Segmented Bayesian calibration approach for estimating age in forensic science

Abstract

Forensic age estimation is receiving growing attention from researchers in the last few years. Accurate estimates of age are needed both for identifying real age in individuals without any identity document and assessing it for human remains. The methods applied in such context are mostly based on radiological analysis of some anatomical districts and entail the use of a regression model. However, estimating chronological age by regression models leads to overestimated ages in younger subjects and underestimated ages in older ones. We introduced a full Bayesian calibration method combined with a segmented function for age estimation that relied on a Normal distribution as a density model to mitigate this bias. In this way, we were also able to model the decreasing growth rate in juveniles. We compared our new Bayesian-segmented model with other existing approaches. The proposed method helped producing more robust and precise forecasts of age than compared models while exhibited comparable accuracy in terms of forecasting measures. Our method seemed to overcome the estimation bias also when applied to a real data set of South-African juvenile subjects.

Combining Bayesian Calibration and Copula Models for Age Estimation

“…For example, Rynkiewicz et al [19] reported an average accuracy of ±3.5 years using a wrist bone maturation method, while Chen et al [20] reported an average accuracy of ±3.7 years using a combination of dental and wrist bone maturation. Finally, this paper extends to two indices the methodology proposed in Bucci et al [8], where the best-performing method exhibited an accuracy of ±2.8 years. These findings suggest that the model developed in our study is a promising tool for accurately estimating chronological age in children and adolescents.…”

“…The predictive performance of the methodology here presented was compared with that of a single predictor model and a model in which the predictors were considered independent. Given the information found in the existing literature ([1,7,8]), we assumed a segmented relationship with a single breakpoint for the dental maturity index, S, by using a segmented function including one breakpoint [8], and a linear relationship for the hand-wrist maturation index, W. The simulation process is:…”

“…To avoid this issue, Ferrante [7] introduced a Bayesian calibration method with dental maturation as a unique predictor of chronological age. More recently, Bucci [8] followed the same approach using a segmented function to model the relationship between age and dental maturity to consider the changes in the maturation process in juveniles.…”