Ntovas Panagiotis, Marchand Laurent, Schnappauf Albrect, Finkelman Matthew, Revilla-Leon Marta, Att Wael
Department of Prosthodontics, Tufts University School of Dental Medicine, Boston, Massachusetts, USA.
Dental-Wings, Chemnitz, Germany.
Clin Oral Implants Res. 2025 Mar;36(3):290-297. doi: 10.1111/clr.14382. Epub 2024 Nov 21.
To investigate the reliability and time efficiency of the conventional compared to the automatic artificial intelligence (AI) segmentation of the mandibular canal and registration of the CBCT with the model scan data, in relation to clinician's experience.
Twenty clinicians, 10 with a moderate and 10 with a high experience in computer-assisted implant planning, were asked to perform a bilateral localization of the mandibular canal, followed by a registration of the intraoral model scan with the CBCT. Subsequently, for each data set and each participant, the same operations were performed utilizing the AI tool. Statistical significance was assessed via a mixed model (using the PROC MIXED statement and the compound symmetry covariance structure).
The mean time for the segmentation of the mandibular canals and the registration of the models was 4.75 (2.03)min for the manual and 2.03 (0.36) min for the AI-automated operations (p < 0.001). The mean discrepancy in the mandibular canals was 0.71 (1.80) mm RMS error for the manual segmentation and 0.68 (0.36) RMS error for the AI-assisted segmentation (p > 0.05). For the registration between the CBCT and the intraoral scans, the mean discrepancy was 0.45 (0.16) mm for the manual and 0.37 (0.07) mm for the AI-assisted superimposition (p > 0.05).
AI-automated implant planning tools are feasible options that can lead to a similar or better accuracy compared to the conventional manual workflow, providing improved time efficiency for both experienced and less experienced users. Further research including a variety of software and data sets is required to be able to generalize the outcomes of the present study.
研究与传统方法相比，人工智能（AI）自动分割下颌管以及将CBCT与模型扫描数据配准的可靠性和时间效率，并探讨其与临床医生经验的关系。
邀请20名临床医生，其中10名在计算机辅助种植计划方面经验中等，10名经验丰富，要求他们对下颌管进行双侧定位，然后将口内模型扫描与CBCT进行配准。随后，针对每个数据集和每位参与者，使用AI工具执行相同的操作。通过混合模型（使用PROC MIXED语句和复合对称协方差结构）评估统计学意义。
手动分割下颌管和配准模型的平均时间为4.75（2.03）分钟，而AI自动操作的平均时间为2.03（0.36）分钟（p < 0.001）。手动分割下颌管的平均差异为0.71（1.80）mm均方根误差，AI辅助分割的平均差异为0.68（0.36）均方根误差（p > 0.05）。对于CBCT与口内扫描之间的配准，手动操作的平均差异为0.45（0.16）mm，AI辅助叠加的平均差异为0.37（0.07）mm（p > 0.05）。
与传统的手动工作流程相比，AI自动种植计划工具是可行的选择，其准确性相似或更高，为经验丰富和经验不足的用户都提高了时间效率。需要进一步开展包括各种软件和数据集的研究，以便能够推广本研究的结果。
