IADR Abstract Archives
Gingivitis Detection by Color and Shape Analysis From Intraoral Scans
Objectives: To validate a new technique for digital evaluation of gingival inflammation using intraoral scan (IOS) files.
Methods: 57 participants diagnosed with moderate gingivitis were randomized to follow Mechanical plaque control/chemical plaque control using Multi-Component Oral Hygiene Regimens Dual Zinc plus arginine and CPC mouthwash for 9 months. Full mouth Gingival index scores obtained chairside by two calibrated examiners and a (3Shape-TRIOS3) scanner used to capture IOS at the same clinical visit by a third investigator. Data acquisition was done Chairside measuring Gingival scores of 6 upper anterior teeth at 3 sites per tooth (Mesial, central, distal) yielded 18 sites per participant. Quality inspection software (Geomagic Control X-3D systems) was used to measure the digital variables of the same sites, and a software algorism was used to automatically trace the gingival margin. Region of interest (ROI) for gingival margin color (GMC) was determined by a 1mm offset from tooth-gingival contact (TGC) on gingival direction, ROI for TGC Curvature was determined at 0.5mm offset on both directions at the level of gingival margin.
Results: After statistical analysis, clinical and digital scores of participants were compared. The results showed a TGC mean curvature (-0.81±0.02) in healthy areas and (-0.45±0.05) in areas of gingivitis. The gingival margins mean red color hue was (2°±3) in healthy areas and(351°±5) in areas of gingivitis. The reproducibility of the color and Curvature detection was measured by scan-rescan of the same patient at the same time point of two consequent patients showed high level of reliability. There was a positive correlation between clinical gingival index to either TGC or GMC.
Conclusions: Digital analysis of GMC and TGC presents a precise and highly reproducible technique for the detection of gingival inflammation. This method can be a valuable tool for dentists and researchers in the diagnosis and/or monitoring of gingivitis.
Methods: 57 participants diagnosed with moderate gingivitis were randomized to follow Mechanical plaque control/chemical plaque control using Multi-Component Oral Hygiene Regimens Dual Zinc plus arginine and CPC mouthwash for 9 months. Full mouth Gingival index scores obtained chairside by two calibrated examiners and a (3Shape-TRIOS3) scanner used to capture IOS at the same clinical visit by a third investigator. Data acquisition was done Chairside measuring Gingival scores of 6 upper anterior teeth at 3 sites per tooth (Mesial, central, distal) yielded 18 sites per participant. Quality inspection software (Geomagic Control X-3D systems) was used to measure the digital variables of the same sites, and a software algorism was used to automatically trace the gingival margin. Region of interest (ROI) for gingival margin color (GMC) was determined by a 1mm offset from tooth-gingival contact (TGC) on gingival direction, ROI for TGC Curvature was determined at 0.5mm offset on both directions at the level of gingival margin.
Results: After statistical analysis, clinical and digital scores of participants were compared. The results showed a TGC mean curvature (-0.81±0.02) in healthy areas and (-0.45±0.05) in areas of gingivitis. The gingival margins mean red color hue was (2°±3) in healthy areas and(351°±5) in areas of gingivitis. The reproducibility of the color and Curvature detection was measured by scan-rescan of the same patient at the same time point of two consequent patients showed high level of reliability. There was a positive correlation between clinical gingival index to either TGC or GMC.
Conclusions: Digital analysis of GMC and TGC presents a precise and highly reproducible technique for the detection of gingival inflammation. This method can be a valuable tool for dentists and researchers in the diagnosis and/or monitoring of gingivitis.