通过机器学习评估骨测量对性别估计的有效性
Diana Toneva1, Silviya Nikolova1, Gennady Agre2
1Institute of Experimental Morphology, Pathology and Anthropology with Museum, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria.
Biology
|July 29, 2025
概括
这项研究分析了来自CT扫描的骨测量结果,以确定性别差异. 机器学习模型在性别估计中实现了95-100%的准确性,突出了骨盆解剖中的显著性二态.
科学领域:
- 法医人类学 法医人类学
- 人类解剖学 人类解剖学
- 生物识别信息 生物识别信息
背景情况:
- 人的骨盆表现出显著的性二态,主要是由于其在分娩中的作用.
- 性二态特征在骨中尤为突出,骨是骨盆腰带和出生通道的关键组成部分.
研究的目的:
- 为了量化骨尺寸的性别差异.
- 开发和评估机器学习模型以使用骨形态测量来估计性别.
主要方法:
- 使用计算机断层扫描 (CT) 对276名保加利亚成年人进行扫描.
- 创建了3D骨盆模型,并从骨中收集了34个地标坐标.
- 计算了各种骨测量,并根据性别,年龄和横向性分析了差异.
- 训练有素的机器学习模型 (支持矢量机器,后勤回归) 用于性别分类.
主要成果:
- 在骨尺寸中表现出显著的性二态.
- 鉴定了骨盆形态的轻微双边和与年龄有关的变化.
- 使用开发的机器学习模型,在性别估计方面取得了高准确性 (95-100%).
结论:
- 骨形态表现出明显的两性变异,使其成为估计性别的可靠指标.
- 机器学习方法有效地利用骨测量,在法医和人类学背景下准确地确定性别.
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