Department of Orthodontics, Faculty of Dentistry, Alexandria University, Alexandria, Egypt.
Department of Orthodontics, Faculty of Dentistry, Alexandria University, Alexandria, Egypt.
Am J Orthod Dentofacial Orthop. 2024 Sep;166(3):282-291.e1. doi: 10.1016/j.ajodo.2024.05.015. Epub 2024 Jun 21.
The accuracy of tooth segmentation in intraoral scans is crucial for performing virtual setups and appliance fabrication. Hence, the objective of this study was to estimate and compare the accuracy of automated tooth segmentation generated by the artificial intelligence of dentOne software (DIORCO Co, Ltd, Yongin, South Korea) and Medit Ortho Simulation software (Medit Corp, Seoul, South Korea).
Twelve maxillary and mandibular pretreatment dental scan sets comprising 286 teeth were collected for this investigation from the archives of the Department of Orthodontics, Faculty of Dentistry, Alexandria University. The scans were imported as standard tessellation language files into both dentOne and Medit Ortho Simulation software. Automatic segmentation was run on each software. The number of successfully segmented teeth vs failed segmentations was recorded to determine the success rate of automated segmentation of each program. Evaluation of success and/or failure was based on the software's identification of the teeth and the quality of the segmentation. The mesiodistal tooth width measurements after segmentation using both tested software programs were compared with those measured on the unsegmented scan using Meshmixer software (Autodesk, San Rafael, Calif). The unsegmented scans served as the reference standard.
A total of 288 teeth were examined. Successful identification rates were 99% and 98.3% for Medit and dentOne, respectively. Success rates of segmenting the lingual surfaces of incisors were significantly higher in Medit than in dentOne (93.7% vs 66.7%, respectively; P <0.001). DentOne overestimated the mesiodistal width of canines (0.11 mm, P = 0.032), premolars (0.22 mm, P < 0.001), and molars (0.14 mm, P = 0.043) compared with the reference standard, whereas Medit overestimated the mesiodistal width of premolars only (0.13 mm, P = 0.006). Bland-Altman plots showed that mesiodistal tooth width agreement limits exceeded 0.2 mm between each software and the reference standard.
Both artificial intelligence-segmentation software demonstrated acceptable accuracy in tooth segmentation. There is a need for improvement in segmenting incisor lingual tooth surfaces in dentOne. Both software programs tended to overestimate the mesiodistal widths of segmented teeth, particularly the premolars. Artificial intelligence-segmentation needs to be manually adjusted by the operator to ensure accuracy. However, this still does not solve the problem of proximal surface reconstruction by the software.
口腔内扫描中牙齿分割的准确性对于进行虚拟设置和矫正器制作至关重要。因此，本研究的目的是评估和比较 dentOne 软件（DIORCO Co，Ltd，韩国 Yongin）和 Medit Ortho Simulation 软件（Medit Corp，韩国首尔）的人工智能生成的自动牙齿分割的准确性。
从亚历山大大学牙科学院正畸科的档案中收集了 12 个上颌和下颌预处理牙扫描集，共 286 颗牙齿。将扫描以标准曲面细分语言文件的形式导入 dentOne 和 Medit Ortho Simulation 软件。在每个软件上运行自动分割。记录成功分割的牙齿数量与失败分割的牙齿数量，以确定每个程序自动分割的成功率。成功和/或失败的评估基于软件识别牙齿和分割质量。使用这两个测试软件程序分割后的中近远牙宽测量值与使用 Meshmixer 软件（Autodesk，加利福尼亚州圣拉斐尔）在未分割扫描上的测量值进行比较。未分割的扫描作为参考标准。
共检查了 288 颗牙齿。Medit 和 dentOne 的成功识别率分别为 99%和 98.3%。Medit 中切牙舌侧表面分割的成功率明显高于 dentOne（分别为 93.7%和 66.7%；P<0.001）。与参考标准相比，dentOne 高估了尖牙（0.11 毫米，P=0.032）、前磨牙（0.22 毫米，P<0.001）和磨牙（0.14 毫米，P=0.043）的中近远牙宽，而 Medit 仅高估了前磨牙的中近远牙宽（0.13 毫米，P=0.006）。Bland-Altman 图显示，每个软件与参考标准之间的中近远牙宽一致性限制超过 0.2 毫米。
两种人工智能分割软件在牙齿分割方面都具有可接受的准确性。需要改进 dentOne 中切牙舌侧牙齿表面的分割。两种软件程序都倾向于高估分割牙齿的中近远牙宽，特别是前磨牙。需要由操作员手动调整人工智能分割以确保准确性。然而，这仍然不能解决软件对近中面重建的问题。
