Elgarba Bahaaeldeen M, Ali Saleem, Fontenele Rocharles Cavalcante, Meeus Jan, Jacobs Reinhilde
Doctoral Researcher, OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven; Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium; and Department of Prosthodontics, Faculty of Dentistry, Tanta University, Tanta, Egypt.
Researcher, OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven; Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium; and King Hussein Medical Center, Jordanian Royal Medical Services, Amman, Jordan.
J Prosthet Dent. 2025 Feb 26. doi: 10.1016/j.prosdent.2025.02.004.
Accurately registering intraoral and cone beam computed tomography (CBCT) scans in patients with metal artifacts poses a significant challenge. Whether a cloud-based platform trained for artificial intelligence (AI)-driven segmentation can improve registration is unclear.
The purpose of this clinical study was to validate a cloud-based platform trained for the AI-driven segmentation of prosthetic crowns on CBCT scans and subsequent multimodal intraoral scan-to-CBCT registration in the presence of high metal artifact expression.
A dataset consisting of 30 time-matched maxillary and mandibular CBCT and intraoral scans, each containing at least 4 prosthetic crowns, was collected. CBCT acquisition involved placing cotton rolls between the cheeks and teeth to facilitate soft tissue delineation. Segmentation and registration were compared using either a semi-automated (SA) method or an AI-automated (AA). SA served as clinical reference, where prosthetic crowns and their radicular parts (natural roots or implants) were threshold-based segmented with point surface-based registration. The AA method included fully automated segmentation and registration based on AI algorithms. Quantitative assessment compared AA's median surface deviation (MSD) and root mean square (RMS) in crown segmentation and subsequent intraoral scan-to-CBCT registration with those of SA. Additionally, segmented crown STL files were voxel-wise analyzed for comparison between AA and SA. A qualitative assessment of AA-based crown segmentation evaluated the need for refinement, while the AA-based registration assessment scrutinized the alignment of the registered-intraoral scan with the CBCT teeth and soft tissue contours. Ultimately, the study compared the time efficiency and consistency of both methods. Quantitative outcomes were analyzed with the Kruskal-Wallis, Mann-Whitney, and Student t tests, and qualitative outcomes with the Wilcoxon test (all α=.05). Consistency was evaluated by using the intraclass correlation coefficient (ICC).
Quantitatively, AA methods excelled with a 0.91 Dice Similarity Coefficient for crown segmentation and an MSD of 0.03 ±0.05 mm for intraoral scan-to-CBCT registration. Additionally, AA achieved 91% clinically acceptable matches of teeth and gingiva on CBCT scans, surpassing SA method's 80%. Furthermore, AA was significantly faster than SA (P<.05), being 200 times faster in segmentation and 4.5 times faster in registration. Both AA and SA exhibited excellent consistency in segmentation and registration, with ICC values of 0.99 and 1 for AA and 0.99 and 0.96 for SA, respectively.
The novel cloud-based platform demonstrated accurate, consistent, and time-efficient prosthetic crown segmentation, as well as intraoral scan-to-CBCT registration in scenarios with high artifact expression.
在存在金属伪影的患者中，准确配准口腔内扫描和锥形束计算机断层扫描（CBCT）具有重大挑战。基于云的平台经过人工智能（AI）驱动的分割训练后能否改善配准尚不清楚。
本临床研究的目的是验证一个基于云的平台，该平台经过训练可用于在CBCT扫描上对修复冠进行AI驱动的分割，并在存在高金属伪影表现的情况下进行后续的多模式口腔内扫描与CBCT配准。
收集了一个数据集，其中包括30对时间匹配的上颌和下颌CBCT及口腔内扫描，每对扫描至少包含4个修复冠。CBCT采集时在脸颊和牙齿之间放置棉卷以促进软组织勾勒。使用半自动（SA）方法或AI自动（AA）方法比较分割和配准情况。SA作为临床参考，其中修复冠及其牙根部分（天然牙根或种植体）基于阈值进行分割，并采用基于点表面的配准。AA方法包括基于AI算法的全自动分割和配准。定量评估将AA在冠分割及后续口腔内扫描与CBCT配准中的中位数表面偏差（MSD）和均方根（RMS）与SA的进行比较。此外，对分割后的冠STL文件进行体素分析以比较AA和SA。对基于AA的冠分割进行定性评估，以评估细化的必要性，而基于AA的配准评估则仔细检查配准后的口腔内扫描与CBCT牙齿及软组织轮廓的对齐情况。最终，研究比较了两种方法的时间效率和一致性。定量结果采用Kruskal-Wallis、Mann-Whitney和Student t检验进行分析，定性结果采用Wilcoxon检验（所有α = 0.05）。使用组内相关系数（ICC）评估一致性。
在定量方面，AA方法表现出色，冠分割的骰子相似系数为0.91，口腔内扫描与CBCT配准的MSD为0.03±0.05 mm。此外，AA在CBCT扫描上实现了91%的牙齿和牙龈临床可接受匹配，超过了SA方法的80%。此外，AA比SA快得多（P < 0.05），分割速度快200倍，配准速度快4.5倍。AA和SA在分割和配准方面均表现出出色的一致性，AA的ICC值分别为0.99和1，SA的ICC值分别为0.99和0.96。
新型基于云的平台在高伪影表现的情况下，展示了准确、一致且高效的修复冠分割以及口腔内扫描与CBCT配准。
