Quantitative Ultrasound for Periodontal Soft Tissue Characterization

Periodontal diseases are a spectrum of inflammatory diseases that affect 45.9% of adults aged ≥30 years in the United States Current standard of care in clinics for the assessment of oral soft tissue inflammation is bleeding on probing,which is invasive, subjective and semi-qualitative. Quantitative ultrasound (QUS) has shown promising results in the non-invasive quantitative characterization of various soft tissues; however, it has not been used in clinical periodontics.

Here, we investigated the QUS analysis of two periodontal soft tissues (alveolar mucosa and gingiva) in vivo. The study cohort included 10 swine scanned at four oral quadrants, resulting in 40 scans. Two-parameter Burr and Nakagami models were employed for QUS-based speckle modeling. Parametric imaging of these parameters was also created using an optimal window size estimated in a separate phantom study.

Phantom results suggested a window size of 10 wavelengths as the reasonable estimation kernel. The Burr power-law parameter and Nakagami shape factor were higher in gingiva than alveolar mucosa, while Burr and Nakagami scale factors were both lower in the gingiva. The difference between the two tissue types was statistically significant (p < 0.0001). Linear classifications of these two tissue types using a 2-D parameter space of the Burr and Nakagami models resulted in a segmentation accuracy of 93.51% and 90.91%, respectively. Findings from histology-stained images showed that gingiva and alveolar mucosa had distinct underlying structures, with the gingiva showing a denser stain.

QUS results suggest that gingiva and alveolar mucosa can be differentiated using Burr and Nakagami parameters. We propose that QUS holds promising potential for the characterization of periodontal soft tissues and could become an objective and quantitative diagnostic tool for periodontology and implant dentistry to improve dental health care.