Department of Orthodontics and Dentofacial Orthopedics, University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany.
Department of Statistics, Statistical Consultation Unit, StaBLab, LMU Munich, Akademiestr. 1, 80799, Munich, Germany.
BMC Oral Health. 2023 May 10;23(1):274. doi: 10.1186/s12903-023-02984-2.
One of the main uses of artificial intelligence in the field of orthodontics is automated cephalometric analysis. Aim of the present study was to evaluate whether developmental stages of a dentition, fixed orthodontic appliances or other dental appliances may affect detection of cephalometric landmarks.
For the purposes of this study a Convolutional Neural Network (CNN) for automated detection of cephalometric landmarks was developed. The model was trained on 430 cephalometric radiographs and its performance was then tested on 460 new radiographs. The accuracy of landmark detection in patients with permanent dentition was compared with that in patients with mixed dentition. Furthermore, the influence of fixed orthodontic appliances and orthodontic brackets and/or bands was investigated only in patients with permanent dentition. A t-test was performed to evaluate the mean radial errors (MREs) against the corresponding SDs for each landmark in the two categories, of which the significance was set at p < 0.05.
The study showed significant differences in the recognition accuracy of the Ap-Inferior point and the Is-Superior point between patients with permanent dentition and mixed dentition, and no significant differences in the recognition process between patients without fixed orthodontic appliances and patients with orthodontic brackets and/or bands and other fixed orthodontic appliances.
The results indicated that growth structures and developmental stages of a dentition had an impact on the performance of the customized CNN model by dental cephalometric landmarks. Fixed orthodontic appliances such as brackets, bands, and other fixed orthodontic appliances, had no significant effect on the performance of the CNN model.
人工智能在口腔正畸领域的主要应用之一是自动头影测量分析。本研究旨在评估牙列的发育阶段、固定正畸矫治器或其他牙科矫治器是否会影响头影测量标志点的检测。
本研究开发了一种用于自动检测头影测量标志点的卷积神经网络（CNN）。该模型在 430 张头颅侧位片上进行了训练，然后在 460 张新的头颅侧位片上进行了测试。比较了恒牙列患者和混合牙列患者的标志点检测准确性。此外，仅在恒牙列患者中研究了固定正畸矫治器和正畸托槽和/或带的影响。对两种类别中的每个标志点的平均径向误差（MRE）与相应的标准差（SD）进行 t 检验，以评估差异的显著性，显著性水平设为 p < 0.05。
研究表明，恒牙列患者和混合牙列患者的 Ap-下点和 Is-上点的识别准确率存在显著差异，无固定正畸矫治器的患者和有正畸托槽和/或带及其他固定正畸矫治器的患者的识别过程无显著差异。
结果表明，牙列的生长结构和发育阶段对头影测量标志点的定制 CNN 模型的性能有影响。固定正畸矫治器，如托槽、带及其他固定正畸矫治器，对 CNN 模型的性能没有显著影响。
