基于使用经典和机器学习分类器对全景射线图的牙测量进行性别估计
Samaneh Talebi1, Hossien Fallahzadeh1, Sara Jambarsang1
1Center for Healthcare Data Modeling, Department of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Frontiers in dentistry
|July 9, 2025
概括
从全景射线影像中测量部左头的测量可以估计伊朗人的性别. 在水泥结处的冠长 (CEJL) 是一个可靠的指标,在6.25以上的值表明男性性别.
科学领域:
- 法医人类学 法医人类学
- 牙科放射学 牙科放射学
- 机器学习在法医中
背景情况:
- 准确的性别估计在法医鉴定中至关重要.
- 牙科指标,特别是从牙上,为性别确定提供了潜在的途径.
- 全景放射提供了一个易于访问的成像模式,用于牙科分析.
研究的目的:
- 为了评估上左头的有效性,在伊朗人口中对全景射线图进行第一个骨测量,以估计性别.
- 为了比较经典和机器学习分类器在此性别估计任务中的性能.
主要方法:
- 对来自伊朗人的131张全景射线图 (18-30岁) 的横截面研究.
- 测量牙长度和牙宽度的变量,大的左侧第一个牙.
- 规范差别分析 (RDA),支持向量机 (SVM) 和神经网络模型用于性别估计的应用.
- 使用类内相关系数 (ICC) 评估观察者内部可靠性.
主要成果:
- 实现了高的观察者内部可靠性 (ICC=0.9).
- 支持矢量机 (SVM) 在性别估计方面表现最好.
- 在水泥接处 (CEJL) 的冠长被确定为具有最高分辨潜力的关键变量 (AUC=78.8).
- 与单个变量相比,变量的比率并没有显著提高性别估计的准确性.
结论:
- 在水泥结处的皇冠长度 (CEJL) 是伊朗人口中性别估计的可靠指标.
- 6.25毫米的CEJL值可以区分男性和女性个体.
- 机器学习,特别是SVM,显示了通过X光牙科测量来提高性别估计准确性的前景.
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