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. 2023 Dec 15;23(1):1007.
doi: 10.1186/s12903-023-03745-x.

Application of entire dental panorama image data in artificial intelligence model for age estimation

Se Hoon Kahm  1 , Ji-Youn Kim  2 , Seok Yoo  3 , Soo-Mi Bae  4 , Ji-Eun Kang  5 , Sang Hwa Lee  6
Affiliations

Application of entire dental panorama image data in artificial intelligence model for age estimation

Se Hoon Kahm et al. BMC Oral Health. .

Abstract

Background: Accurate age estimation is vital for clinical and forensic purposes. With the rapid advancement of artificial intelligence(AI) technologies, traditional methods relying on tooth development, while reliable, can be enhanced by leveraging deep learning, particularly neural networks. This study evaluated the efficiency of an AI model by applying the entire panoramic image for age estimation. The outcome performances were analyzed through supervised learning (SL) models.

Methods: Total of 27,877 dental panorama images from 5 to 90 years of age were classified by 2 types of grouping. In type 1 they were classified by each age and in type 2, applying heuristic grouping, the age over 20 years were classified by every 5 years. Wide ResNet (WRN) and DenseNet (DN) were used for supervised learning. In addition, the analysis with ± 3 years of deviation in both types were performed.

Results: For the DN model, while the type 1 grouping achieved an accuracy of 0.1016 and F1 score of 0.058, the type 2 achieved an accuracy of 0.3146 and F1 score of 0.2027. Incorporating ± 3years of deviation, the accuracy of type 1 and 2 were 0.281, 0.7323 respectively; and the F1 score were 0.1768, 0.6583 respectively. For the WRN model, while the type 1 grouping achieved an accuracy of 0.1041 and F1 score of 0.0599, the type 2 achieved an accuracy of 0.3182 and F1 score of 0.2071. Incorporating ± 3years of deviation, the accuracy of type 1 and 2 were 0.2716, 0.7323 respectively; and the F1 score were 0.1709, 0.6437 respectively.

Conclusions: The application of entire panorama image data for supervised with classification by heuristics grouping with ± 3years of deviation for supervised learning models and demonstrated satisfactory outcome for the age estimation.

Keywords: Age determination; Artificial intelligence; Deep learning; Forensic dentistry; Panoramic radiography.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Image data acquisition process through CDW & EDP system
Figs. 2
Figs. 2
a and b. Confusion matrix of the results by DenseNet. 2a results before heuristic grouping (type1gourping). 2b, results after heuristic grouping (type 2 grouping)
Figs. 3
Figs. 3
a and b. Confusion matrix of the results by WideResNet. 3a results before heuristic grouping (type1gourping). 3b, results after heuristic grouping (type 2 grouping)
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