Age-group determination of living individuals using first molar images based on artificial intelligence

Abstract

Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic radiography. The data set consisted of four first molar images from the right and left sides of the maxilla and mandible of each of 1586 individuals across all age groups, which were extracted from their panoramic radiographs. The accuracy of the tooth-wise estimation was 89.05 to 90.27%. Performance accuracy was evaluated mainly using a majority voting system and area under curve (AUC) scores. The AUC scores ranged from 0.94 to 0.98 for all age groups, which indicates outstanding capacity. The learned features of CNNs were visualized as a heatmap, and revealed that CNNs focus on differentiated anatomical parameters, including tooth pulp, alveolar bone level, or interdental space, depending on the age and location of the tooth. With this, we provided a deeper understanding of the most informative regions distinguished by age groups. The prediction accuracy and heat map analyses support that this AI-based age-group determination model is plausible and useful.

Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters

“…The models presented in the study are characterized by high accuracy. Compared with the work of Kim and co-authors [46], the quality of the model determining the age of men and women was 9 percentage points higher. The R 2 coefficient of the produced model was 0.93; Kim's model had a quality level of accuracy of 0.84.…”

“…The application of artificial neural networks in information and image processing in dentistry was presented by Kim et al in 2021 [46]. They investigated the estimation of age groups by applying a conventional neural network (CNN) using X-ray images of first molars on pantomographic images.…”

Age Group Prediction with Panoramic Radiomorphometric Parameters Using Machine Learning Algorithms

“…By contrast, this study has relative strengths because it can be applied to individuals in their teens and 60s. In our previous study, based on the rst molar image and a CNN, the AUC values of the two extreme age groups were higher than those of the middle-aged group 23 . By further increasing the input data, we need to apply a machine learning model and nd a way to increase the prediction accuracy in the middle-aged group.…”

Age Estimation with Panoramic Radiomorphometric Parameters Using Generalized Linear Models

“…Since 2004, many researchers have evaluated whether variations in pulp chamber size and PTR ratio is an indicator of age. This method of age estimation seems promising in canines and molars on PRs [33][34][35][36]. While those authors obtained acceptable levels of accuracy in age prediction (mean error: 3-9 years), they advised that future research investigate the effect of race and culture on the parameters.…”