Dental age estimation using the pulp-to-tooth ratio in canines by neural networks
Imaging Science in Dentistry
;
: 19-26, 2019.
Artigo
em Inglês
| WPRIM
| ID: wpr-740404
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 ANDMETHODS:
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.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Modelos Lineares
/
Redes Neurais de Computação
/
Tomografia Computadorizada de Feixe Cônico
/
Conjunto de Dados
/
Odontologia Legal
/
Métodos
Tipo de estudo:
Estudo prognóstico
Limite:
Humanos
Idioma:
Inglês
Revista:
Imaging Science in Dentistry
Ano de publicação:
2019
Tipo de documento:
Artigo
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