法医牙科学中的年龄估计主要基于恒牙的发育。为了记录被检查牙齿的发育状态,分期技术的发展。然而,由于校准不当,阶段分配过程中的不确定性,缺乏经验,专家观察员之间存在阶段分配的不均匀性。因此,相关年龄估计结果不一致。适用于所有牙齿类型的自动分级技术可以克服该缺点。这项研究旨在建立一种集成的自动化技术,以分期所有下颌牙齿类型的发展并比较其分期性能。校准的观察者根据十阶段改良的Demirjian分期技术对FDI牙齿31、33、34、37和38进行了分期。根据每个检查牙齿周围的标准化边界框,使用PhotoshopCC2021®软件(Adobe®,版本23.0)。选择1639张射线照片的黄金标准集(n31=259,n33=282,n34=308,n37=390,n38=400),并输入到为最佳分期精度而训练的卷积神经网络(CNN)中。网络的性能评估是在五重交叉验证方案中进行的。在每个折叠中,整个数据集以折叠之间的非重叠方式分为训练集和测试集(即,80%和20%的数据集,分别)。按牙齿类型和总体计算分期性能(精度,平均绝对差,线性加权科恩的Kappa和类内相关系数)。总的来说,这些指标分别等于0.53、0.71、0.71和0.89。所有分期绩效指数均为37最佳,31最差。错误分类阶段的最高数量与相邻阶段相关。在31的所有可用阶段中观察到大多数错误分类。我们的发现表明,下颌磨牙的发育状况可以在年龄估计的自动化方法中考虑,虽然考虑门牙可能会妨碍年龄估计。
Age estimation in forensic odontology is mainly based on the development of permanent teeth. To register the developmental status of an examined tooth, staging techniques were developed. However, due to inappropriate calibration, uncertainties during stage allocation, and lack of experience, non-uniformity in stage allocation exists between expert observers. As a consequence, related age estimation results are inconsistent. An automated staging technique applicable to all tooth types can overcome this drawback.This study aimed to establish an integrated automated technique to stage the development of all mandibular tooth types and to compare their staging performances.Calibrated observers staged FDI teeth 31, 33, 34, 37 and 38 according to a ten-stage modified Demirjian staging technique. According to a standardised bounding box around each examined tooth, the retrospectively collected panoramic radiographs were cropped using Photoshop CC 2021® software (Adobe®, version 23.0). A gold standard set of 1639 radiographs were selected (n31 = 259, n33 = 282, n34 = 308, n37 = 390, n38 = 400) and input into a convolutional neural network (CNN) trained for optimal staging accuracy. The performance evaluation of the network was conducted in a five-fold cross-validation scheme. In each fold, the entire dataset was split into a training and a test set in a non-overlapping fashion between the folds (i.e., 80% and 20% of the dataset, respectively). Staging performances were calculated per tooth type and overall (accuracy, mean absolute difference, linearly weighted Cohen\'s Kappa and intra-class correlation coefficient). Overall, these metrics equalled 0.53, 0.71, 0.71, and 0.89, respectively. All staging performance indices were best for 37 and worst for 31. The highest number of misclassified stages were associated to adjacent stages. Most misclassifications were observed in all available stages of 31.Our findings suggest that the developmental status of mandibular molars can be taken into account in an automated approach for age estimation, while taking incisors into account may hinder age estimation.