Abstract Objectives To clinically validate an AI-based tool for automated maxillary gingival segmentation of marginal and supracrestal gingiva on cone beam computed tomography (CBCT) scans using a novel soft tissue separation technique with a fluoride tray. Methods A validation set of 35 CBCT scans, covering maxilla, acquired with fluoride trays was processed using a cloud-based AI platform (Relu, Leuven, Belgium) for gingival segmentation. The resulting models were refined by an expert using 3D mesh-processing software and compared with the original AI outputs to assess accuracy. Additionally, six CBCT scans were manually segmented, using intraoral scans as reference, and compared with the AI model. Surface-based and voxel-wise analyses, colour-coded maps, consistency, and time-efficiency were evaluated. Wilcoxon signed-rank test was used to assess time differences among methods. Results AI vs expert refinement showed strong agreement with high overlap (medianDSC≥96%) and minor surface deviations (medianMSD∼0.00 mm). Minor differences were found between anterior and posterior regions (medianΔDSC=1%, ΔMSD∼0.00 mm). AI vs manual segmentations showed median DSC of 82% and small median MSD of 0.15 mm. Labial/buccal area showed the highest surface deviations from colour-coded maps. Bland-Altman plots showed low intra- and inter-operator consistency in time, while AI showed excellent consistency. AI demonstrated significantly faster segmentation, achieving 4x faster with expert refinement and 20x faster with manual approach. Conclusion The use of fluoride tray facilitates separation of oral soft tissues on CBCT scans, enabling accurate and time-efficient AI-driven segmentation of maxillary marginal and supracrestal gingiva. This supports integration into digital workflows and more efficient treatment planning. Advances in knowledge The use of a fluoride tray for soft tissue separation during CBCT scans facilitates gingival visualisation while maintaining patient comfort. The resulting 3D gingival models from AI-based segmentation can be integrated with automatically segmented dentomaxillofacial structures, enhancing clinical visualisation of oral soft and hard tissues and facilitating diagnosis and treatment planning, including periodontal evaluation, implant planning, prosthodontics, and orthodontics.
Papasratorn et al. (2026) studied this question.