Application of machine-learning methods in age-at-death estimation from 3D surface scans of the adult acetabulum

IF 2.2 3区 医学 Q1 MEDICINE, LEGAL Forensic science international Pub Date : 2024-10-28 DOI:10.1016/j.forsciint.2024.112272
Michal Štepanovský , Zdeněk Buk , Anežka Pilmann Kotěrová , Jaroslav Brůžek , Šárka Bejdová , Nawaporn Techataweewan , Jana Velemínská
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Abstract

Objective

Age-at-death estimation is usually done manually by experts. As such, manual estimation is subjective and greatly depends on the past experience and proficiency of the expert. This becomes even more critical if experts need to evaluate individuals with unknown population affinity or with affinity that they are not familiar with. The purpose of this study is to design a novel age-at-death estimation method allowing for automatic evaluation on computers, thus eliminating the human factor.

Methods

We used a traditional machine-learning approach with explicit feature extraction. First, we identified and described the features that are relevant for age-at-death estimation. Then, we created a multi-linear regression model combining these features. Finally, we analysed the model performance in terms of Mean Absolute Error (MAE), Mean Bias Error (MBE), Slope of Residuals (SoR) and Root Mean Squared Error (RMSE).

Results

The main result of this study is a population-independent method of estimating an individual's age-at-death using the acetabulum of the pelvis. Apart from data acquisition, the whole procedure of pre-processing, feature extraction and age estimation is fully automated and implemented as a computer program. This program is a part of a freely available web-based software tool called CoxAGE3D, which is available at https://coxage3d.fit.cvut.cz/. Based on our dataset, the MAE of the presented method is about 10.7 years. In addition, five population-specific models for Thai, Lithuanian, Portuguese, Greek and Swiss populations are also given. The MAEs for these populations are 9.6, 9.8, 10.8, 10.5 and 9.2 years, respectively. Our age-at-death estimation method is suitable for individuals with unknown population affinity and provides acceptable accuracy. The age estimation error cannot be completely eliminated, because it is a consequence of the variability of the ageing process of different individuals not only across different populations but also within a certain population.
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应用机器学习方法从成人髋臼的三维表面扫描中估算死亡年龄。
目的:死亡年龄估计通常由专家手工完成。因此,人工估算具有主观性,在很大程度上取决于专家过去的经验和熟练程度。如果专家需要评估未知种群亲缘关系或他们不熟悉的亲缘关系的个体,这一点就变得更加重要。本研究的目的是设计一种新的死亡年龄估计方法,允许在计算机上进行自动评估,从而消除人为因素:方法:我们采用了传统的机器学习方法,并进行了明确的特征提取。首先,我们确定并描述了与死亡年龄估计相关的特征。然后,我们结合这些特征创建了一个多线性回归模型。最后,我们从平均绝对误差(MAE)、平均偏差误差(MBE)、残差斜率(SoR)和均方根误差(RMSE)等方面分析了模型的性能:本研究的主要成果是利用骨盆髋臼估算个人死亡年龄的一种不受人口影响的方法。除数据采集外,预处理、特征提取和年龄估计的整个过程都是全自动的,并以计算机程序的形式实现。该程序是基于网络的免费软件工具 CoxAGE3D 的一部分,可在 https://coxage3d.fit.cvut.cz/ 上获取。根据我们的数据集,该方法的 MAE 约为 10.7 岁。此外,我们还给出了泰国、立陶宛、葡萄牙、希腊和瑞士人群的五个特定人群模型。这些人群的 MAE 分别为 9.6、9.8、10.8、10.5 和 9.2 岁。我们的死亡年龄估计方法适用于未知人群亲缘关系的个体,并具有可接受的准确性。年龄估计误差不可能完全消除,因为它不仅是不同种群间不同个体衰老过程差异的结果,也是同一种群内不同个体衰老过程差异的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Forensic science international
Forensic science international 医学-医学:法
CiteScore
5.00
自引率
9.10%
发文量
285
审稿时长
49 days
期刊介绍: Forensic Science International is the flagship journal in the prestigious Forensic Science International family, publishing the most innovative, cutting-edge, and influential contributions across the forensic sciences. Fields include: forensic pathology and histochemistry, chemistry, biochemistry and toxicology, biology, serology, odontology, psychiatry, anthropology, digital forensics, the physical sciences, firearms, and document examination, as well as investigations of value to public health in its broadest sense, and the important marginal area where science and medicine interact with the law. The journal publishes: Case Reports Commentaries Letters to the Editor Original Research Papers (Regular Papers) Rapid Communications Review Articles Technical Notes.
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