Digital photometric analysis as a non-invasive method to determine gingival phenotype: A comparative study

Identification of periodontal phenotype is critical in clinical practice. Thick and thin tissues respond differently to inflammation, and trauma. It significantly influences the outcomes of restorative treatment, regenerative therapy, and success of implants and periodontal surgery. Periodontal phenotype can be assessed via invasive or non-invasive methods. This study aimed to establish the reliability of non-invasive methods in determining gingival phenotypes in comparison with validated methods.

This preapproved cross-sectional observational study was conducted at Batterjee Medical College in Saudi Arabia. The participants were conveniently sampled based on the inclusion criteria. The clinical study utilized Colorvue® biotype probes to evaluate gingival tissue phenotype in the region of interest, intraoral digital scanner (IOS) (iTero® scanner), and digital photography. Densitometric acquisition of photographs and intraoral scans was performed using Adobe Photoshop to quantify three-dimensional color measurements expressed in Delta E values (ΔE). Furthermore, patient-reported experience measures (PREMs) were used to evaluate anxiety and pain perception. Values of p < 0.05 were considered statistically significant.

The analysis of color difference values (ΔE) revealed significant variations in color perception across methods for the thin, medium and very thick groups, indicating perceptible color differences (p < 0.001). The assessment of anxiety levels indicated a statistically significant decrease in stress levels in favor of the IOS method for the medium phenotype. Furthermore, perceived pain was significantly lower with the IOS method than with the probing method for all phenotypes.

Densitometric analysis of standardized clinical photographs and intraoral scans of the marginal gingiva offers a promising, non-invasive, less stressful, and virtually non-painful method of periodontal phenotype evaluation with reliable numerical outputs. Furthermore, these data may be used to feed AI systems, where machines can learn to recognize color differences and possibly deduce phenotype assessments.