Sex estimation from patellar measurements in a contemporary Italian population: a machine learning approach.

IF 2.2 3区 医学 Q1 MEDICINE, LEGAL International Journal of Legal Medicine Pub Date : 2024-11-04 DOI:10.1007/s00414-024-03359-0
Siam Knecht, Paolo Morandini, Lucie Biehler-Gomez, Luisa Nogueira, Pascal Adalian, Cristina Cattaneo
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Abstract

Biological sex estimation in forensic anthropology is a crucial topic, and the patella has shown promise in this regard due to its sexual dimorphism. This study uses 12 machine learning models for sex estimation based on three patellar measurements (maximum height, breadth, and thickness). Data was collected from 180 skeletons of a contemporary Italian population (83 males and 97 females) as well as from an independent sample of 21 forensic cases (13 males and 8 females). Statistical analyses indicated that each of the variables exhibited significant sexual dimorphism. To predict biological sex, the classifiers were built using 70% of a reference sample, then tested on the remaining 30% of the original sample and then tested again on the independent sample. The different classifiers generated accuracies varied between 0.85 and 0.91 on the reference sample and between 0.71 and 0.95 for the validation sample. SVM classifier stood out with the highest accuracy and seemed the best model for our study.This study contributes to the growing application of machine learning in forensic anthropology by being the first to apply such techniques to patellar measurements in an Italian population. It aims to enhance the accuracy and efficiency of biological sex estimation from the patella, building on promising results observed with other skeletal elements.

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从当代意大利人口的髌骨测量结果推测性别:一种机器学习方法。
法医人类学中的生物性别估计是一个重要课题,而髌骨因其性别二形性而在这方面大有可为。本研究使用 12 个机器学习模型,根据三个髌骨测量值(最大高度、宽度和厚度)进行性别估计。数据收集自 180 具当代意大利人的骨骼(83 具男性骨骼和 97 具女性骨骼)以及 21 个法医案例(13 个男性案例和 8 个女性案例)的独立样本。统计分析表明,每个变量都表现出明显的性别二态性。为了预测生物性别,使用 70% 的参考样本建立了分类器,然后在原始样本的其余 30% 上进行测试,最后在独立样本上再次进行测试。不同分类器对参考样本的准确度在 0.85 和 0.91 之间,对验证样本的准确度在 0.71 和 0.95 之间。本研究首次将机器学习技术应用于意大利人群的髌骨测量,为机器学习在法医人类学中的日益广泛应用做出了贡献。本研究首次将机器学习技术应用于意大利人群的髌骨测量,旨在提高通过髌骨进行生物性别估计的准确性和效率,同时借鉴在其他骨骼元素上观察到的良好结果。
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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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