Anat Cell Biol.  2023 Dec;56(4):474-481. Dental characteristics on panoramic radiographs as.

Dental characteristics on panoramic radiographs as parameters for non-invasive age estimation: a pilot study

• Cheong H
• Kumagai A
• Oh S
• Lee SS

• KMID: 2548903
• DOI: http://doi.org/Dental characteristics on panoramic radiographs as

Abstract

The dental characteristics created by acquired dental treatments can be used as age estimators. This pilot study aimed to analyze the correlation between the number of teeth observed for dental characteristics and chronological age and to develop new non-invasive age estimation models. Dental features on panoramic radiographs (420 radiographs of subjects aged 20–89 years) were classified and coded. The correlation between the number of teeth for each selected code (codes V, X, T, F, P, and L) and age was observed, and multiple regression was performed to analyze the relationship between them. Eleven regression models with various combinations of dental sextants were presented. The model with the data from both sides of the posterior teeth on both jaws showed the best performance (root mean square error of 14.78 years and an adjusted R 2 of 0.461). The model with all teeth was the second-best. Based on these results, we confirmed statistically significant correlations between certain dental features and chronological age. We also observed that some regression models performed sufficiently well to be used as adjunctive methods in forensic practice. These results provide valuable information for the design and performance of future full-scale studies.

Keyword

Age determination by teeth; Dental characteristics; Non-invasive; Panoramic radiography

Figure

• 

  Fig. 1 Age-related changes in mean numbers of teeth by dental codes. The x-axis represents the age group (in years), and the y-axis represents the mean number of teeth observed. The definitions for each code are provided in Table 2.

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  Fig. 2 Comparison of coefficient of determination (adjusted R2) values for each regression model. The x-axis represents adjusted R2 values, and the y-axis represents regression models based on various combinations of sextants.

Reference

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