Department of Anthropology and Human Genetics, Faculty of Science, Charles University, Viničná 7, 128 44, Prague 2, Czech Republic.
UMR 5199 - PACEA, Université de Bordeaux, Bâtiment B2, Allée Geoffroy Saint Hilaire, CS 50023, 33615, Pessac Cedex, France.
Int J Legal Med. 2024 Jul;138(4):1759-1768. doi: 10.1007/s00414-024-03215-1. Epub 2024 Mar 26.
An increasing number of software tools can be used in forensic anthropology to estimate a biological profile, but further studies in other populations are required for more robust validation. The present study aimed to evaluate the validity of MorphoPASSE software for sex estimation from sexually dimorphic cranial traits recorded on 3D CT models (n = 180) from three populations samples (Czech, French, and Egyptian). Two independent observers performed scoring of 4 cranial traits (2 of them bilateral) in each population sample of 30 males and 30 females. The accuracy of sex estimation using traditional posterior probability threshold (pp = 0.5) ranged from 85.6% to 88.3% and overall classification error from 14.4% to 11.7% for both observers, and corresponds to the previously published values of the method. The MorphoPASSE method is also affected by the subjectivity of the observers, as both observers show agreement in sex assignment in 83.9% of cases, regardless of the accuracy of the estimates. Applying a higher posterior probability threshold (pp 0.95) provided classification accuracy of 97.9% and 93.3% of individuals (for observer A and B respectively), minimizing the risk of error to 2.1% and 6.7%, respectively. However, sex estimation can only be applied to 54% and 66% of individuals, respectively. Our results demonstrate the validity of the MorphoPASSE software for cranial sex estimation outside the reference population. However, the achieved classification success is accompanied by a high risk of errors, the reduction of which is only possible by increasing the posterior probability threshold.
越来越多的软件工具可用于法医人类学中,以估计生物指标,但需要在其他人群中进行更多研究,以实现更可靠的验证。本研究旨在评估 MorphoPASSE 软件在三个群体样本(捷克、法国和埃及)的 3D CT 模型上记录的性别可识别颅骨特征(n = 180)方面的性别估计有效性。两位独立观察者对来自三个群体样本的 30 名男性和 30 名女性的每个群体样本中的 4 个颅骨特征(其中 2 个为双侧)进行评分。使用传统的后验概率阈值(pp = 0.5)进行性别估计的准确性为 85.6%至 88.3%,两位观察者的总体分类误差为 14.4%至 11.7%,与该方法之前的发表值相符。MorphoPASSE 方法也受到观察者主观性的影响,因为两位观察者在 83.9%的情况下在性别分配上达成一致,而不管估计的准确性如何。应用更高的后验概率阈值(pp 0.95)可将分类准确性提高到 97.9%和 93.3%的个体(分别对应观察者 A 和 B),将错误风险分别最小化至 2.1%和 6.7%。然而,性别估计只能分别应用于 54%和 66%的个体。我们的结果表明,MorphoPASSE 软件在外群中进行颅骨性别估计是有效的。然而,实现的分类成功率伴随着高错误风险,只有通过增加后验概率阈值才能降低这种风险。