Age Group Classification of Dental Radiography without Precise Age Information Using Convolutional Neural Networks

Abstract

Automatic age estimation using panoramic dental radiographic images is an important procedure for forensics and personal oral healthcare. The accuracies of the age estimation have increased recently with the advances in deep neural networks (DNN), but DNN requires large sizes of the labeled dataset which is not always available. This study examined whether a deep neural network is able to estimate tooth ages when precise age information is not given. A deep neural network model was developed and applied to age estimation using an image augmentation technique. A total of 10,023 original images were classified according to age groups (in decades, from the 10s to the 70s). The proposed model was validated using a 10-fold cross-validation technique for precise evaluation, and the accuracies of the predicted tooth ages were calculated by varying the tolerance. The accuracies were 53.846% with a tolerance of ±5 years, 95.121% with ±15 years, and 99.581% with ±25 years, which means the probability for the estimation error to be larger than one age group is 0.419%. The results indicate that artificial intelligence has potential not only in the forensic aspect but also in the clinical aspect of oral care.

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.…”

“…The standardized methods for entering data into AI technology helped mitigate bias in the flow and timing domain. Nevertheless, two of the studies (15.38%) [ 37 , 46 ] failed to clearly delineate the reference standard employed, giving rise to inherent bias concerns in the index test, reference standard, flow, and timing domains. Another (7.69%) study [ 46 ] relied on notations from solitary observations as a gold standard, culminating in a high risk of bias with respect to index tests.…”

“…Nevertheless, two of the studies (15.38%) [ 37 , 46 ] failed to clearly delineate the reference standard employed, giving rise to inherent bias concerns in the index test, reference standard, flow, and timing domains. Another (7.69%) study [ 46 ] relied on notations from solitary observations as a gold standard, culminating in a high risk of bias with respect to index tests. Despite the above-mentioned issues, both research arms exhibited minimal risk of bias in all the studies considered.…”