Imaging Sci Dent.  2019 Mar;49(1):19-26. 10.5624/isd.2019.49.1.19.

Dental age estimation using the pulp-to-tooth ratio in canines by neural networks

• Farhadian
• Salemi F
• Saati S
• Nafisi N

• KMID: 2442105
• DOI: http://doi.org/10.5624/isd.2019.49.1.19

Abstract

PURPOSE
It has been proposed that using new prediction methods, such as neural networks based on dental data, could improve age estimation. This study aimed to assess the possibility of exploiting neural networks for estimating age by means of the pulp-to-tooth ratio in canines as a non-destructive, non-expensive, and accurate method. In addition, the predictive performance of neural networks was compared with that of a linear regression model.
MATERIALS AND METHODS
Three hundred subjects whose age ranged from 14 to 60 years and were well distributed among various age groups were included in the study. Two statistical software programs, SPSS 21 (IBM Corp., Armonk, NY, USA) and R, were used for statistical analyses.
RESULTS
The results indicated that the neural network model generally performed better than the regression model for estimation of age with pulp-to-tooth ratio data. The prediction errors of the developed neural network model were acceptable, with a root mean square error (RMSE) of 4.40 years and a mean absolute error (MAE) of 4.12 years for the unseen dataset. The prediction errors of the regression model were higher than those of the neural network, with an RMSE of 10.26 years and a MAE of 8.17 years for the test dataset.
CONCLUSION
The neural network method showed relatively acceptable performance, with an MAE of 4.12 years. The application of neural networks creates new opportunities to obtain more accurate estimations of age in forensic research.

Keyword

Forensic Dentistry; Cone-Beam Computed Tomography; Regression Analysis, Neural Networks

MeSH Terms

Cone-Beam Computed Tomography
Dataset
Forensic Dentistry
Humans
Linear Models
Methods
Neural Networks (Computer)

Figure

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  Fig. 1 Pulp-to-tooth-area ratio (AR).

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  Fig. 2 Pulp-to-tooth-length ratio (P).

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  Fig. 3 Buccolingual width measurements of the tooth and pulp at 3 levels in a cross-sectional image.

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  Fig. 4 Mesiodistal width measurements of the tooth and pulp at 3 levels in a panoramic image.

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  Fig. 5 Structure of the developed feed-forward neural networks for age estimation.

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