Predictive Artificial Intelligence Model for Detecting Dental Age Using Panoramic Radiograph Images

Abstract

Predicting dental development in individuals, especially children, is important in evaluating dental maturity and determining the factors that influence the development of teeth and growth of jaws. Dental development can be accelerated in patients with an accelerated skeletal growth rate and can be related to the skeletal growth pattern as a child. The dental age (DA) of an individual is essential to the dentist for planning treatment in relation to maxillofacial growth. A deep-learning-based regression model was developed in this study using panoramic radiograph images to predict DA. The dataset included 529 samples of panoramic radiographs collected from the dental hospital at Imam Abdulrahman Bin Faisal university in Saudi Arabia. Different deep learning methods were applied to implement the model, including Xception, VGG16, DenseNet121, and ResNet50. The results indicated that the Xception model had the best performance, with an error rate of 1.417 for the 6–11 age group. The proposed model can assist the dentist in determining the appropriate treatment for patients based on their DA rather than their chronological age.

Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs—A Systematic Review

“…AI has primarily been used for automated age estimation by analyzing tooth development stages [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ], tooth and bone parameters [ 48 , 49 , 50 ], bone age measurements [ 51 ], and pulp–tooth ratio [ 52 , 53 ]. We gathered data from the studies included, but due to the varied data samples used to assess AI model performance, a meta-analysis could not be conducted.…”

Estimating chronological age through learning local and global features of panoramic radiographs in the Korean population

“…We used two types of age estimation models. The first is ResNet, a well-known CNN-based model which has been used as a feature extractor in many studies related to age prediction 24 , 25 . ResNet can build deep layers by solving the gradient vanishing problem through residual learning using skip connection 26 .…”

Integrating artificial intelligence and adult dental age estimation in forensic identification: A literature review

“…Beyond age estimation, AI holds the potential to forecast the probability of specific dental conditions and diseases based on patient data, thus aiding in preventive measures and treatment strategies. Nonetheless, the field of AI-based age estimation in dentistry is nascent, lacking a universally accepted approach for adults with permanent dentition [7,32].…”