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A novel fluoride tray-assisted workflow for artificial intelligence-driven automated maxillary gingival segmentation
Dhanaporn Papasratorn1,2,3, Rellyca Sola Gracea1,2,4, Rocharles Cavalcante Fontenele1,2,5
1OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven 3000, Belgium.
Objectives:
To clinically validate an artificial intelligence (AI)-based tool for automated maxillary gingival segmentation of marginal and supracrestal gingiva on cone beam CT (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 3-dimensional (3D) mesh-processing software and compared with the original AI outputs to assess accuracy. Additionally, 6 CBCT scans were manually segmented, using intraoral scans as reference, and compared with the AI model. Surface-based and voxel-wise analyses, color-coded maps, consistency, and time-efficiency were evaluated. Wilcoxon signed-rank test was used to assess time differences among methods.
Results:
Artificial intelligence 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). Artificial intelligence vs manual segmentations showed median dice similarity coefficient (DSC) of 82% and small median MSD of 0.15 mm. Labial/buccal area showed the highest surface deviations from color-coded maps. Bland-Altman plots showed low intra- and inter-operator consistency in time, while AI showed excellent consistency. Artificial intelligence 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 visualization while maintaining patient comfort. The resulting 3D gingival models from AI-based segmentation can be integrated with automatically segmented dentomaxillofacial structures, enhancing clinical visualization of oral soft and hard tissues and facilitating diagnosis and treatment planning, including periodontal evaluation, implant planning, prosthodontics, and orthodontics.
