Variational Autoencoder-based Estimation of Chronological Age and Changes in Morphological Features of Teeth

The present study developed a variational autoencoder (VAE) capable of estimating the chronological age of subjects using feature values extracted from their teeth, and further determine how given teeth images affected the accuracy of estimation. The developed VAE was trained with first molar and canine teeth images, and a parallel-VAE structure was further constructed to extract common features shared by the two types of teeth in a more effective manner. The encoder of the VAE was designed to be combined with a regression model to estimate age. In an attempt to determine which parts of tooth images were more or less important when estimating age, a method of visualizing the obtained regression coefficient using the decoder of the VAE was developed. The developed age estimation model was trained with data obtained from a total of 910 individuals aged 10–79 years. This model showed a median absolute error (MAE) of 6.99 years, demonstrating its ability to estimate age accurately. Further, this method of visualizing the influence of particular parts of tooth images on the accuracy of age estimation using a decoder is expected to provide novel insights for future research on explainable AI.