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논문 기본 정보

자료유형
학술저널
저자정보
Farhadian, Maryam (Department of Biostatistics, School of Public Health and Research Center for Health Sciences, Hamadan University of Medical Sciences) Salemi, Fatemeh (Department of Oral and Maxillofacial Radiology, Dental School, Hamadan University of Medical Sciences) Saati, Samira (Department of Oral and Maxillofacial Radiology, Dental School, Hamadan University of Medical Sciences) Nafisi, Nika (Department of Oral and Maxillofacial Radiology, Dental School, Hamadan University of Medical Sciences)
저널정보
대한영상치의학회 Imaging science in dentistry Imaging science in dentistry 제49권 제1호
발행연도
2019.1
수록면
19 - 26 (8page)

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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.

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