Department of Statistics and Computer Science, University of Florence, Viale Morgagni 59, 50134, Florence, Italy.
Int J Legal Med. 2013 Nov;127(6):1157-64. doi: 10.1007/s00414-013-0919-3. Epub 2013 Oct 1.
Criminal cases involving young people, irregular immigration and the many issues related to asylum seekers has increased the judicial demand of age estimation. Calcification of teeth and specifically of third molar has demonstrated to be reliable evidence to estimate age respect to 18 years threshold of age.
As prosecution of research of Pinchi et al. (2010) and Corradi et al. (J Forensic Sci 58:51-59, 2013), the study aims to evaluate if tuning the size of the zone of indifference posed around the age threshold improves the performances of the age classification model.
The sample was composed of 1,560 OPGs of Italian subjects aged between 15 and 22 years. Third molar calcification stage was assessed according to Demirjian's scale by three different experts. Intra- and inter-operator variability has been calculated. The statistical analysis was provided by a Modified Naïve Bayesian allowing the use of soft evidence. Rate of in/correct classification was provided for individuals classified at a very high level of probability (90 %), as needed for criminal cases, and for a lower probability level (51 %) as it suffices for civil cases.
The intra-observer reproducibility varies between 79.2 and 89.2 % with soft evidence, whilst it decreases from values between 0.589 and 0.763, when only hard evidence is allowed to experts showing the usefulness of the MBN approach. In civil cases, imposing the constraint of classifying at least 95 % of the individuals, the method achieved a rate of correct classification in the range 80-83 % depending on the expert. In criminal cases, we tuned the ZOI size to achieve 85 % of individuals correctly classified and the model succeeded in classifying 66-81 % of the sample, the variability still being dependent on the expert's ability.
After a review of several studies concerning the age classification of young individuals by using dental evidence, we must conclude that it is almost impossible to make a comparison among them. To rank the effectiveness of different methods is to challenge them with the same problem and data, looking at the results measured by the same accepted scoring rule. It could also be interesting to repeat the experiment in different conditions varying the reference population and considering if some important covariates, like sex and health status, influence the model performances.
涉及年轻人、非正常移民和寻求庇护者的诸多问题的刑事案件增加了年龄估计的司法需求。牙齿，特别是第三磨牙的钙化已被证明是估计 18 岁年龄界限的可靠证据。
继 Pinchi 等人的研究（2010 年）和 Corradi 等人的研究（J Forensic Sci 58:51-59, 2013 年）之后，本研究旨在评估围绕年龄阈值调整不确定区域的大小是否会提高年龄分类模型的性能。
样本由 1560 名年龄在 15 至 22 岁之间的意大利受试者的 OPG 组成。第三磨牙钙化阶段根据 Demirjian 量表由三位不同的专家进行评估。计算了内部和外部观察者的变异性。统计分析由改良的朴素贝叶斯法提供，允许使用软证据。对于在非常高的概率（90%）下被分类的个体（如刑事案件所需）和概率较低的个体（51%），提供了分类的正确率/错误率。
当允许专家使用软证据时，内部观察者的可重复性在 79.2%至 89.2%之间变化，而当只允许使用硬证据时，可重复性从 0.589 至 0.763 之间降低，表明 MBN 方法的有用性。在民事案件中，规定至少对 95%的个体进行分类的约束，该方法达到了 80-83%的正确分类率，具体取决于专家。在刑事案件中，我们调整了 ZOI 的大小，以实现 85%的个体正确分类，模型成功地对 66-81%的样本进行了分类，其变异性仍然取决于专家的能力。
在对使用牙科证据对年轻个体进行年龄分类的多项研究进行审查后，我们必须得出结论，几乎不可能对它们进行比较。通过使用相同的问题和数据来挑战不同的方法，并根据相同的公认评分规则衡量结果，从而对不同方法的有效性进行排名。在不同的条件下重复实验，改变参考人群，并考虑一些重要的协变量（如性别和健康状况）是否会影响模型性能，这也可能很有趣。
