Nguyen Huu Tue

The “periodontal phenotype” varies based on nationality, ethnicity, and oral location. The study aimed to evaluate the gingival thickness and keratinized tissue width of the anterior maxillary teeth in the Vietnamese population. This was a descriptive cross-sectional study (n = 65 participants, 390 teeth). To assess gingival thickness, we employed the probe transparency method, based on the visibility of differently colored probes through the gingival tissue; thickness was categorized as thin, average, thick, or very thick. The KTW was measured by the Rolling test method. Based on the mixed-effects model, the estimated mean KTW was 5.31 mm (95% confidence interval [CI]: 5.12–5.50) in participants aged ≤25 years and 5.14 mm (95% CI: 4.67–5.61) in those >25 years ( P = .38). The model-based mean KTW for males was 5.21 mm (95% CI: 5.00–5.42) and for females was 5.32 mm (95% CI: 5.10–5.54) ( P = .49). The overall mean KTW of anterior maxillary teeth was 5.27 ± 1.11 mm. Thin gingiva was the most prevalent phenotype, with a generalized estimating equations–based estimated prevalence of 71.3% (95% CI: 66.2%–76.0%). The most common gingival phenotype in Vietnamese individuals was “thin” Participants with thin gingiva tended to have a wider KTW, while age and sex had no significant effect.

Objective: This in vitro study aimed to evaluate and compare the trueness and precision of four extraoral 3D facial scanning systems using a standardized 3D-printed human head model. Methods: A 3D-printed head model with 16 anatomical landmarks and 17 inter-landmark linear distances was fabricated using a high-resolution 3D printer. Caliper measurements were used as reference standards. The model was scanned 15 times by four systems: a handheld scanner (MetiSmile, Shining 3D, Hangzhou, China), a desktop scanner (RAYFace v2.0, Ray Co., Seongnam, Gyeonggi-do, Republic of Korea), and two mobile applications (Heges and Polycam, iPhone 15, Apple Inc., Cupertino, CA, USA). All digital distances were measured in Blender software. To assess intra-observer reliability, all measurements were repeated twice by the same examiner with a 3-week interval between sessions, and intra-class correlation coefficients were calculated using a two-way mixed-effects, single-measurement, absolute-agreement model (ICC 3,1). Trueness, defined as the absolute deviation from the reference caliper values, was compared across scanners using the Kruskal–Wallis test due to its non-normal distribution. Precision, regional trueness and precision values across the four scanners defined as the standard deviation of repeated scans, was analyzed using One-way ANOVA with Tukey post-hoc comparisons for normally distributed datasets (α = 0.05). Distances were measured digitally in Blender software, and trueness (absolute deviation from reference) and precision (standard deviation of repeated scans) were analyzed using the Kruskal–Wallis test and One-way ANOVA with Tukey post hoc comparisons (α = 0.05). Results: The Polycam application demonstrated the highest trueness (0.49 ± 0.32 mm), followed by MetiSmile (0.51 ± 0.36 mm), RAYFace (0.58 ± 0.39 mm), and Heges (0.73 ± 0.42 mm). The MetiSmile scanner showed the highest precision (0.12 ± 0.07 mm), while RAYFace and Polycam exhibited moderate precision (0.28 ± 0.19 mm and 0.15 ± 0.06 mm, respectively). Vertical measurements tended to be more accurate than horizontal ones, and the lower facial region showed smaller deviations; however, these differences were not statistically significant (p > 0.05). Conclusions: MetiSmile achieved the highest precision and Polycam the highest trueness. Although all systems showed mean deviations < 1 mm, only three demonstrated <0.6 mm accuracy (except for Heges scanner). These results suggest that professional and mobile-based scanners can provide clinically acceptable facial data for educational and preliminary digital workflow applications, though further validation under clinical conditions is required. This study provides quantitative evidence on the accuracy and repeatability of commonly available extraoral 3D facial scanning systems under controlled laboratory conditions. The results indicate that both professional-grade and mobile-based scanners can reproduce facial morphology with clinically acceptable deviations, particularly in flat and stable regions such as the forehead and chin. Although only three systems achieved mean trueness below 0.6 mm, all demonstrated errors within 1 mm, sufficient for diagnostic visualization, digital smile design, and preliminary virtual patient modeling. These findings support the safe and cost-effective adoption of extraoral facial scanning in dental education and treatment planning, while emphasizing the need for further validation in real clinical environments where motion, lighting, and soft-tissue variability may affect accuracy.