Robotics Laboratory, Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Korea.
Department of Orofacial Pain and Oral Medicine, Kyung Hee University Dental Hospital, #26 Kyunghee-daero, Dongdaemun-gu, Seoul, 02447, Korea.
Sci Rep. 2021 Jan 13;11(1):1073. doi: 10.1038/s41598-020-80182-8.
Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic radiography. The data set consisted of four first molar images from the right and left sides of the maxilla and mandible of each of 1586 individuals across all age groups, which were extracted from their panoramic radiographs. The accuracy of the tooth-wise estimation was 89.05 to 90.27%. Performance accuracy was evaluated mainly using a majority voting system and area under curve (AUC) scores. The AUC scores ranged from 0.94 to 0.98 for all age groups, which indicates outstanding capacity. The learned features of CNNs were visualized as a heatmap, and revealed that CNNs focus on differentiated anatomical parameters, including tooth pulp, alveolar bone level, or interdental space, depending on the age and location of the tooth. With this, we provided a deeper understanding of the most informative regions distinguished by age groups. The prediction accuracy and heat map analyses support that this AI-based age-group determination model is plausible and useful.
对活体个体的牙龄估计既困难又具有挑战性，而在恒牙期成年人中尚无共识方法。因此，我们旨在通过使用卷积神经网络（CNN）并结合通过全景放射摄影术提取的第一磨牙的牙 X 射线图像斑块，为年龄组估计提供一种准确且强大的基于人工智能（AI）的诊断系统。该数据集由来自上颌和下颌左右两侧的四个第一磨牙图像组成，每个图像来自 1586 名个体的所有年龄组，这些图像是从他们的全景片上提取的。牙齿估算的准确性为 89.05%至 90.27%。主要使用多数投票系统和曲线下面积（AUC）评分来评估性能准确性。所有年龄组的 AUC 评分范围为 0.94 至 0.98，这表明具有出色的能力。将 CNN 的学习特征可视化作为热图，并揭示了 CNN 专注于根据牙齿的年龄和位置区分的有区别的解剖参数，包括牙髓、牙槽骨水平或牙间隙。通过这种方式，我们更深入地了解了按年龄组区分的信息量最大的区域。预测准确性和热图分析支持该基于 AI 的年龄组确定模型是合理且有用的。
