Exploratory analysis of new craniometric measures for the investigation of biological sex using open-access statistical and machine-learning tools on a cone-beam computed tomography sample.

IF 2.2 3区 医学 Q1 MEDICINE, LEGAL International Journal of Legal Medicine Pub Date : 2024-11-01 Epub Date: 2024-06-10 DOI:10.1007/s00414-024-03259-3
Carla Reis Machado, Janaina Paiva Curi, Cícero André da Costa Moraes, Letícia Vilela Santos, Rodolfo Francisco Haltenhoff Melani, Israel Chilvarquer, Thiago Leite Beaini
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Abstract

Investigation of the biological sex of human remains is a crucial aspect of physical anthropology. However, due to varying states of skeletal preservation, multiple approaches and structures of interest need to be explored. This research aims to investigate the potential use of distances between bifrontal breadth (FMB), infraorbital foramina distance (IOD), nasal breadth (NLB), inter-canine width (ICD), and distance between mental foramina (MFD) for combined sex prediction through traditional statistical methods and through open-access machine-learning tools. Ethical approval was obtained from the ethics committee, and out of 100 cone beam computed tomography (CBCT) scans, 54 individuals were selected with all the points visible. Ten extra exams were chosen to test the predictors developed from the learning sample. Descriptive analysis of measurements, standard deviation, and standard error were obtained. T-student and Mann-Whitney tests were utilized to assess the sex differences within the variables. A logistic regression equation was developed and tested for the investigation of the biological sex as well as decision trees, random forest, and artificial neural networks machine-learning models. The results indicate a strong correlation between the measurements and the sex of individuals. When combined, the measurements were able to predict sex using a regression formula or machine learning based models which can be exported and added to software or webpages. Considering the methods, the estimations showed an accuracy rate superior to 80% for males and 82% for females. All skulls in the test sample were accurately predicted by both statistical and machine-learning models. This exploratory study successfully established a correlation between facial measurements and the sex of individuals, validating the prediction potential of machine learning, augmenting the investigative tools available to experts with a high differentiation potential.

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在锥形束计算机断层扫描样本上使用开放式统计和机器学习工具,对用于调查生物性别的新头颅测量方法进行探索性分析。
调查人类遗骸的生物性别是体质人类学的一个重要方面。然而,由于骨骼保存状况各不相同,需要探索多种方法和相关结构。本研究旨在通过传统统计方法和开放存取的机器学习工具,研究双额宽(FMB)、眶下孔距(IOD)、鼻宽(NLB)、犬间宽(ICD)和心孔距(MFD)之间的距离在综合性别预测中的潜在用途。该研究获得了伦理委员会的伦理批准,并从 100 张锥形束计算机断层扫描(CBCT)扫描图像中选取了 54 人的所有点均可见。另外还选择了 10 项检查来测试从学习样本中开发的预测因子。对测量结果、标准偏差和标准误差进行了描述性分析。利用 T-student 和 Mann-Whitney 检验来评估变量中的性别差异。为调查生理性别,还开发并测试了逻辑回归方程以及决策树、随机森林和人工神经网络机器学习模型。结果表明,测量结果与个体性别之间存在很强的相关性。将这些测量结果结合起来,就能利用回归公式或基于机器学习的模型预测性别,这些模型可以导出并添加到软件或网页中。考虑到这些方法,估计结果显示男性的准确率超过 80%,女性的准确率超过 82%。统计和机器学习模型都能准确预测测试样本中的所有头骨。这项探索性研究成功地建立了面部测量与个人性别之间的相关性,验证了机器学习的预测潜力,增强了专家可用的调查工具,具有很高的区分潜力。
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来源期刊
CiteScore
5.80
自引率
9.50%
发文量
165
审稿时长
1 months
期刊介绍: The International Journal of Legal Medicine aims to improve the scientific resources used in the elucidation of crime and related forensic applications at a high level of evidential proof. The journal offers review articles tracing development in specific areas, with up-to-date analysis; original articles discussing significant recent research results; case reports describing interesting and exceptional examples; population data; letters to the editors; and technical notes, which appear in a section originally created for rapid publication of data in the dynamic field of DNA analysis.
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