Xinhua Dai, Anjie Liu, Junhong Liu, Mengjun Zhan, Yuanyuan Liu, Wenchi Ke, Lei Shi, Xinyu Huang, Hu Chen, Zhenhua Deng, Fei Fan
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引用次数: 0
摘要
成人年龄估计是法医学和体质人类学中最具挑战性的问题之一。在这项研究中,我们旨在开发和评估基于修改后的 Gustafson 牙齿年龄估计标准的机器学习(ML)方法。在这项回顾性研究中,共收集了 851 张 15 至 40 岁患者的正畸照片。根据修改后的 Gustafson 标准分析了四颗下颌前磨牙的继发性牙本质形成(SE)、牙周退缩(PE)和损耗(AT)。在年龄估计方面,共生成并比较了 10 个 ML 模型。偏最小二乘回归模型在男性中的表现优于其他模型,平均绝对误差(MAE)为 4.151 岁。支持向量回归模型(MAE = 3.806 岁)在女性中表现良好。ML 模型的准确性优于以往研究中提供的单齿模型(男性 MAE = 4.747 年,女性 MAE = 4.957 年)。利用 Shapley 加法解释法揭示了 12 个特征在 ML 模型中的重要性,发现 AT 和 PE 对年龄估计的影响最大。研究结果表明,改进的 Gustafson 方法可以有效地用于中国西南地区人群的成人年龄估计。此外,本研究还强调了机器学习模型在协助专家实现准确、可解释的年龄估计方面的潜力。
Machine Learning Supported the Modified Gustafson’s Criteria for Dental Age Estimation in Southwest China
Adult age estimation is one of the most challenging problems in forensic science and physical anthropology. In this study, we aimed to develop and evaluate machine learning (ML) methods based on the modified Gustafson’s criteria for dental age estimation. In this retrospective study, a total of 851 orthopantomograms were collected from patients aged 15 to 40 years old. The secondary dentin formation (SE), periodontal recession (PE), and attrition (AT) of four mandibular premolars were analyzed according to the modified Gustafson’s criteria. Ten ML models were generated and compared for age estimation. The partial least squares regressor outperformed other models in males with a mean absolute error (MAE) of 4.151 years. The support vector regressor (MAE = 3.806 years) showed good performance in females. The accuracy of ML models is better than the single-tooth model provided in the previous studies (MAE = 4.747 years in males and MAE = 4.957 years in females). The Shapley additive explanations method was used to reveal the importance of the 12 features in ML models and found that AT and PE are the most influential in age estimation. The findings suggest that the modified Gustafson method can be effectively employed for adult age estimation in the southwest Chinese population. Furthermore, this study highlights the potential of machine learning models to assist experts in achieving accurate and interpretable age estimation.
期刊介绍:
The Journal of Digital Imaging (JDI) is the official peer-reviewed journal of the Society for Imaging Informatics in Medicine (SIIM). JDI’s goal is to enhance the exchange of knowledge encompassed by the general topic of Imaging Informatics in Medicine such as research and practice in clinical, engineering, and information technologies and techniques in all medical imaging environments. JDI topics are of interest to researchers, developers, educators, physicians, and imaging informatics professionals.
Suggested Topics
PACS and component systems; imaging informatics for the enterprise; image-enabled electronic medical records; RIS and HIS; digital image acquisition; image processing; image data compression; 3D, visualization, and multimedia; speech recognition; computer-aided diagnosis; facilities design; imaging vocabularies and ontologies; Transforming the Radiological Interpretation Process (TRIP™); DICOM and other standards; workflow and process modeling and simulation; quality assurance; archive integrity and security; teleradiology; digital mammography; and radiological informatics education.