ObjectivesTo 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.Materials and MethodsTwenty 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).ResultsThe 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).ConclusionsAI‐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.

中文翻译：

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数字种植体规划的传统和人工智能辅助设置之间的比较：准确性、时间效率和用户体验


目的与临床医生的经验相关，探讨传统下颌管自动人工智能 （AI） 分割和 CBCT 与模型扫描数据配准的可靠性和时间效率。材料和方法要求 20 名临床医生，其中 10 名具有中度，10 名具有计算机辅助种植体规划经验，进行下颌管的双侧定位，然后使用 CBCT 进行口内模型扫描的配准。随后，对于每个数据集和每个参与者，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） 评估统计显著性。结果手动下颌管分割和模型配准的平均时间为 4.75 （2.03） min，AI 自动操作为 2.03 （0.36） min （p < 0.001）。手动分割下颌管的平均差异为 0.71 （1.80） mm RMS 误差，AI 辅助分割为 0.68 （0.36） RMS 误差 （p > 0.05）。对于 CBCT 和口内扫描之间的配准，手动的平均差异为 0.45 （0.16） mm，AI 辅助叠加的平均差异为 0.37 （0.07） mm （p > 0.05）。结论AI 自动化种植体规划工具是可行的选择，与传统的手动工作流程相比，它可以带来相似或更好的准确性，为有经验和经验不足的用户提供更高的时间效率。需要进一步的研究，包括各种软件和数据集，以便能够概括本研究的结果。