OBJECTIVES To investigate the accuracy of conventional and automatic artificial intelligence (AI)-based registration of cone-beam computed tomography (CBCT) with intraoral scans and to evaluate the impact of user's experience, restoration artifact, number of missing teeth, and free-ended edentulous area. MATERIALS AND METHODS Three initial registrations were performed for each of the 150 randomly selected patients, in an implant planning software: one from an experienced user, one from an inexperienced operator, and one from a randomly selected post-graduate student of implant dentistry. Six more registrations were performed for each dataset by the experienced clinician: implementing a manual or an automatic refinement, selecting 3 small or 3 large in-diameter surface areas and using multiple small or multiple large in-diameter surface areas. Finally, an automatic AI-driven registration was performed, using the AI tools that were integrated into the utilized implant planning software. The accuracy between each type of registration was measured using linear measurements between anatomical landmarks in metrology software. RESULTS Fully automatic-based AI registration was not significantly different from the conventional methods tested for patients without restorations. In the presence of multiple restoration artifacts, user's experience was important for an accurate registration. Registrations' accuracy was affected by the number of free-ended edentulous areas, but not by the absolute number of missing teeth (p < .0083). CONCLUSIONS In the absence of imaging artifacts, automated AI-based registration of CBCT data and model scan data can be as accurate as conventional superimposition methods. The number and size of selected superimposition areas should be individually chosen depending on each clinical situation.

中文翻译：

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利用种植规划软件将锥形束计算机断层扫描与数字扫描数据进行手动和人工智能叠加的准确性：一项随机临床研究。


目的 探讨基于传统和自动人工智能 （AI） 的锥形束计算机断层扫描 （CBCT） 配准口内扫描的准确性，并评估用户体验、修复伪影、缺失牙齿数量和自由端无牙颌区域的影响。材料和方法 在种植牙规划软件中对 150 名随机选择的患者中的每一位进行 3 次初始注册：1 例来自有经验的用户，1 例来自没有经验的操作员，1 例来自随机选择的种植牙研究生。经验丰富的临床医生对每个数据集进行了六次再配准：实施手动或自动精炼，选择 3 个小或 3 个大的直径表面积，并使用多个小或多个大的直径表面积。最后，使用集成到所用种植体规划软件中的 AI 工具进行自动 AI 驱动的配准。使用计量软件中解剖标志之间的线性测量来测量每种类型配准之间的准确性。结果 基于全自动的 AI 配准与对无修复体患者测试的常规方法没有显着差异。在存在多个修复工件的情况下，用户体验对于准确注册非常重要。套准的准确性受自由末端无牙颌区域数量的影响，但不受缺失牙齿的绝对数量的影响 （p < .0083）。结论 在没有成像伪影的情况下，基于 AI 的 CBCT 数据和模型扫描数据的自动配准可以与传统叠加方法一样准确。 应根据每种临床情况单独选择所选叠加区域的数量和大小。