目的:性别评估是法医专业知识的一个重要方面。一些特殊的解剖结构,比如上颌窦,在恶劣的环境条件下仍然可以保持完整性,并且可以作为性别估计的基础。由于性别估计的复杂性,已经进行了一些研究,使用不同的机器学习算法,以提高从解剖测量性别预测的准确性。
方法:在本研究中,通过使用锥形束计算机断层扫描(CBCT)收集中国西北地区人群上颌窦的线性数据,K-近邻(KNN),支持向量机(SVM)和随机森林(RF)模型的性别估计与R4.3.1。来自477个汉族人群样本的CBCT图像(75个男性和81个女性,5-17岁;162名男性和159名女性,18-72岁)用于建立和验证模型。长度(MSL),宽度(MSW),测量左右上颌窦的高度(MSH)和两个上颌窦之间的侧壁距离(距离)。80%的数据被随机挑选为训练集,其他数据为测试集。此外,这些样本按年龄段和拟合模型进行分组作为尝试.
结果:总体而言,测试集上18岁以上个体的性别估计准确率为77.78%,男性的准确率为78.12%,女性为77.42%。然而,18岁以下个体性别估计的准确性具有挑战性.与物流相比,KNN和SVM,RF表现出更高的准确率。此外,将年龄作为变量提高了性别估计的准确性,特别是在18-27岁年龄段,其中准确率提高到88.46%。同时,所有变量均与年龄呈线性相关.
结论:上颌窦的线性测量可能是18岁及以上个体性别评估的有价值的工具。已开发出一种稳健的RF模型,用于居住在中国西北地区的汉族人口中的性别估计。当使用年龄作为预测变量时,性别估计的准确性可能会更高。
OBJECTIVE: Sex estimation is a critical aspect of forensic expertise. Some special anatomical structures, such as the maxillary sinus, can still maintain integrity in harsh environmental conditions and may be served as a basis for sex estimation. Due to the complex nature of sex estimation, several studies have been conducted using different machine learning algorithms to improve the accuracy of sex prediction from anatomical measurements.
METHODS: In this study, linear data of the maxillary sinus in the population of northwest China by using Cone-Beam Computed Tomography (CBCT) were collected and utilized to develop logistic, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and random forest (RF) models for sex estimation with R 4.3.1. CBCT images from 477 samples of Han population (75 males and 81 females, aged 5-17 years; 162 males and 159 females, aged 18-72) were used to establish and verify the model. Length (MSL), width (MSW), height (MSH) of both the left and right maxillary sinuses and distance of lateral wall between two maxillary sinuses (distance) were measured. 80% of the data were randomly picked as the training set and others were testing set. Besides, these samples were grouped by age bracket and fitted models as an attempt.
RESULTS: Overall, the accuracy of the sex estimation for individuals over 18 years old on the testing set was 77.78%, with a slightly higher accuracy rate for males at 78.12% compared to females at 77.42%. However, accuracy of sex estimation for individuals under 18 was challenging. In comparison to logistic, KNN and SVM, RF exhibited higher accuracy rates. Moreover, incorporating age as a variable improved the accuracy of sex estimation, particularly in the 18-27 age group, where the accuracy rate increased to 88.46%. Meanwhile, all variables showed a linear correlation with age.
CONCLUSIONS: The linear measurements of the maxillary sinus could be a valuable tool for sex estimation in individuals aged 18 and over. A robust RF model has been developed for sex estimation within the Han population residing in the northwestern region of China. The accuracy of sex estimation could be higher when age is used as a predictive variable.