Centre for Forensic Anthropology, University of Western Australia, Crawley, WA, 6009, Australia.
Department of Forensic Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan.
Int J Legal Med. 2024 Jul;138(4):1381-1390. doi: 10.1007/s00414-024-03178-3. Epub 2024 Feb 6.
The present study analyzes morphological differences in the pelvis of Japanese and Western Australian individuals and investigates the feasibility of population affinity classification based on computed tomography (CT) data. The Japanese and Western Australian samples comprise CT scans of 207 (103 females; 104 males) and 158 (78 females; 80 males) adult individuals, respectively. Following volumetric reconstruction, a total of 19 pelvic landmarks were obtained on each sample, and 11 measurements, including two angles, were calculated. Machine learning methods (random forest modeling [RFM] and support vector machine [SVM]) were used to classify population affinity. Classification accuracy of the two-way models was approximately 80% for RFM: the two-way sex-specific and sex-mixed models for SVM achieved > 90% and > 85%, respectively. The sex-specific models had higher accurate classification rates than the sex-mixed models, except for the Japanese male sample. The classification accuracy of the four-way sex and population affinity model had an overall classification accuracy of 76.71% for RFM and 87.67% for SVM. All the correct classification rates were higher in the Japanese relative to the Western Australian sample. Our data suggest that pelvic morphology is sufficiently distinct between Japanese and Western Australian individuals to facilitate the accurate classification of population affinity based on measurements acquired in CT images. To the best of our knowledge, this is the first study investigating the feasibility of population affinity estimation based on CT images of the pelvis, which appears as a viable supplement to traditional approaches based on cranio-facial morphology.
本研究分析了日本和西澳大利亚个体骨盆的形态差异，并探讨了基于计算机断层扫描（CT）数据进行人群亲和分类的可行性。日本和西澳大利亚样本分别包含 207 名（103 名女性；104 名男性）和 158 名（78 名女性；80 名男性）成年个体的 CT 扫描。进行容积重建后，在每个样本上共获得 19 个骨盆标志点，并计算了 11 个测量值，包括两个角度。使用机器学习方法（随机森林建模[RFM]和支持向量机[SVM]）对人群亲和性进行分类。RFM 中双向模型的分类准确率约为 80%：SVM 的双向性别特异性和性别混合模型分别达到了>90%和>85%。除了日本男性样本外，性别特异性模型的准确分类率高于性别混合模型。四向性别和人群亲和性模型的分类准确率，RFM 为 76.71%，SVM 为 87.67%。所有正确分类率在日本样本中均高于西澳大利亚样本。我们的数据表明，日本和西澳大利亚个体的骨盆形态差异足以准确地根据 CT 图像测量值进行人群亲和性分类。据我们所知，这是首次研究基于骨盆 CT 图像进行人群亲和性估计的可行性，这似乎是对基于头面部形态的传统方法的可行补充。
