}^{2}$$ 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.https://doi.org/10.1186/s12903-025-06694-9Age estimationDental ageMachine learningDeep learningBuccal alveolar bone levelSupport vector machine |
| spellingShingle | Seyed Matin Mazloom Nezhad Erma Rahayu Mohd Faizal Abdullah Norliza Ibrahim Heba H. Bakhsh Uzair Ishtiaq Rabiah Al Adawiyah Rahmat Sarah AlMugairin Sara M. ElKhateeb A hybrid convolutional neural network model for dental age estimation using buccal alveolar bone level for Saudi children BMC Oral Health Age estimation Dental age Machine learning Deep learning Buccal alveolar bone level Support vector machine |
| title | A hybrid convolutional neural network model for dental age estimation using buccal alveolar bone level for Saudi children |
| title_full | A hybrid convolutional neural network model for dental age estimation using buccal alveolar bone level for Saudi children |
| title_fullStr | A hybrid convolutional neural network model for dental age estimation using buccal alveolar bone level for Saudi children |
| title_full_unstemmed | A hybrid convolutional neural network model for dental age estimation using buccal alveolar bone level for Saudi children |
| title_short | A hybrid convolutional neural network model for dental age estimation using buccal alveolar bone level for Saudi children |
| title_sort | hybrid convolutional neural network model for dental age estimation using buccal alveolar bone level for saudi children |
| topic | Age estimation Dental age Machine learning Deep learning Buccal alveolar bone level Support vector machine |
| url | https://doi.org/10.1186/s12903-025-06694-9 |
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