Variational autoencoder-based estimation of chronological age and changes in morphological features of teeth

This study led to the development of a variational autoencoder (VAE) for estimating the chronological age of subjects using feature values extracted from their teeth. Further, it determined how given teeth images affected the estimation accuracy. The developed VAE was trained with the first molar an...

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Vydáno v:Scientific reports Ročník 13; číslo 1; s. 704 - 11
Hlavní autoři: Joo, Subin, Jung, Won, Oh, Seung Eel
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 13.01.2023
Nature Publishing Group
Nature Portfolio
Témata:
631/114/1305
631/114/1314
631/114/1564
639/166/985
Age
Age determination
Age Determination by Teeth - methods
Artificial Intelligence
Canine teeth
Cuspid
Humanities and Social Sciences
Molar - diagnostic imaging
multidisciplinary
Science
Science (multidisciplinary)
Teeth
ISSN:2045-2322, 2045-2322
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Shrnutí:This study led to the development of a variational autoencoder (VAE) for estimating the chronological age of subjects using feature values extracted from their teeth. Further, it determined how given teeth images affected the estimation accuracy. The developed VAE was trained with the first molar and canine tooth images, and a parallel VAE structure was further constructed to extract common features shared by the two types of teeth more effectively. The encoder of the VAE was combined with a regression model to estimate the age. To determine which parts of the 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 using data from 910 individuals aged 10–79. This model showed a median absolute error (MAE) of 6.99 years, demonstrating its ability to estimate age accurately. Furthermore, 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 artificial intelligence.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-27950-4