Center for Nanotechnology & Biomaterials Application and Research (NBUAM), Marmara University, Istanbul, Turkey.
Department of Metallurgical and Materials Engineering, Faculty of Technology, Marmara University, Istanbul, Turkey.
Oral Radiol. 2024 Jul;40(3):415-423. doi: 10.1007/s11282-024-00751-9. Epub 2024 Apr 16.
This study aimed to evaluate the usability of morphometric features obtained from mandibular panoramic radiographs in gender determination using machine learning algorithms.
High-resolution radiographs of 200 patients aged 20-77 (41.0 ± 12.7) were included in the study. Twelve different morphometric measurements were extracted from each digital panoramic radiography included in the study. These measurements were used as features in the machine learning phase in which six different machine learning algorithms were used (k-nearest neighbor, decision trees, support vector machines, naive Bayes, linear discrimination analysis, and neural networks). To evaluate the reliability, we have performed tenfold cross-validation and we repeated this 10 times for every classification process. This process enhances the reliability of the results for other datasets.
When all 12 features are used together, the accuracy rate is found to be 82.6 ± 0.5%. The classification accuracies are also compared using each feature alone. Three features that give the highest accuracy are coronoid height (80.9 ± 0.9%), condyle height (78.2 ± 0.5%), and ramus height (77.2 ± 0.4%), respectively. When compared to the classification algorithms, the highest accuracy was obtained with the naive Bayes algorithm with a rate of 84.0 ± 0.4%.
Machine learning techniques can accurately determine gender by analyzing mandibular morphometric structures from digital panoramic radiographs. The most precise results are achieved by evaluating the structures in combination, using attributes obtained from applying the MRMR algorithm to all features.
本研究旨在评估使用机器学习算法从下颌全景片获得的形态特征在性别判定中的可用性。
本研究纳入了 200 名年龄在 20-77 岁（41.0±12.7）的患者的高分辨率射线照片。从研究中包含的每一张数字化全景射线照片中提取了 12 种不同的形态测量值。这些测量值被用作机器学习阶段的特征，其中使用了六种不同的机器学习算法（k-最近邻、决策树、支持向量机、朴素贝叶斯、线性判别分析和神经网络）。为了评估可靠性，我们进行了 10 折交叉验证，并对每个分类过程重复了 10 次。这个过程增强了结果对其他数据集的可靠性。
当同时使用所有 12 个特征时，准确率为 82.6±0.5%。还比较了单独使用每个特征的分类准确率。给出最高准确率的三个特征分别是冠状突高度（80.9±0.9%）、髁突高度（78.2±0.5%）和下颌支高度（77.2±0.4%）。与分类算法相比，朴素贝叶斯算法的准确率最高，为 84.0±0.4%。
通过分析数字化全景射线片中下颌的形态结构，机器学习技术可以准确地确定性别。通过评估组合结构，并使用从所有特征应用 MRMR 算法获得的属性，可获得最精确的结果。
