Broll Alexander, Rosentritt Martin, Schlegl Thomas, Goldhacker Markus
Department of Prosthetic Dentistry, University Hospital Regensburg, Regensburg, Germany.
Faculty of Mechanical Engineering, Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany.
Front Artif Intell. 2024 Apr 16;7:1339193. doi: 10.3389/frai.2024.1339193. eCollection 2024.
Due to the high prevalence of dental caries, fixed dental restorations are regularly required to restore compromised teeth or replace missing teeth while retaining function and aesthetic appearance. The fabrication of dental restorations, however, remains challenging due to the complexity of the human masticatory system as well as the unique morphology of each individual dentition. Adaptation and reworking are frequently required during the insertion of fixed dental prostheses (FDPs), which increase cost and treatment time. This article proposes a data-driven approach for the partial reconstruction of occlusal surfaces based on a data set that comprises 92 3D mesh files of full dental crown restorations.
A Generative Adversarial Network (GAN) is considered for the given task in view of its ability to represent extensive data sets in an unsupervised manner with a wide variety of applications. Having demonstrated good capabilities in terms of image quality and training stability, StyleGAN-2 has been chosen as the main network for generating the occlusal surfaces. A 2D projection method is proposed in order to generate 2D representations of the provided 3D tooth data set for integration with the StyleGAN architecture. The reconstruction capabilities of the trained network are demonstrated by means of 4 common inlay types using a Bayesian Image Reconstruction method. This involves pre-processing the data in order to extract the necessary information of the tooth preparations required for the used method as well as the modification of the initial reconstruction loss.
The reconstruction process yields satisfactory visual and quantitative results for all preparations with a root mean square error (RMSE) ranging from 0.02 mm to 0.18 mm. When compared against a clinical procedure for CAD inlay fabrication, the group of dentists preferred the GAN-based restorations for 3 of the total 4 inlay geometries.
This article shows the effectiveness of the StyleGAN architecture with a downstream optimization process for the reconstruction of 4 different inlay geometries. The independence of the reconstruction process and the initial training of the GAN enables the application of the method for arbitrary inlay geometries without time-consuming retraining of the GAN.
由于龋齿患病率高，常常需要进行固定义齿修复，以恢复受损牙齿或替换缺失牙齿，同时保持功能和美观。然而，由于人类咀嚼系统的复杂性以及每个个体牙列的独特形态，义齿修复体的制作仍然具有挑战性。在固定义齿（FDP）植入过程中，经常需要进行调整和返工，这增加了成本和治疗时间。本文基于一个包含92个全冠修复体3D网格文件的数据集，提出了一种数据驱动的咬合面部分重建方法。
鉴于生成对抗网络（GAN）能够以无监督方式表示大量数据集并具有广泛应用，因此考虑将其用于给定任务。鉴于StyleGAN-2在图像质量和训练稳定性方面表现出良好的能力，已被选作生成咬合面的主要网络。提出了一种二维投影方法，以便生成所提供的3D牙齿数据集的二维表示，以便与StyleGAN架构集成。使用贝叶斯图像重建方法，通过4种常见的嵌体类型展示了训练网络的重建能力。这涉及对数据进行预处理，以提取所用方法所需的牙齿预备的必要信息，以及修改初始重建损失。
重建过程对所有预备体均产生了令人满意的视觉和定量结果，均方根误差（RMSE）范围为0.02毫米至0.18毫米。与CAD嵌体制备的临床程序相比，在总共4种嵌体几何形状中，牙医组对基于GAN的修复体在其中3种上更为青睐。
本文展示了StyleGAN架构结合下游优化过程对4种不同嵌体几何形状进行重建的有效性。重建过程的独立性以及GAN的初始训练使得该方法能够应用于任意嵌体几何形状，而无需对GAN进行耗时的重新训练。
