基于机器学习的CT鼻腔和上颌窦测量指标用于法医学性别预测

文献信息

中文摘要

背景： 性别判定是法医鉴定的关键组成部分，尤其是在涉及破碎或腐烂的人类遗骸且传统骨骼标志无法获取的情况下。上颌窦和鼻腔结构是受保护的颅面组成部分，具有性别二态性，可能有助于性别估计。本研究评估了基于计算机断层扫描（CT）的这些结构的人体测量指标，利用机器学习（ML）在埃及人群中进行性别判定。
方法： 对195名埃及成年人（100名女性，95名男性）进行了比较横断面研究，分别来自上埃及（艾斯尤特，n=104）和下埃及（本哈，n=91）。分析鼻旁窦的CT扫描，获得六个上颌窦尺寸和三个鼻腔测量值。推导出年龄和十个额外工程特征，共产生20个特征用于ML分析。评估了两种ML框架，它们在特征选择和超参数优化的顺序上不同，使用六种分类器和五种特征选择方法。使用准确率、受试者工作特征曲线下面积（AUC）、精确率、召回率、F1分数和特异性评估性能。
结果： 在若干测量中观察到显著的性别相关差异，上埃及和下埃及之间存在区域差异。框架2通常优于框架1。表现最佳的模型AUC分别为0.771和0.768，最高准确率达到74.4%。鼻额角、鼻根点-鼻尖距离和平均上颌窦前后径是最一致的预测指标。
结论： 基于CT的鼻腔和上颌窦人体测量学在埃及人法医性别估计中显示出中等效用。ML通过捕捉复杂的形态测量关系改进了分类，鼻腔测量成为稳健的性别判别标志。

英文摘要

BACKGROUND: Sex determination is a key component of forensic identification, especially in cases involving fragmented or decomposed human remains where conventional skeletal markers are unavailable. The maxillary sinus and nasal structures are protected craniofacial components that exhibit sexual dimorphism and may aid sex estimation. This study evaluated computed tomography (CT)-based anthropometric measurements of these structures for sex determination in an Egyptian population using machine learning (ML).
METHODS: A comparative cross-sectional study was conducted on 195 adult Egyptians (100 females, 95 males) from Upper Egypt (Assiut, n = 104) and Lower Egypt (Benha, n = 91). CT scans of the paranasal sinuses were analyzed to obtain six maxillary sinus dimensions and three nasal measurements. Age and ten additional engineered features were derived, yielding 20 features for ML analysis. Two ML frameworks differing in the sequence of feature selection and hyperparameter optimization were evaluated using six classifiers and five feature-selection methods. Performance was assessed using accuracy, area under the receiver operating characteristic curve (AUC), precision, recall, F1-score, and specificity.
RESULTS: Significant sex-related differences were observed in several measurements, with regional variation between Upper and Lower Egypt. Framework 2 generally outperformed Framework 1. The best-performing models achieved AUCs of 0.771 and 0.768, while the highest accuracy reached 74.4%. Nasofrontal angle, nasion-tip distance and mean anteroposterior maxillary dimension were the most consistent predictors.
CONCLUSION: CT-based nasal and maxillary sinus anthropometry shows moderate utility for forensic sex estimation in Egyptians. ML improved classification by capturing complex morphometric relationships, with nasal measurements emerging as robust sex-discriminative markers.