Secondary dentine is responsible for a decrease in the volume of the dental pulp cavity with aging. The aim of this study is to evaluate a human dental age estimation method based on the ratio between the volume of the pulp and the volume of its corresponding tooth, calculated on clinically taken cone beam computed tomography (CBCT) images from monoradicular teeth. On the 3D images of 111 clinically obtained CBCT images (Scanora(®) 3D dental cone beam unit) of 57 female and 54 male patients ranging in age between 10 and 65 years, the pulp-tooth volume ratio of 64 incisors, 32 canines, and 15 premolars was calculated with Simplant(®) Pro software. A linear regression model was fit with age as dependent variable and ratio as predictor, allowing for interactions of specific gender or tooth type. The obtained pulp-tooth volume ratios were the strongest related to age on incisors.

Dental age estimation methods based on the radiologically detected third molar developmental stages are implemented in forensic age assessments to discriminate between juveniles and adults considering the judgment of young unaccompanied asylum seekers. Accurate and unbiased age estimates combined with appropriate quantified uncertainties are the required properties for accurate forensic reporting. In this study, a subset of 910 individuals uniformly distributed in age between 16 and 22 years was selected from an existing dataset collected by Gunst et al. containing 2,513 panoramic radiographs with known third molar developmental stages of Belgian Caucasian men and women. This subset was randomly split in a training set to develop a classical regression analysis and a Bayesian model for the multivariate distribution of the third molar developmental stages conditional on age and in a test set to assess the performance of both models. The aim of this study was to verify if the Bayesian approach differentiates the age of maturity more precisely and removes the bias, which disadvantages the systematically overestimated young individuals. The Bayesian model offers the discrimination of subjects being older than 18 years more appropriate and produces more meaningful prediction intervals but does not strongly outperform the classical approaches.

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