A framework to integrate deep learning and an ethnicity-specific reference data set for reliable dental age estimation from digital panoramic radiographs

Abstract

Automated dental age estimation is essential for clinical dentistry. However, traditional methods like Demirjian's tooth development stage (TDS) require time-consuming manual annotation by trained experts. Moreover, population differences in dental development patterns can significantly affect the accuracy of age estimation, highlighting the need for an ethnicity-specific reference data set (RDS). This study aims to use deep neural networks (DNNs) for automated estimation of Demirjian's TDS of molars on digital panoramic radiographs, integrating the most complete southern Chinese RDS for ethnicity-appropriate dental age assessment. Panoramic radiographs from individuals aged 2 to 25 years are annotated for molar TDS and used to train eight DNN architectures, including AlexNet, DenseNet-201, and ResNet-50. DenseNet-201 achieves the highest accuracy of 93% in classifying molar TDS. Most misclassifications involve adjacent stages. The integrated mean dental age (IMDA) is obtained by mapping the predicted molar TDS using the southern Chinese RDS. The mean difference and correlation coefficient between the estimated IMDA from the best-performing DNN (AlexNet) and chronological age are -0.063 years (-3.3 weeks) and r=0.898 (p< 0.001), respectively. These findings demonstrate that combining DNN for TDS estimation with ethnic-specific RDS enables accurate and reliable dental age assessment.

Keywords

Deep neural networks (DNNs) / Dental age estimation / Panoramic radiographs / Southern Chinese / Tooth development

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