Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, 710004, Shaanxi, People's Republic of China.
Department of Orthodontics, Stomatological Hospital of Xi'an Jiaotong University, 98 XiWu Road, Xi'an, 710004, Shaanxi, People's Republic of China.
Int J Legal Med. 2021 Jul;135(4):1589-1597. doi: 10.1007/s00414-021-02542-x. Epub 2021 Mar 4.
Age estimation is an important challenge in many fields, including immigrant identification, legal requirements, and clinical treatments. Deep learning techniques have been applied for age estimation recently but lacking performance comparison between manual and machine learning methods based on a large sample of dental orthopantomograms (OPGs). In total, we collected 10,257 orthopantomograms for the study. We derived logistic regression linear models for each legal age threshold (14, 16, and 18 years old) for manual method and developed the end-to-end convolutional neural network (CNN) which classified the dental age directly to compare with the manual method. Both methods are based on left mandibular eight permanent teeth or the third molar separately. Our results show that compared with the manual methods (92.5%, 91.3%, and 91.8% for age thresholds of 14, 16, and 18, respectively), the end-to-end CNN models perform better (95.9%, 95.4%, and 92.3% for age thresholds of 14, 16, and 18, respectively). This work proves that CNN models can surpass humans in age classification, and the features extracted by machines may be different from that defined by human.
年龄估计是许多领域的一个重要挑战，包括移民身份识别、法律要求和临床治疗。深度学习技术最近已被应用于年龄估计，但缺乏基于大量牙科全景片（OPG）的手动和机器学习方法之间的性能比较。我们总共收集了 10257 张全景片进行研究。我们为手动方法推导出了每个法定年龄阈值（14 岁、16 岁和 18 岁）的逻辑回归线性模型，并开发了端到端卷积神经网络（CNN），直接对牙齿年龄进行分类，与手动方法进行比较。两种方法均基于左侧下颌的 8 颗恒牙或第三磨牙。我们的结果表明，与手动方法（分别为 14、16 和 18 岁年龄阈值的 92.5%、91.3%和 91.8%）相比，端到端 CNN 模型的性能更好（分别为 14、16 和 18 岁年龄阈值的 95.9%、95.4%和 92.3%）。这项工作证明了 CNN 模型可以在年龄分类方面超越人类，机器提取的特征可能与人类定义的特征不同。
