Talebi Samaneh, Fallahzadeh Hossien, Jambarsang Sara, Ezoddini Ardakani Fatemeh
Center for Healthcare Data Modeling, Department of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Department of Oral and Maxillofacial Radiology, Dental Faculty, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Front Dent. 2025 Apr 12;22:14. doi: 10.18502/fid.v22i14.18470. eCollection 2025.
This study assessed sex estimation of Iranians according to maxillary left first molar measurements made on panoramic radiographs using classical and machine-learning classifiers. In this cross-sectional study, tooth length- and width-related variables were calculated for maxillary left first molars on 131 panoramic radiographs (65 males, 66 females; age range of 18-30 years). A subsample of the radiographs was selected and reevaluated by two examiners after 1 month. The intra-class correlation coefficient (ICC) was calculated to assess reliability. The regularized discriminant analysis (RDA), support vector machine (SVM), and cascade-forward and feed-forward neural network models were used for sex estimation. Comparisons were made with the Mann-Whitney and t tests. The intra-observer reliability was 0.9. SVM had the best performance on the test data in both classification schemes. The crown length at the cementoenamel junction (CEJL) and total crown length (CL) in the classification scheme I (sex estimation based on length and width variables), and CEJL/root length (RL), cementoenamel junction width (CEJW)/CEJL, and RL/total tooth length (TTL) in the classification scheme II (sex estimation based on the ratio of variables) were important variables for sex estimation determined by the SVM model. The CEJL had the highest discriminative potential with an area under the curve (AUC) of 78.8. The ratio of variables did not substantially improve sex estimation compared with single variables. CEJL is a reliable measure for sex estimation in Iranians with values higher than 6.25 indicating the male sex and other values indicating the female sex.
本研究根据在全景X光片上对上颌左侧第一磨牙进行的测量，使用经典分类器和机器学习分类器评估伊朗人的性别估计。在这项横断面研究中，计算了131张全景X光片（65名男性，66名女性；年龄范围为18至30岁）上的上颌左侧第一磨牙与牙长和牙宽相关的变量。选取了一部分X光片样本，1个月后由两名检查人员重新评估。计算组内相关系数（ICC）以评估可靠性。使用正则判别分析（RDA）、支持向量机（SVM）以及级联前馈和前馈神经网络模型进行性别估计。采用曼-惠特尼检验和t检验进行比较。观察者内可靠性为0.9。在两种分类方案中，SVM在测试数据上的表现最佳。在分类方案I（基于长度和宽度变量的性别估计）中，釉牙骨质界处的冠长（CEJL）和总冠长（CL），以及在分类方案II（基于变量比率的性别估计）中，CEJL/根长（RL）、釉牙骨质界宽度（CEJW）/CEJL和RL/总牙长（TTL）是SVM模型确定的性别估计的重要变量。CEJL具有最高的判别潜力，曲线下面积（AUC）为78.8。与单一变量相比，变量比率并未显著改善性别估计。CEJL是伊朗人性别估计的可靠指标，值高于6.25表明为男性，其他值表明为女性。
