van Nistelrooij Niels, Maier Eva, Bronkhorst Hilde, Crins Luuk, Xi Tong, Loomans Bas A C, Vinayahalingam Shankeeth
Department of Oral and Maxillofacial Surgery, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands; Department of Oral and Maxillofacial Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and HumboldtUniversität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany.
Department of Operative Dentistry and Periodontology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Maximiliansplatz 2, 91054 Erlangen, Germany; Department of Dentistry, Research Institute for Medical Innovation, Radboud University Medical Center, Philips van Leydenlaan 25, 6525 EX Nijmegen, the Netherlands.
J Dent. 2024 Nov;150:105323. doi: 10.1016/j.jdent.2024.105323. Epub 2024 Aug 27.
This study aimed to develop and evaluate a fully automated method for visualizing and measuring tooth wear progression using pairs of intraoral scans (IOSs) in comparison with a manual protocol.
Eight patients with severe tooth wear progression were retrospectively included, with IOSs taken at baseline and 1-year, 3-year, and 5-year follow-ups. For alignment, the automated method segmented the arch into separate teeth in the IOSs. Tooth pair registration selected tooth surfaces that were likely unaffected by tooth wear and performed point set registration on the selected surfaces. Maximum tooth profile losses from baseline to each follow-up were determined based on signed distances using the manual 3D Wear Analysis (3DWA) protocol and the automated method. The automated method was evaluated against the 3DWA protocol by comparing tooth segmentations with the Dice-Sørensen coefficient (DSC) and intersection over union (IoU). The tooth profile loss measurements were compared with regression and Bland-Altman plots. Additionally, the relationship between the time interval and the measurement differences between the two methods was shown.
The automated method completed within two minutes. It was very effective for tooth instance segmentation (826 teeth, DSC = 0.947, IoU = 0.907), and a correlation of 0.932 was observed for agreement on tooth profile loss measurements (516 tooth pairs, mean difference = 0.021mm, 95% confidence interval = [-0.085, 0.138]). The variability in measurement differences increased for larger time intervals.
The proposed automated method for monitoring tooth wear progression was faster and not clinically significantly different in accuracy compared to a manual protocol for full-arch IOSs.
General practitioners and patients can benefit from the visualization of tooth wear, allowing quantifiable and standardized decisions concerning therapy requirements of worn teeth. The proposed method for tooth wear monitoring decreased the time required to less than two minutes compared with the manual approach, which took at least two hours.
本研究旨在开发并评估一种全自动方法，该方法利用成对的口内扫描（IOS）来可视化和测量牙齿磨损进展，并与手动方案进行比较。
回顾性纳入8例牙齿磨损进展严重的患者，在基线、1年、3年和5年随访时进行IOS扫描。为了进行对齐，自动方法在IOS中将牙弓分割成单独的牙齿。牙齿对配准选择可能未受牙齿磨损影响的牙齿表面，并在所选表面上进行点集配准。使用手动三维磨损分析（3DWA）方案和自动方法，基于有符号距离确定从基线到每次随访的最大牙齿轮廓损失。通过将牙齿分割与骰子-索伦森系数（DSC）和交并比（IoU）进行比较，对自动方法与3DWA方案进行评估。将牙齿轮廓损失测量结果与回归分析和布兰德-奥特曼图进行比较。此外，还展示了时间间隔与两种方法测量差异之间的关系。
自动方法在两分钟内完成。它对牙齿实例分割非常有效（826颗牙齿，DSC = 0.947，IoU = 0.907），并且在牙齿轮廓损失测量一致性方面观察到0.932的相关性（516对牙齿，平均差异 = 0.021mm，95%置信区间 = [-0.085, 0.138]）。对于更大的时间间隔，测量差异的变异性增加。
所提出的用于监测牙齿磨损进展的自动方法更快，并且与全牙弓IOS的手动方案相比，在准确性方面临床差异不显著。
全科医生和患者可以从牙齿磨损的可视化中受益，从而能够就磨损牙齿的治疗需求做出可量化和标准化的决策。与至少需要两小时的手动方法相比，所提出的牙齿磨损监测方法将所需时间减少到不到两分钟。
