ObjectivesThe objective of this study is to assess accuracy, time‐efficiency and consistency of a novel artificial intelligence (AI)‐driven automated tool for cone‐beam computed tomography (CBCT) and intraoral scan (IOS) registration compared with manual and semi‐automated approaches.Materials and MethodsA dataset of 31 intraoral scans (IOSs) and CBCT scans was used to validate automated IOS‐CBCT registration (AR) when compared with manual (MR) and semi‐automated registration (SR). CBCT scans were conducted by placing cotton rolls between the cheeks and teeth to facilitate gingival delineation. The time taken to perform multimodal registration was recorded in seconds. A qualitative analysis was carried out to assess the correspondence between hard and soft tissue anatomy on IOS and CBCT. In addition, a quantitative analysis was conducted by measuring median surface deviation (MSD) and root mean square (RMS) differences between registered IOSs.ResultsAR was the most time‐efficient, taking 51.4 ± 17.2 s, compared with MR (840 ± 168.9 s) and SR approaches (274.7 ± 100.7 s). Both AR and SR resulted in significantly higher qualitative scores, favoring perfect IOS‐CBCT registration, compared with MR (p = .001). Additionally, AR demonstrated significantly superior quantitative performance compared with SR, as indicated by low MSD (0.04 ± 0.07 mm) and RMS (0.19 ± 0.31 mm). In contrast, MR exhibited a significantly higher discrepancy compared with both AR (MSD = 0.13 ± 0.20 mm; RMS = 0.32 ± 0.14 mm) and SR (MSD = 0.11 ± 0.15 mm; RMS = 0.40 ± 0.30 mm).ConclusionsThe novel AI‐driven method provided an accurate, time‐efficient, and consistent multimodal IOS‐CBCT registration, encompassing both soft and hard tissues. This approach stands as a valuable alternative to manual and semi‐automated registration approaches in the presurgical implant planning workflow.

中文翻译：

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验证基于 AI 的新型 CBCT 自动多模态图像配准和口内扫描，辅助术前种植体规划


目的本研究的目的是评估一种用于锥形束计算机断层扫描 （CBCT） 和口内扫描 （IOS） 配准的新型人工智能 （AI） 驱动的自动化工具与手动和半自动方法相比的准确性、时间效率和一致性。材料和方法与手动 （MR） 和半自动注册 （SR） 相比，使用 31 次口内扫描 （IOS） 和 CBCT 扫描的数据集来验证自动 IOS-CBCT 注册 （AR）。通过在脸颊和牙齿之间放置棉卷来进行 CBCT 扫描，以促进牙龈划定。执行多模式注册所花费的时间以秒为单位记录。进行定性分析以评估 IOS 和 CBCT 上硬组织和软组织解剖结构之间的对应关系。此外，通过测量注册 IOS 之间的中位表面偏差 （MSD） 和均方根 （RMS） 差异进行定量分析.结果AR 最省时，需要 51.4 ± 17.2 s，与 MR （840 ± 168.9 s） 和 SR 方法 （274.7 ± 100.7 s）。与 MR 相比，AR 和 SR 都导致显着更高的定性分数，有利于完美的 IOS-CBCT 配准 （p = .001）。此外，与 SR 相比，AR 表现出显著优越的定量性能，如低 MSD （0.04 ± 0.07 mm） 和 RMS （0.19 ± 0.31 mm）所示。相比之下，MR 与 AR 相比表现出显着更高的差异 （MSD = 0.13 ± 0.20 mm;RMS = 0.32 ± 0.14 mm）和 SR （MSD = 0.11 ± 0.15 mm;RMS = 0.40 ± 0.30 毫米）。结论新颖的 AI 驱动方法提供了一种准确、省时且一致的多模态 IOS-CBCT 配准，包括软组织和硬组织。 在术前种植体规划工作流程中，这种方法是手动和半自动套准方法的有价值的替代方案。