Methods to assess tooth gingival thickness and diagnose gingival phenotypes: A systematic review

Abstract

Objective Measurement of the periodontal soft tissue dimension is crucial for clinical decision‐making and aesthetic prognosis. However, the effectiveness of different measuring methods remains unclear. This systematic review aimed to explore the diagnostic accuracy of two non‐invasive methods (namely CBCT and ultrasound) for gingival thickness measurement at different tooth positions. Materials and methods A systematic search was performed using PubMed (including Medline), PubMed Central, OVID, Cochrane Library, LILACS and OpenGrey. Studies focusing on comparisons between CBCT, ultrasound and direct transgingival probing were included. The means, SDs and correlation coefficients with 95% confidence intervals were extracted and analyzed using Review Manager and R software. Results Twelve studies were selected. No significant difference was found between CBCT measurement and transgingival probing in the anterior and posterior dentition, and a moderate correlation was observed between these two methods (r = 0.41). A weak correlation was found between ultrasound measurement and transgingival probing (r = 0.32), and a slight but statistically significant difference was found when comparing ultrasonic devices and transgingival probing in the posterior area. Conclusion CBCT can be considered a relatively reliable method for gingival thickness measurement in both the anterior and posterior areas compared with direct probing. Ultrasonic devices provide limited accuracy in the posterior area but are relatively comparable with direct clinical assessments in the anterior area. Clinical significance Measurement location may affect the diagnostic accuracy and repeatability of gingival thickness measurements. Appropriate method selection in different clinical scenarios is crucial to aesthetic outcome prediction and decision‐making.

Correlation between periodontal phenotype and sinus membrane thickness: A systematic review

“…At the same time, ultrasonic devices provide limited specificity in determining gingival thickness in the posterior sector compared that to in the anterior sector. However, CBCT thickness measurement has proven to be the most reliable method for both anterior and posterior sectors, and future studies should employ this methodology (Wang et al, 2022). Regarding the second point, although the correlation analysis corresponds to an initial estimate, further population‐based studies are required that also control for other covariates such as age and sex, or other environmental variables such as smoking, since the included studies performed a bivariate analysis.…”

The feasibility of ultrasonography for the measurement of periodontal and peri‐implant phenotype: A systematic review and meta‐analysis

“…11 Recent studies have successfully demonstrated the potential of US in implant dentistry. [12][13][14][15] The diagnostic examination of periodontal and peri-implant phenotypes requires a high level of accuracy. The brightness mode (B-mode) of US devices generates continuous two-dimensional (2D) grayscale images, where brightness is determined by the envelope of the returned echo signal, offering several advantages over conventional methods.…”

“…16,17 However, US has difficulty penetrating and accurately imaging dense, calcified structures, such as bone or teeth. To date, only four systematic reviews [12][13][14][15] have focused on the application and validation of US in dentistry, reporting limited findings regarding its accuracy compared to CBCT and probing.…”

“…However, US has difficulty penetrating and accurately imaging dense, calcified structures, such as bone or teeth. To date, only four systematic reviews 12–15 have focused on the application and validation of US in dentistry, reporting limited findings regarding its accuracy compared to CBCT and probing. It is crucial to maintain an up‐to‐date understanding of US imaging in implantology, especially focus on the feasibility of B‐mode screening.…”

Accurate gingival segmentation from 3D images with artificial intelligence: an animal pilot study

“…As a proof-of-concept study, the off-the-shelf deep-learning model is first recruited to segment teeth and bone from CBCT images of Yorkshire pig mandibles, which is then superimposed onto the intraoral scan of the pig semi-automatically. To evaluate the accuracy of this methodology, the gingival thickness obtained from the AI virtual measurements will be compared with those assessed by horizontal transmucosal bone sounding, the current clinical best practice [ 26 ]. Our hypothesis of the current study is that the trained AI models utilized in the current work are mature enough to be applied across subjects without retraining; in addition, a good agreement can be achieved between virtual and clinical measurements of soft tissue thickness.…”