School of Public Health, Southern Medical University, Guangzhou, 510515, China.
School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, China.
Sci Rep. 2022 Sep 19;12(1):15649. doi: 10.1038/s41598-022-20034-9.
Age estimation based on the mineralized morphology of teeth is one of the important elements of forensic anthropology. To explore the most suitable age estimation protocol for adolescents in the South China population, 1477 panoramic radiograph images of people aged 2-18 years in the South were collected and staged by the Demirjian mineralization staging method. The dental ages were estimated using the parameters of the Demirjian and Willems. Mathematical optimization and machine learning optimization were also performed in the data processing process in an attempt to obtain a more accurate model. The results show that the Willems method was more accurate in the dental age estimation of the southern China population and the model can be further optimized by reassigning the model through a nonintercept regression method. The machine learning model presented excellent results in terms of the efficacy comparison results with the traditional mathematical model, and the machine learning model under the boosting framework, such as gradient boosting decision tree (GBDT), significantly reduced the error in dental age estimation compared to the traditional mathematical method. This machine learning processing method based on traditional estimation data can effectively reduce the error of dental age estimation while saving arithmetic power. This study demonstrates the effectiveness of the GBDT algorithm in optimizing forensic age estimation models and provides a reference for other regions to use this parameter for age estimation model establishment, and the lightweight nature of machine learning offers the possibility of widespread forensic anthropological age estimation.
基于牙齿矿化形态的年龄估计是法医人类学的重要内容之一。为了探索最适合华南人群青少年的年龄估计方案，本研究收集了来自华南地区 2-18 岁人群的 1477 张全景放射图像，并采用 Demirjian 矿化分期法对其进行分期。使用 Demirjian 和 Willems 参数估计牙齿年龄。在数据处理过程中还进行了数学优化和机器学习优化，以试图获得更准确的模型。结果表明，Willems 方法在华南人群的牙齿年龄估计中更准确，并且可以通过非截距回归方法重新分配模型来进一步优化模型。与传统数学模型相比，机器学习模型在疗效比较结果方面表现出色，并且在提升框架下的机器学习模型（如梯度提升决策树（GBDT））显著降低了与传统数学方法相比的牙齿年龄估计误差。这种基于传统估计数据的机器学习处理方法可以在节省算法复杂度的同时，有效降低牙齿年龄估计的误差。本研究证明了 GBDT 算法在优化法医年龄估计模型方面的有效性，并为其他地区使用该参数建立年龄估计模型提供了参考，同时机器学习的轻量级特性为广泛的法医人类学年龄估计提供了可能性。
