Department of Prosthodontics, Yonsei University College of Dentistry, 50-1, Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
Department of Prosthodontics & Dental Research Institute, Seoul National University, 101 Daehak-ro, Jongro-gu, Seoul, 03080, Republic of Korea.
Clin Oral Investig. 2022 Sep;26(9):5763-5771. doi: 10.1007/s00784-022-04533-7. Epub 2022 May 10.
To evaluate whether the accuracy and duration of registration for cone beam computed tomography (CBCT) and intraoral scans differ according to the method of registration and ratio of dental restorations to natural teeth.
CBCT data and intraoral scans of eligible patients were grouped as follows according to the ratio of the number of dental restorations to the number of natural teeth (N): group 1, N = 0%; group 2, 0% < N < 50%; group 3, 50% ≤ N < 100%; and group 4, 100% ≤ N. Marker-free registration was performed with a deep learning-based platform and four implant planning software with different registration methods (two point-based, one surface-based, and one manual registration software) by a single operator, and the time consumption was recorded. Registration accuracy was evaluated by measuring the distances between the three-dimensional models of CBCT data and intraoral scans.
A total of 36 patients, one jaw per patient, were enrolled. Although registration accuracy was similar, the time consumed for registration significantly differed for the different methods. The deep learning-based registration method consumed the least time. Greater proportions of dental restorations significantly reduced the registration accuracy for semi-automatic and deep learning-based methods and reduced the time consumed for semi-automatic registration.
No superiority in registration accuracy was found. The proportion of dental restorations significantly affects the accuracy and duration of registration for CBCT data and intraoral scans.
ClinicalTrials.gov Identifier: KCT0006710 CLINICAL RELEVANCE: Registration accuracy for virtual implant planning decreases when the proportion of dental restorations increases regardless of registration methods.
评估锥形束 CT(CBCT)和口内扫描的配准准确性和时长是否因配准方法和牙修复体与天然牙的比例而异。
根据牙修复体与天然牙的数量比(N),将符合条件的患者的 CBCT 数据和口内扫描分为以下几组:组 1,N=0%;组 2,0%<N<50%;组 3,50%≤N<100%;组 4,N≥100%。由一名操作员使用基于深度学习的平台和四种具有不同配准方法的种植体规划软件(两种基于点的、一种基于面的和一种手动注册软件)进行无标记注册,并记录时间消耗。通过测量 CBCT 数据和口内扫描的三维模型之间的距离来评估配准精度。
共纳入 36 名患者,每位患者一个颌骨。尽管配准精度相似,但不同方法的配准时间消耗差异显著。基于深度学习的注册方法消耗的时间最少。牙修复体的比例越大,半自动和基于深度学习的方法的配准精度显著降低,半自动注册的时间消耗也降低。
未发现配准准确性的优势。牙修复体的比例显著影响 CBCT 数据和口内扫描的配准准确性和时长。
ClinicalTrials.gov 标识符:KCT0006710
无论采用何种配准方法,随着牙修复体比例的增加,虚拟种植体规划的配准准确性都会降低。