Bucci Andrea, Skrami Edlira, Faragalli Andrea, Gesuita Rosaria, Cameriere Roberto, Carle Flavia, Ferrante Luigi
Centre of Epidemiology, Biostatistics and Medical Information Technology, Università Politecnica delle Marche, Ancona, Italy.
Institute of Legal Medicine, Università degli Studi di Macerata, Macerata, Italy.
Biom J. 2019 Nov;61(6):1575-1594. doi: 10.1002/bimj.201900016. Epub 2019 Aug 7.
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.
近年来，法医年龄估计越来越受到研究人员的关注。对于没有任何身份证件的个人确定实际年龄以及对人类遗骸进行年龄评估，都需要准确的年龄估计。在此背景下应用的方法大多基于对某些解剖区域的放射学分析，并需要使用回归模型。然而，通过回归模型估计实足年龄会导致年轻受试者的年龄被高估，而年长受试者的年龄被低估。我们引入了一种全贝叶斯校准方法，并结合了一种分段函数进行年龄估计，该方法依赖正态分布作为密度模型来减轻这种偏差。通过这种方式，我们还能够对青少年生长速率的下降进行建模。我们将新的贝叶斯分段模型与其他现有方法进行了比较。与对比模型相比，所提出的方法有助于生成更稳健、精确的年龄预测，同时在预测指标方面表现出相当的准确性。当应用于南非青少年受试者的真实数据集时，我们的方法似乎也克服了估计偏差。
