Validation of a novel AI‐based automated multimodal image registration of CBCT and intraoral scan aiding presurgical implant planning

• Published in: Clinical oral implants research 2024-11, Vol.35 (11), p.1506-1517
• Main Authors: Elgarba, Bahaaeldeen M., Fontenele, Rocharles Cavalcante, Ali, Saleem, Swaity, Abdullah, Meeus, Jan, Shujaat, Sohaib, Jacobs, Reinhilde
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Automation; Computed tomography; Cone-Beam Computed Tomography - methods; cone‐beam computed tomography; Cotton; deep learning; Humans; Image registration; imaging, three‐dimensional; multimodal imaging; Multimodal Imaging - methods; Patient Care Planning; Qualitative analysis; Quantitative analysis; Registration; Soft tissues; Workflow
• ISSN: 0905-7161; 1600-0501; 1600-0501
• DOI: 10.1111/clr.14338

Objectives The 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 Methods A 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. Results AR 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). Conclusions The 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.