School of Dentistry, Tuiuti University of Paraná, Curitiba, Paraná, Brazil.
Graduate Program in Human Communication Health, Tuiuti University of Paraná, Curitiba, Paraná, Brazil.
Head Face Med. 2024 Aug 30;20(1):44. doi: 10.1186/s13005-024-00446-w.
Cranial, facial, nasal, and maxillary widths have been shown to be significantly affected by the individual's sex. The present study aims to use measurements of dental arch and maxillary skeletal base to determine sex, employing supervised machine learning.
Maxillary and mandibular tomographic examinations from 100 patients were analyzed to investigate the inter-premolar width, inter-molar width, maxillary width, inter-pterygoid width, nasal cavity width, nostril width, and maxillary length, obtained through Cone Beam Computed Tomography scans. The following machine learning algorithms were used to build the predictive models: Logistic Regression, Gradient Boosting Classifier, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron Classifier (MLP), Decision Tree, and Random Forest Classifier. A 10-fold cross-validation approach was adopted to validate each model. Metrics such as area under the curve (AUC), accuracy, recall, precision, and F1 Score were calculated for each model, and Receiver Operating Characteristic (ROC) curves were constructed.
Univariate analysis showed statistical significance (p < 0.10) for all skeletal and dental variables. Nostril width showed greater importance in two models, while Inter-molar width stood out among dental measurements. The models achieved accuracy values ranging from 0.75 to 0.85 on the test data. Logistic Regression, Random Forest, Decision Tree, and SVM models had the highest AUC values, with SVM showing the smallest disparity between cross-validation and test data for accuracy metrics.
Transverse dental arch and maxillary skeletal base measurements exhibited strong predictive capability, achieving high accuracy with machine learning methods. Among the evaluated models, the SVM algorithm exhibited the best performance. This indicates potential usefulness in forensic sex determination.
个体的性别会显著影响颅面、鼻面和上颌宽度。本研究旨在使用牙弓和上颌骨基测量来确定性别，采用监督机器学习。
分析了 100 例患者的上颌和下颌断层检查，通过锥形束 CT 扫描获得了以下测量值：尖牙间宽度、磨牙间宽度、上颌宽度、翼突间宽度、鼻腔宽度、鼻孔宽度和上颌长度。使用以下机器学习算法构建预测模型：逻辑回归、梯度提升分类器、K-最近邻（KNN）、支持向量机（SVM）、多层感知机分类器（MLP）、决策树和随机森林分类器。采用 10 折交叉验证方法对每个模型进行验证。为每个模型计算了曲线下面积（AUC）、准确性、召回率、精度和 F1 分数等指标，并构建了接收者操作特征（ROC）曲线。
单变量分析显示所有骨骼和牙齿变量均具有统计学意义（p < 0.10）。鼻孔宽度在两个模型中具有重要性，而磨牙间宽度在牙齿测量中突出。模型在测试数据上的准确性值在 0.75 到 0.85 之间。逻辑回归、随机森林、决策树和 SVM 模型的 AUC 值最高，SVM 在准确性指标的交叉验证和测试数据之间的差异最小。
横向牙弓和上颌骨基测量具有很强的预测能力，通过机器学习方法可以实现高精度。在所评估的模型中，SVM 算法表现最好。这表明它在法医性别鉴定中具有潜在的用途。
