Colorimetric analysis of intraoral scans: A novel approach for detecting gingival inflammation

Gingivitis, a widely prevalent oral health condition, affects up to 80% of the population. Traditional assessment methods for gingivitis rely heavily on subjective clinical evaluation. This study seeks to explore the efficacy of interpreting the color metrics from intraoral scans to objectively differentiate between healthy and inflamed gingiva.

This study used the percentage of bleeding on probing (BOP%) as the clinical reference standard. Intraoral scans, obtained before and after gingivitis treatment using a scanner, were analyzed through a custom MATLAB script to quantify HSV (hue, saturation, value) and CIELAB (Commission Internationale de l'Eclairage L*a*b*) color coordinates. The region of interest was a 2-mm-wide gingival strip along the buccal margin of the maxillary anterior teeth. Linear regression analysis was performed to evaluate the relationship between photometric outcomes and continuous, dichotomous, and categorical BOP data. Diagnostic accuracy was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), as well as sensitivity and specificity measures.

The analysis included clinical and digital color data from 110 scans, adhering to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines. The multilevel linear regression analysis underscored a significant correlation between the BOP% and digital color metrics, specifically the CIELAB a* (red-green chroma), CIELAB b* (yellow-blue chroma), and color saturation, with AUC performances of 70%, 79.5%, and 80.8%, respectively.

Digital color analysis of intraoral scans has demonstrated a range of performance from acceptable to excellent in distinguishing sites with BOP. This innovative approach presents a promising tool for dentists and researchers in the accurate diagnosis, screening, and management of gingivitis.

Our study focuses on finding a better way to detect gingivitis, a common gum disease affecting many people. Traditional methods rely on the dentist's visual inspection, which can be subjective. We explored the use of color measurements from digital intraoral scans to objectively identify healthy versus inflamed gums. We analyzed 110 scans from 55 participants, examining the color differences in the gums before and after treatment. By measuring specific color values, we achieved up to 80.8% accuracy in distinguishing between healthy and inflamed gums. This method could offer a more reliable tool for dentists and researchers to diagnose and manage gingivitis, leading to better oral health outcomes.