Validation of a novel tool for automated tooth modelling by fusion of CBCT-derived roots with the respective IOS-derived crowns

OBJECTIVES: To validate a novel artificial intelligence (AI)-based tool for automated tooth modelling by fusing cone beam computed tomography (CBCT)-derived roots with corresponding intraoral scanner (IOS)-derived crowns. METHODS: A retrospective dataset of 30 patients, comprising 30 CBCT scans and 55 IOS dental arches, was used to evaluate the fusion model at full arch and single tooth levels. AI-fused models were compared with CBCT tooth segmentation using point-to-point surface distances-reported as median surface distance (MSD), root mean square distance (RMSD), and Hausdorff distance (HD)- alongside visual assessments. Qualitative assessment included visual inspection of CBCT multiplanar views. The automated fused model was also compared to expert-manual fusions for single tooth analysis in terms of accuracy, time efficiency, and consistency. RESULTS: AI-based fusion evaluation showed mean values of MSD, RMSD, and HD of 4 μm, 114 μm, and 940 μm for full arch; 5 μm, 104 μm, and 503 μm for single tooth analysis. Qualitative assessment showed discrepancies between fused tooth outline and CBCT tooth margin lower than 1 voxel for 59% of cases. AI-based fusion showed high similarity with expert-manual fusions with median MSD, RMSD, and HD values of 28 μm, 104 μm, and 576 μm, respectively. However, AI-based fusion was 32 times faster than manual fusion. Considering the time required for manual fusion, intra-observer agreement was high (ICC 0.93), while inter-observer agreement was moderate (ICC 0.48). CONCLUSION: The AI-based CBCT/IOS fusion demonstrated clinically acceptable accuracy, efficiency, and consistency, offering substantial time savings and robust performance across different patients and imaging devices. CLINICAL SIGNIFICANCE: Manual CBCT/IOS fusion performed by experts is effective but labor-intensive and time-consuming. AI algorithms show a remarkable ability to minimize human variability, resulting in more reliable and efficient fusion. This capability demonstrates the potential to provide a more personalized, precise and standardized approach for treatment planning and dental procedures.