通过使用机器学习算法从talocrural关节的变量进行性别估计
Abdullah Ray1, Gülçin Ray1, İbrahim Kürtül1
1Department of Anatomy, Faculty of Medicine, Bolu Abant Izzet Baysal University, Bolu, Turkey.
Journal of forensic and legal medicine
|June 11, 2025
概括
从talocrural关节X射线确定性别是可以使用机器学习 (ML). 随机森林分类器实现了92%的准确性,最远点之间的距离 (DOT) 是性别分类中最重要的变量.
科学领域:
- 法医人类学 法医人类学.
- 放射学 放射学是指放射学
- 机器学习 机器学习
背景情况:
- 性别确定在法医人类学中至关重要.
- 传统方法依赖于骨遗骸,但先进的成像和计算技术提供了新的可能性.
- 脚关节,是脚的一部分,具有两性特征.
研究的目的:
- 为了评估机器学习算法用于确定性别的有效性,使用骨关节的放射变量.
- 为了确定哪些骨关节变量最能预测性别.
- 在形态学数据中分析潜在的人口和种族差异.
主要方法:
- 利用300个人的X射线图像 (150名男性,150名女性).
- 提取的形态变量:骨的中侧直径 (TMLD),骨 (FMLD),骨关节最内端 (DIT) 和最外端 (DOT) 点之间的距离,以及远端骨关节表面 (TAS).
- 应用了六种机器学习分类器:逻辑回归,决策树,K-最近邻居,线性差异分析,天真贝叶斯和随机森林分类器 (RFC).
主要成果:
- 机器学习模型的分类准确度在82%至92%之间.
- 随机森林分类器 (RFC) 的准确率最高 (92%).
- 极端点 (DOT) 之间的距离是决定性别的最有影响力的变量. 年龄和FMLD对性别差异没有统计学意义,而其他变量是.
结论:
- 骨关节的放射变量可以使用机器学习准确地按性别分类.
- 机器学习,特别是RFC,提供了一个可靠的工具,用于从脚X射线来确定性别.
- 该研究提供了形态测量数据,并强调了在法医分析中考虑人口和种族差异的重要性.
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