Age estimation is a crucial step in biological identification in the forensic field. Age estimation is required to identify the deceased, and it is essential for living people, particularly children, and adolescents, to answer numerous legal questions and resolve civil and judicial issues1,2.
Numerous techniques are available for estimating age using various body components. Several studies have focused on the connection between epiphyseal closure and age3,4. Many factors are related to epiphyseal fusion, including sex, genetics, and geography3,5. However, the bone age assessment method is usually used to evaluate immature individuals because of incomplete skeletal development6.
Evaluation of dental age using radiographic tooth development and tooth eruption sequences is more accurate than other methods7,8. As tooth and dental tissue is largely genetically formed and is less susceptible to environmental and dietary influences, there is less deformation caused by external chemical and physical damage2,3,7.
Many attempts have been made to create standards for age estimation using human interpretations of dental radiological images. The most common method, the Demirjian technique, divides teeth into eight categories, A–H, based on their maturity and degree of calcification9. Willems et al. modified the Demirjian method and provided a new scoring method that allowed direct conversion from classification to age10. Cameriere established a European formula by gauging the open apices of seven permanent teeth in the left mandible on panoramic radiographs11. However, these methods have a certain degree of subjectivity, leading to a relatively high level of personal error, and their application requires adequate experience to minimize errors12. Also, there are fundamental limitations to its applicability in young subjects.
Machine learning, the cornerstone of artificial intelligence, enables more precise and effective dental age prediction12–14. Tao and Galibourg applied machine learning to the Demirjian and Willams method for dental age estimation13,14, and Shihui et al. used the Cameriere method12. Most studies related to age estimation use CNN-based models15–18. CNN-based models learn local features well because of the convolution filter operation but do not learn global information well. This problem can be solved by learning local features and global information using a vision transformer (ViT)19. In addition, the hybrid method, which uses the feature map extracted from the CNN-based model as input to the ViT model, showed better image classification performance than using each model alone19. Therefore, this study used a hybrid method to design an age estimation model because learning by considering both local features and global information is important for estimating age.
This study aimed to construct an age estimation model using a hybrid method of the ResNet50 and ViT models. Subsequently, we aimed to confirm whether the model performs better so that it can be used effectively in clinical field.