Department of Oral Medical Imaging, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 3rd Section South Renmin Road 14#, Chengdu, 610041, China.
Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, China.
Clin Oral Investig. 2024 Mar 7;28(3):198. doi: 10.1007/s00784-024-05598-2.
This study aimed to use all permanent teeth as the target and establish an automated dental age estimation method across all developmental stages of permanent teeth, accomplishing all the essential steps of tooth determination, tooth development staging, and dental age assessment.
A three-step framework for automatically estimating dental age was developed for children aged 3 to 15. First, a YOLOv3 network was employed to complete the tasks of tooth localization and numbering on a digital orthopantomogram. Second, a novel network named SOS-Net was established for accurate tooth development staging based on a modified Demirjian method. Finally, the dental age assessment procedure was carried out through a single-group meta-analysis utilizing the statistical data derived from our reference dataset.
The performance tests showed that the one-stage YOLOv3 detection network attained an overall mean average precision 50 of 97.50 for tooth determination. The proposed SOS-Net method achieved an average tooth development staging accuracy of 82.97% for a full dentition. The dental age assessment validation test yielded an MAE of 0.72 years with a full dentition (excluding the third molars) as its input.
The proposed automated framework enhances the dental age estimation process in a fast and standard manner, enabling the reference of any accessible population.
The tooth development staging network can facilitate the precise identification of permanent teeth with abnormal growth, improving the effectiveness and comprehensiveness of dental diagnoses using pediatric orthopantomograms.
本研究旨在以所有恒牙为目标，建立一种适用于恒牙所有发育阶段的自动牙龄估测方法，完成牙齿确定、牙齿发育分期和牙龄评估的所有基本步骤。
为 3 至 15 岁的儿童开发了一个三步框架，用于自动估计牙龄。首先，使用 YOLOv3 网络完成数字化全景片上的牙齿定位和编号任务。其次，建立了一个名为 SOS-Net 的新网络，基于改良的 Demirjian 方法进行准确的牙齿发育分期。最后，通过利用我们参考数据集得出的统计数据进行单组荟萃分析来进行牙龄评估程序。
性能测试表明，一阶段 YOLOv3 检测网络在牙齿确定方面的总体平均精度 50 达到 97.50。所提出的 SOS-Net 方法在全牙列中实现了平均 82.97%的牙齿发育分期准确性。牙龄评估验证测试在输入全牙列（不包括第三磨牙）时产生了 0.72 年的 MAE。
所提出的自动框架以快速和标准的方式增强了牙龄估计过程，能够参考任何可获得的人群。
牙齿发育分期网络可以促进对异常生长的恒牙的精确识别，提高使用儿科全景片进行牙科诊断的有效性和全面性。
