Gingival Phenotype Assessment: A Comparative Study of A Novel CBCT-Based Measurement Technique and Transgingival Probing

Gingival Phenotype Assessment: A Comparative Study of A Novel CBCT-Based Measurement Technique and Transgingival Probing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gingival Phenotype Assessment: A Comparative Study of A Novel CBCT-Based Measurement Technique and Transgingival Probing Ning Zhang, Jijun Dong, Shuang Qu, Guangda Li, Lan Kluwe, Jingfu Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9010834/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background This study aimed to evaluate the accuracy and clinical utility of a novel, non-invasive cone-beam computed tomography (CBCT)-based protocol for measuring gingival thickness in the maxillary anterior region, using transgingival probing (TGP) as the reference standard. Methods In this cross-sectional study, 39 periodontally healthy patients (24 males, 15 females; mean age 36 ± 12 years) were recruited. Gingival thickness was measured at 2 mm, 4 mm, and 6 mm apical to the gingival zenith on the facial aspect of maxillary anterior teeth (234 teeth, 702 sites) using both TGP and a CBCT protocol involving radiopaque resin markers. Method agreement was analyzed via Bland-Altman plots and Deming regression. Diagnostic performance of CBCT for classifying gingival phenotype was assessed. Results CBCT measurements showed a strong correlation with TGP (ρ > 0.98, p < 0.001). Deming regression indicated a constant systemic error, with CBCT underestimating thickness by approximately 0.04 mm (95% CI: -0.053 to -0.035), but no proportional error. Despite this minor bias, agreement was excellent (Kappa > 0.95). CBCT demonstrated 100% specificity and > 96% sensitivity for identifying thick phenotypes. No significant differences in gingival thickness were found between genders. Conclusion The novel CBCT-based measurement protocol shows high agreement with gold standard. The identified constant error is clinically negligible, supporting its utility as a reliable, non-invasive method for assessing gingival thickness and phenotype, with potential for integration with digital impression technology. Gingival Thickness Gingival Phenotype Cone-Beam Computed Tomography Transgingival Probing Measurement Agreement Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The gingival phenotype is a crucial prognostic factor influencing the outcomes of various dental treatments[ 1 ]. In the aesthetically sensitive anterior region, it is a key clinical parameter of significant concern across multiple disciplines, including implantology, prosthodontics, orthodontics, periodontology, and operative dentistry. Gingival phenotypes are primarily categorized into the thin and thick types[ 2 ]. These different phenotypes exhibit varied responses to physiological, pathological, and therapeutic stimuli. Due to its poorer tissue stability, the thin phenotype is often associated with more pronounced gingival recession following insult compared to the thick phenotype, which generally yields more stable and predictable clinical outcomes[ 3 , 4 ]. Consequently, for an identical clinical procedure, the prognosis can differ substantially among patients with different gingival phenotypes. Therefore, the accurate determination of the gingival thickness holds significant clinical importance for both treatment planning and the evaluation of post-therapeutic results. As the classification of gingival phenotype is predominantly based on gingival thickness, its accurate clinical assessment is of paramount importance. Current primary methods for measuring gingival thickness include the transgingival probing method (TGP), the periodontal probe transparency method, and cone-beam computed tomography (CBCT). While the transgingival probing method can obtain relatively accurate measurements at different sites, its invasive nature often results in poor patient acceptance. The periodontal probe transparency method, although non-invasive and painless, yields unstable results with considerable measurement error. CBCT provides high imaging resolution and more accurate sagittal views compared to spiral CT, meeting the requirements for oral soft tissue imaging; however, its measurements are still subject to certain inherent errors[ 5 ]. Currently, there remains some debate regarding non-invasive methods for measuring gingival thickness and the determination of gingival phenotype. Therefore, this study designed a gingival thickness measurement protocol that integrates CBCT with digital impressions and evaluated the relationship between gingival phenotype and specific measurement points across different genders. Utilizing Bland-Altman agreement analysis, this study demonstrated a high level of agreement between the CBCT-based gingival thickness measurement method and the transgingival probing method. Subsequently, Deming regression analysis was employed to verify a constant systematic error between the two methods, confirming that the proposed method possesses high measurement accuracy. Thus, this work provides clinical and theoretical references for the assessment of gingival phenotype. 2. Materials and Methods 2.1. Study Design and Ethical Considerations This cross-sectional study was conducted in strict accordance with the ethical principles outlined in the Declaration of Helsinki and received approval from the Medical Ethics Committee of the General Hospital of the Northern Theater Command (Approval No.: Y 2024036). Prior to the commencement of the study, the research protocol was explained in detail to each participant or their legal guardian, and written informed consent was obtained. 2.2. Subjects Patients who received dental treatment at the Department of Stomatology, General Hospital of the Northern Theater Command between June 2024 and October 2025 were recruited. A total of 39 patients were included, comprising 24 males and 15 females, with a mean age of 36 ± 12 years. Inclusion criteria were as follows: (1) Periodontal health, defined as a Gingival Index of 0, absence of bleeding on probing, probing depth ≤ 3 mm, and no obvious gingival recession or clinical attachment loss; (2) No history of trauma, tumors, or congenital deformities in the maxilla; (3) No tooth crowding or missing teeth in the maxillary anterior region; (4) Age ≥ 18 years. Exclusion criteria were as follows: (1) Presence of coronal defects or missing teeth in the maxillary anterior region; (2) Presence of implants, crown restorations, or impacted teeth in the maxillary anterior teeth; (3) History of root canal treatment or orthodontic therapy in the maxillary anterior region; (4) Active periodontitis with evident inflammation of the periodontal soft tissues; (5) Use of medications known to induce gingival enlargement within the past six months; (6) Pregnancy or lactation; (7) Presence of systemic diseases such as leukemia or diabetes mellitus; (8) Presence of oral parafunctional habits, such as bruxism or mouth breathing. 2.3. Research Methods 2.3.1. Acquisition of CBCT Data CBCT scans of the maxillofacial region were performed using a CBCT system (KaVo, Germany). Patients were seated upright, wearing lead aprons for radiation protection, with the head positioned such that the Frankfort horizontal plane was parallel to the floor. Scanning parameters were set as follows: field of view (diameter 23 cm × height 17 cm), tube voltage 120 kV, tube current 5 mA, voxel size 0.3 mm, and exposure time 17.8 seconds. The acquired data were saved and exported in Digital Imaging and Communications in Medicine (DICOM) format. All radiographic analyses and measurements were performed using the proprietary i-CAT imaging software bundled with the CBCT system. 2.3.2. CBCT-Based Gingival Thickness Measurement Gingival thickness in the anterior region was measured from both the CBCT data and the subsequently registered digital impression data. To ensure consistent landmark identification across all the measurement methods, the measurement sites were marked intraorally using a radiopaque flowable composite resin (Filtek™ Z350 XT, 3M ESPE). The sites were located at 2 mm, 4 mm, and 6 mm apical to the zenith of the facial gingival margin along the long axis of each maxillary anterior tooth (central incisors, lateral incisors, and canines) (Figure.1a and b). Within the imaging software, the axial cross-sectional slice corresponding to each of the three marked sites per tooth was selected. A perpendicular line was drawn from the marked point on the gingival surface to the surface of the underlying hard tissue (root or alveolar bone). The length of this line was recorded as the gingival thickness at that specific site (Figure. 1c). All CBCT measurements were performed by a radiologist from the Department of Stomatology who received specific training for this study. Each site was measured three times, and the mean value was calculated and used for subsequent analysis. 2.3.3. Acquisition of Digital Optical Impressions Intraoral scans were obtained by a single, clinically experienced operator using the same intraoral scanner (3Shape, Denmark) for all patients. Prior to scanning, the teeth and surrounding soft tissues were dried using an air syringe. The dentition and mucosa were scanned following a standardized sequence to minimize the acquisition of redundant data (Figure. 1d). Upon completion, the scan data were exported in Standard Tessellation Language (STL) format. 2.3.4. Gingival Thickness Measurement via TGP To minimize soft tissue distortion that could result from injectable anesthetic, a combination of topical and infiltration anesthesia was employed. A topical anesthetic gel was first applied to the measurement area for 5 minutes. The efficacy of anesthesia was then verified by gentle probing. For patients with inadequate anesthesia, supplemental local infiltration anesthesia was administered at the mucobuccal fold in the maxillary anterior region. At the pre-marked facial site, a size 25 K-file was inserted perpendicular to the long axis of the tooth, penetrating the gingival tissue until definite contact with the root surface was felt. A rubber stopper on the K-file was used to mark the penetration depth. The file was then removed, and the distance from the file tip to the stopper was measured using a digital caliper with an accuracy of 0.01 mm. Each site was measured three times, and the mean value was recorded as the TGP-derived gingival thickness. 2.4. Statistical Analysis Statistical analysis was performed using SPSS software (version 23.0, IBM Corp.). The normality of data distribution was assessed using the Shapiro-Wilk test. For normally distributed continuous data, independent samples t-test, paired samples t-test, and one-way analysis of variance (ANOVA) were used to compare gingival thickness differences, with post-hoc pairwise comparisons conducted using the Least Significant Difference (LSD) test. For continuous data that violated the assumption of normality, the Mann-Whitney U test and the Wilcoxon signed-rank test were employed to compare differences between genders and between measurement methods, respectively. The agreement between the TGP and CBCT measurement methods was further analyzed using Bland-Altman plots with 95% limits of agreement and Deming regression analysis. The diagnostic performance of the CBCT method for assessing gingival phenotype was evaluated using diagnostic test parameters (sensitivity, specificity, accuracy). A p-value of less than 0.05 was considered statistically significant. 3. Results 3.1. Comparison of Gingival Thickness Measured by TGP and CBCT Gingival thickness was measured at a total of 702 sites (234 teeth from 39 subjects) using both TGP method and the CBCT-based method. The overall descriptive statistics for the measurements obtained by the two methods are presented in Table 1 . Table 1 Overall descriptive statistics of gingival thickness measured by TGP and CBCT. Method n Mean ± SD (mm) Median (IQR) (mm) Range (mm) TGP 702 1.20 ± 0.42 1.18 (0.84, 1.49) 0.28–2.96 CBCT 702 1.16 ± 0.42 1.12 (0.79, 1.46) 0.25–2.93 SD, standard deviation; IQR, interquartile range (P25, P75). As the data violated the assumption of normality, the Wilcoxon signed-rank test was used for comparison. Overall, the median gingival thickness measured by TGP was 1.18 mm, which was slightly but significantly higher than the median of 1.12 mm measured by CBCT (p < 0.05). A further analysis of measurements at different apico-coronal levels is shown in Table 2 . The median TGP measurements were 1.17 mm, 1.18 mm, and 1.20 mm at the 2 mm, 4 mm, and 6 mm levels, respectively. The corresponding CBCT measurements were 1.12 mm at all three levels. The TGP measurements were consistently slightly higher than the CBCT measurements at each level, and all differences were statistically significant (p < 0.001). Table 2 Comparison of gingival thickness at different measurement levels. Level Method n Median (mm) IQR (P25-P75) (mm) Z statistic p-value 2 mm TGP 234 1.17 0.84–1.49 -12.950 < 0.001 CBCT 234 1.12 0.79–1.46 4 mm TGP 234 1.18 0.83–1.56 -12.942 < 0.001 CBCT 234 1.12 0.79–1.50 6 mm TGP 234 1.20 0.87–1.45 -12.538 < 0.001 CBCT 234 1.12 0.79–1.46 3.2. Agreement Analysis between TGP and CBCT Methods Subsequently, the agreement between the two measurement methods was analyzed. A scatter plot and a Bland-Altman plot were generated based on the TGP and CBCT results. Since the data and their differences were not normally distributed (Shapiro-Wilk test, p < 0.001), the 95% limits of agreement (LoA) were calculated using the percentile method (2.5th and 97.5th percentitles were − 0.19 mm and 0.01 mm, respectively). As shown in Figure. 2, the linear correlation coefficient between the two methods was 0.989, indicating a very strong positive correlation. In the Bland-Altman plot (Figure. 3), the differences were concentrated between − 0.19 and 0.01 mm and were not symmetrically distributed around zero, suggesting a systematic bias in the CBCT measurements. However, the spread of the differences did not show a clear increasing or decreasing trend with the magnitude of the measurements, indicating an absence of proportional bias. Deming regression was further performed to quantify the systematic error (Figure. 4, Table 3 ). The intercept was − 0.044 mm (95% CI: -0.053 to -0.035), indicating a constant error where CBCT systematically underestimated the thickness by approximately 0.04 mm. The slope was 0.997 (95% CI: 0.990 to 1.005), and its confidence interval included 1, confirming the absence of proportional error. This constant error of 0.04 mm is considered acceptable in clinical practice. Table 3 Deming regression parameters for overall agreement between TGP and CBCT measurements. Parameter Value 95% Confidence Interval Lower Bound Upper Bound Intercept (Constant Error) -0.044 mm -0.053 mm -0.035 mm Slope (Proportional Error) 0.997 0.990 1.005 The error pattern was further examined at different measurement levels (Table 4 ). The slope confidence intervals for all levels included 1, confirming no proportional error at any level. The intercept confidence interval for the 2 mm level included 0, suggesting no significant constant error at this most coronal level. However, significant constant errors were present at the 4 mm (intercept: -0.042 mm) and 6 mm (intercept: -0.027 mm) levels. While these errors are also clinically acceptable, the sequence of increasing error from 2 mm to 4/6 mm provides a reference for clinical application. Table 4 Deming regression parameters stratified by measurement level. Level Parameter Value 95% Confidence Interval Lower Upper 2 mm Intercept -0.027 mm -0.060 mm 0.007 mm Slope 0.989 0.966 1.011 4 mm Intercept -0.042 mm -0.057 mm -0.027 mm Slope 0.996 0.984 1.008 6 mm Intercept -0.027 mm -0.050 mm -0.004 mm Slope 0.973 0.945 1.000 The agreement was also assessed for different tooth types at each level using Spearman's rank correlation (Table 5 ). The results showed significant positive correlations (ρ > 0.98, p < 0.001) for all tooth-type and level combinations, indicating consistently high agreement across the anterior dentition (Figure. 5). Table 5 Correlation between TGP and CBCT measurements for different tooth types (Spearman's ρ). Level Tooth Type n ρ (Spearman) p-value 95% CI for ρ 2 mm Central Incisor 78 0.989 < 0.001 0.982–0.993 Lateral Incisor 78 0.992 < 0.001 0.987–0.995 Canine 78 0.997 < 0.001 0.996–0.998 4 mm Central Incisor 78 0.986 < 0.001 0.978–0.991 Lateral Incisor 78 0.993 < 0.001 0.989–0.996 Canine 78 0.994 < 0.001 0.991–0.996 6 mm Central Incisor 78 0.982 < 0.001 0.971–0.989 Lateral Incisor 78 0.990 < 0.001 0.984–0.994 Canine 78 0.996 < 0.001 0.993–0.997 3.3. Diagnostic Performance of CBCT for Gingival Phenotype Classification In clinical practice, a gingival thickness threshold of 1 mm is commonly used to classify phenotype (thin: ≤1 mm, thick: >1 mm) for guiding restorative and implant decisions [ 6 , 7 ]. Using TGP as the gold standard, the diagnostic performance of CBCT was evaluated (Table 6 ). Table 6 Diagnostic performance of CBCT for gingival phenotype classification using a 1-mm threshold. TGP Sensitivity % Specificity % Accuracy % McNemar Test χ 2 (P value) Thick n(%) Thin n(%) Total n(%) Kappa 2mm CBCT Thick 141(100) 0(0) 141(100) 99.3 100 99.6 0(1.000) 0.991 Thin 1(0.7) 92(99.3) 93(100) Total 142(60.7) 92(39.3) 234(100) 4mm CBCT Thick 143(100) 0(0) 143(100) 99.3 100 99.6 0(1.000) 0.991 Thin 1(1.1) 90(98.9) 91(100) Total 144(61.5) 90(38.5) 234(100) 6mm CBCT Thick 144(100) 0(0) 144(100) 96.6 100 97.9 1.60(0.063) 0.954 Thin 5(5.6) 85(94.4) 90(100) Total 149(63.7) 85(36.3) 234(100) overall CBCT Thick 428(100) 0(0) 428(100) 98.4 100 99.7 1.71(0.016*) 0.979 Thin 7(2.6) 267(97.4) 274(100) Total 435(62.0) 267(38.0) 702(100) From Table 6 , the specificity of CBCT for diagnosing the thick phenotype was 100% at all levels and overall. Sensitivity was slightly lower but still excellent, being highest at the 2 mm and 4 mm levels (99.3%) and lowest at the 6 mm level (96.6%). This indicates that CBCT misclassified a very small number of thick phenotypes as thin, while it correctly identified all thin phenotypes, which aligns with the systematic underestimation identified by Deming regression. McNemar's test showed no significant difference in the diagnostic distribution between the two methods at individual measurement levels (p > 0.05). However, when data were pooled across all levels, a significant difference emerged (p = 0.016). This apparent contradiction can be explained by the low frequency of false-negative cases at each individual level, which was insufficient for the test to detect a difference. Pooling the data amplified this effect by summing the false-negative cases from all levels, allowing the test to reach statistical significance. The pooled result, showing more false negatives than false positives, further corroborates the conclusion of systematic underestimation by CBCT. The agreement between the two methods for phenotype classification was almost satisfied, with Kappa values exceeding 0.95 at all levels and overall. In summary, despite the systematic underestimation, the error is minimal and the agreement with the gold standard is excellent for clinical phenotype classification. 3.4. Comparison of Gingival Thickness between Genders The distribution of gingival thickness between male and female participants was compared at each measurement level using the Mann-Whitney U test, as the data were non-normally distributed (Table 7 ). No statistically significant differences were found at any of the three levels (2 mm, 4 mm, 6 mm; p > 0.05). Table 7 Comparison of gingival thickness between genders at different levels. Level Gender n Median (mm) U statistic Z score p-value 2 mm Male 144 1.46 6160.5 -0.636 0.525 Female 90 1.48 4 mm Male 144 1.12 6703.5 0.444 0.657 Female 90 1.03 6 mm Male 144 0.85 5685.0 -1.587 0.113 Female 90 0.71 Discussion Accurate assessment of gingival phenotype is fundamental for predicting tissue responses and achieving stable outcomes following periodontal, restorative, and implant therapies [ 1 , 8 ]. This study introduces and validates a novel, non-invasive CBCT-based protocol for measuring gingival thickness, demonstrating its high agreement with the TGP gold standard while elucidating a consistent, quantifiable error profile. The primary finding was a strong correlation (ρ > 0.98) and excellent diagnostic agreement (Kappa > 0.95) between CBCT and TGP measurements. A pivotal and nuanced finding was the identification of a constant systematic error through Deming regression, where CBCT consistently underestimated gingival thickness by approximately 0.04 mm, without evidence of proportional error. This systematic underestimation, likely attributable to the inherent challenge of defining low-density soft tissue boundaries in CBCT imaging[ 9 – 11 ], provides crucial context for interpreting comparative results. It explains the statistically significant differences in median thickness values reported in Section 3.1 , framing them not as random discrepancy but as a predictable, unidirectional bias. The analysis of diagnostic agreement further illuminates the distinction between statistical significance and clinical relevance. While McNemar's test showed no significant difference in phenotype classification at individual measurement levels, a significant difference emerged when all data were pooled (p = 0.016). This apparent paradox is resolved by understanding statistical power: the pooled analysis (n = 702) amplified the detection of the minute, systematic bias revealed by Deming regression. This bias resulted in a very small number of false-negative classifications (7 out of 702 sites, 1.0%). Crucially, the near-perfect agreement indices (Kappa > 0.95, accuracy 99.7%) demonstrate that this statistically significant difference carries negligible clinical consequence for phenotype assessment. The 0.04 mm error is substantially smaller than the clinical probing tolerance and the 1 mm diagnostic threshold, making it highly unlikely to alter key clinical decisions, such as the need for connective tissue grafting [ 12 , 13 ]. Our protocol’s innovation—the use of radiopaque resin markers—addresses a limitation of conventional CBCT assessment. It eliminates the need for lip retractors, thereby avoiding the soft tissue distortion and measurement inaccuracy associated with such maneuvers [ 14 ]. This enhances patient comfort and measurement fidelity in a single step. The high diagnostic performance (100% specificity, > 96% sensitivity) confirms the protocol's reliability for chairside phenotype assessment. Contrary to some literature [ 15 – 17 ], we found no significant gender-based differences in gingival thickness within our cohort. This discrepancy may relate to sample size, ethnic background, or precise measurement methodology. Our findings align more closely with studies reporting no significant gender association [ 18 – 20 ], highlighting the need for further investigation in larger, multi-ethnic populations. The use of digital impression data and marked CBCT scans, as piloted in this study, points toward a clinical advancement. This fusion combines the superior hard tissue resolution of CBCT with the exquisite surface detail of intraoral scans. The resulting integrated digital model allows for precise visualization of the mucogingival complex and non-invasive measurement of keratinized tissue width, which is critical for planning procedures like implant placement in the aesthetic zone or mucogingival surgeries [ 21 , 22 ]. While this approach requires additional training and time, it represents a powerful tool for pre-surgical digital planning and patient-specific outcome simulation. Nonetheless, this study has limitations. The single-center design and specific CBCT parameters used warrant validation across different devices and settings. The sample size, while sufficient for the primary agreement analysis, may be underpowered for detecting subtle demographic associations. Future multi-center studies with larger, diverse populations are recommended to enhance generalizability and explore correlations with long-term clinical outcomes. In conclusion, this study presents a validated, clinically practical CBCT protocol for non-invasive gingival thickness measurement. By employing radiopaque markers to eliminate lip retraction, it overcomes a common source of error. The identified constant systematic error is minimal, quantifiable, and clinically acceptable. This protocol provides a reliable, patient-friendly alternative to invasive probing for gingival phenotype assessment. Its inherent compatibility with digital dentistry workflows further enhances its value for comprehensive, digitally guided treatment planning. Further studies are needed to confirm these results and to explore more useful, reliable, and accurate method for assessing the gingival phenotype in clinical practice and research. Declarations Ethics approval and consent to participate This study was conducted in accordance with the principles outlined in the Declaration of Helsinki. This study was approved by the Medical Ethics Committee of the General Hospital of the Northern Theater Command (Approval No.: Y 2026036). Prior to the commencement of the study, the research protocol was explained in detail to each participant or their legal guardian, and written informed consent was obtained. Clinical trial number Not applicable Competing interests The authors have no conflicts of interest relevant to this article. Consent for publication Not applicable Funding The study was supported by the Department of Science and Technology of Liaoning Province, China (grant number: 2025JH2/101800034). Author Contribution Conceptualization: J Wang. Data curation: J Wang, S Qu. Formal analysis: N Zhang, S Qu. Funding acquisition: J Wang. Investigation: J Dong, N Zhang. Methodology: J Wang, S Qu. Software: S Qu, G Li. Validation: L Kluwe. Visualization: J Dong. Writing - original draft: J Wang. Writing - review & editing: J Wang, S Qu. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9010834","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618013682,"identity":"41ade826-c800-47b3-a4e0-a16cd24a50b1","order_by":0,"name":"Ning Zhang","email":"","orcid":"","institution":"General Hospital of Northern Theater Command","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Zhang","suffix":""},{"id":618013683,"identity":"a4928ee5-3cff-4876-a387-2df6a3af1fb0","order_by":1,"name":"Jijun Dong","email":"","orcid":"","institution":"General Hospital of Northern Theater Command","correspondingAuthor":false,"prefix":"","firstName":"Jijun","middleName":"","lastName":"Dong","suffix":""},{"id":618013684,"identity":"dd3fcbda-fe7c-426f-a3a1-d6dee867014c","order_by":2,"name":"Shuang Qu","email":"","orcid":"","institution":"The 941st Hospital of People's Liberation Army Joint Logistic Support Force","correspondingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Qu","suffix":""},{"id":618013686,"identity":"663601b7-647f-4cc6-b17a-86cd810c04e0","order_by":3,"name":"Guangda Li","email":"","orcid":"","institution":"National Clinical Research Centre for Oral Diseases, The Third Affiliated Hospital of Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guangda","middleName":"","lastName":"Li","suffix":""},{"id":618013689,"identity":"feb28db8-df15-4542-b580-6832e1fe043b","order_by":4,"name":"Lan Kluwe","email":"","orcid":"","institution":"University Medical Center Hamburg-Eppendorf","correspondingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Kluwe","suffix":""},{"id":618013693,"identity":"b0b1d2d3-b0e9-4687-99e2-9b8cebd17e3b","order_by":5,"name":"Jingfu Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYFACHoYDDAZQ9geStTDOIFYLHDDz4FaGAPKzew8eLii4l9g/u/mZtE2ZNQN/e3cCXi0Gd84lHJ5hUJw4484xY+Occ+kMEmfObsCvRSLH4DCPQYIxw40Ew8e5bYeBIrn4tcjPgGqRv5H+4bAlMVoYbkC0yBncyDF8zEiMFoM7ZyBaDG/kFBv2nEvnIegX+dk9xp95/iTwyN1I3ybxo8xajr+9l4DDJFB4bMREDboWwjpGwSgYBaNgxAEAWllFfwjzvXoAAAAASUVORK5CYII=","orcid":"","institution":"General Hospital of Northern Theater Command","correspondingAuthor":true,"prefix":"","firstName":"Jingfu","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-03-02 13:39:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9010834/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9010834/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106545666,"identity":"64515ced-67ac-4f37-9c1a-58772cbcbc79","added_by":"auto","created_at":"2026-04-09 16:52:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1883578,"visible":true,"origin":"","legend":"\u003cp\u003eImage and screenshot of the measurement location. (a) Intraoral photograph showing gingival markings. (b) CBCT three-dimensional reconstruction image with gingival markings. (c) CBCT image of the measurement location. The thickness measurement direction perpendicular to long axis of the tooth (orange line). (d) A screenshot from 3Shape United Studio software. The screenshot can be used for the visual assessment of gingival keratinization.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9010834/v1/0b04eb6e8cc3b45897a64a77.png"},{"id":106724926,"identity":"7203be22-29bf-4009-bed6-d24c9cbf2dc2","added_by":"auto","created_at":"2026-04-12 18:30:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":188626,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot showing the correlation between gingival thickness measurements obtained by TGP and CBCT. The solid line represents the line of identity.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9010834/v1/1d20e3bc70a3e14099f9e187.png"},{"id":106959685,"identity":"b39d1e33-2fba-4536-8754-9388bf6a270e","added_by":"auto","created_at":"2026-04-15 09:13:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":170767,"visible":true,"origin":"","legend":"\u003cp\u003eBland-Altman plot for assessing agreement between TGP and CBCT methods. The solid middle line represents the mean difference. The dashed lines represent the 95% limits of agreement calculated by the percentile method.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9010834/v1/855c1bbabf5812856289a84c.png"},{"id":106545669,"identity":"826ee6d4-eb54-4527-ab80-75afcf16404a","added_by":"auto","created_at":"2026-04-09 16:52:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":46513,"visible":true,"origin":"","legend":"\u003cp\u003eDeming regression plot for the agreement between TGP (reference method) and CBCT measurements. The regression line (solid) and its 95% confidence interval (dashed lines) are shown.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9010834/v1/9e7d83e86f640eae8a354d07.png"},{"id":106725946,"identity":"7eebbdd8-4864-4a25-83b4-5bbac16c3dbf","added_by":"auto","created_at":"2026-04-12 18:34:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":425991,"visible":true,"origin":"","legend":"\u003cp\u003eBland-Altman plots stratified by tooth type and measurement level, demonstrating consistent agreement across central incisors, lateral incisors, and canines at the 2 mm, 4 mm, and 6 mm levels. The dashed lines represent the 95% limits of agreement for each subgroup.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9010834/v1/0eb16d7ea6ae31881e3a39f0.png"},{"id":106967062,"identity":"af43e697-67ce-403b-8206-5124af4e32a6","added_by":"auto","created_at":"2026-04-15 10:02:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3602103,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9010834/v1/786324eb-7d74-4eb7-966e-462aa60d1e6b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gingival Phenotype Assessment: A Comparative Study of A Novel CBCT-Based Measurement Technique and Transgingival Probing","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe gingival phenotype is a crucial prognostic factor influencing the outcomes of various dental treatments[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the aesthetically sensitive anterior region, it is a key clinical parameter of significant concern across multiple disciplines, including implantology, prosthodontics, orthodontics, periodontology, and operative dentistry. Gingival phenotypes are primarily categorized into the thin and thick types[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These different phenotypes exhibit varied responses to physiological, pathological, and therapeutic stimuli. Due to its poorer tissue stability, the thin phenotype is often associated with more pronounced gingival recession following insult compared to the thick phenotype, which generally yields more stable and predictable clinical outcomes[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Consequently, for an identical clinical procedure, the prognosis can differ substantially among patients with different gingival phenotypes. Therefore, the accurate determination of the gingival thickness holds significant clinical importance for both treatment planning and the evaluation of post-therapeutic results.\u003c/p\u003e \u003cp\u003eAs the classification of gingival phenotype is predominantly based on gingival thickness, its accurate clinical assessment is of paramount importance. Current primary methods for measuring gingival thickness include the transgingival probing method (TGP), the periodontal probe transparency method, and cone-beam computed tomography (CBCT). While the transgingival probing method can obtain relatively accurate measurements at different sites, its invasive nature often results in poor patient acceptance. The periodontal probe transparency method, although non-invasive and painless, yields unstable results with considerable measurement error. CBCT provides high imaging resolution and more accurate sagittal views compared to spiral CT, meeting the requirements for oral soft tissue imaging; however, its measurements are still subject to certain inherent errors[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, there remains some debate regarding non-invasive methods for measuring gingival thickness and the determination of gingival phenotype. Therefore, this study designed a gingival thickness measurement protocol that integrates CBCT with digital impressions and evaluated the relationship between gingival phenotype and specific measurement points across different genders. Utilizing Bland-Altman agreement analysis, this study demonstrated a high level of agreement between the CBCT-based gingival thickness measurement method and the transgingival probing method. Subsequently, Deming regression analysis was employed to verify a constant systematic error between the two methods, confirming that the proposed method possesses high measurement accuracy. Thus, this work provides clinical and theoretical references for the assessment of gingival phenotype.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Design and Ethical Considerations\u003c/h2\u003e \u003cp\u003e This cross-sectional study was conducted in strict accordance with the ethical principles outlined in the Declaration of Helsinki and received approval from the Medical Ethics Committee of the General Hospital of the Northern Theater Command (Approval No.: Y 2024036). Prior to the commencement of the study, the research protocol was explained in detail to each participant or their legal guardian, and written informed consent was obtained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Subjects\u003c/h2\u003e \u003cp\u003ePatients who received dental treatment at the Department of Stomatology, General Hospital of the Northern Theater Command between June 2024 and October 2025 were recruited. A total of 39 patients were included, comprising 24 males and 15 females, with a mean age of 36\u0026thinsp;\u0026plusmn;\u0026thinsp;12 years.\u003c/p\u003e \u003cp\u003eInclusion criteria were as follows: (1) Periodontal health, defined as a Gingival Index of 0, absence of bleeding on probing, probing depth\u0026thinsp;\u0026le;\u0026thinsp;3 mm, and no obvious gingival recession or clinical attachment loss; (2) No history of trauma, tumors, or congenital deformities in the maxilla; (3) No tooth crowding or missing teeth in the maxillary anterior region; (4) Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years.\u003c/p\u003e \u003cp\u003eExclusion criteria were as follows: (1) Presence of coronal defects or missing teeth in the maxillary anterior region; (2) Presence of implants, crown restorations, or impacted teeth in the maxillary anterior teeth; (3) History of root canal treatment or orthodontic therapy in the maxillary anterior region; (4) Active periodontitis with evident inflammation of the periodontal soft tissues; (5) Use of medications known to induce gingival enlargement within the past six months; (6) Pregnancy or lactation; (7) Presence of systemic diseases such as leukemia or diabetes mellitus; (8) Presence of oral parafunctional habits, such as bruxism or mouth breathing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Research Methods\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Acquisition of CBCT Data\u003c/h2\u003e \u003cp\u003eCBCT scans of the maxillofacial region were performed using a CBCT system (KaVo, Germany). Patients were seated upright, wearing lead aprons for radiation protection, with the head positioned such that the Frankfort horizontal plane was parallel to the floor. Scanning parameters were set as follows: field of view (diameter 23 cm \u0026times; height 17 cm), tube voltage 120 kV, tube current 5 mA, voxel size 0.3 mm, and exposure time 17.8 seconds. The acquired data were saved and exported in Digital Imaging and Communications in Medicine (DICOM) format. All radiographic analyses and measurements were performed using the proprietary i-CAT imaging software bundled with the CBCT system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. CBCT-Based Gingival Thickness Measurement\u003c/h2\u003e \u003cp\u003eGingival thickness in the anterior region was measured from both the CBCT data and the subsequently registered digital impression data. To ensure consistent landmark identification across all the measurement methods, the measurement sites were marked intraorally using a radiopaque flowable composite resin (Filtek\u0026trade; Z350 XT, 3M ESPE). The sites were located at 2 mm, 4 mm, and 6 mm apical to the zenith of the facial gingival margin along the long axis of each maxillary anterior tooth (central incisors, lateral incisors, and canines) (Figure.1a and b). Within the imaging software, the axial cross-sectional slice corresponding to each of the three marked sites per tooth was selected. A perpendicular line was drawn from the marked point on the gingival surface to the surface of the underlying hard tissue (root or alveolar bone). The length of this line was recorded as the gingival thickness at that specific site (Figure. 1c). All CBCT measurements were performed by a radiologist from the Department of Stomatology who received specific training for this study. Each site was measured three times, and the mean value was calculated and used for subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Acquisition of Digital Optical Impressions\u003c/h2\u003e \u003cp\u003eIntraoral scans were obtained by a single, clinically experienced operator using the same intraoral scanner (3Shape, Denmark) for all patients. Prior to scanning, the teeth and surrounding soft tissues were dried using an air syringe. The dentition and mucosa were scanned following a standardized sequence to minimize the acquisition of redundant data (Figure. 1d). Upon completion, the scan data were exported in Standard Tessellation Language (STL) format.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4. Gingival Thickness Measurement via TGP\u003c/h2\u003e \u003cp\u003eTo minimize soft tissue distortion that could result from injectable anesthetic, a combination of topical and infiltration anesthesia was employed. A topical anesthetic gel was first applied to the measurement area for 5 minutes. The efficacy of anesthesia was then verified by gentle probing. For patients with inadequate anesthesia, supplemental local infiltration anesthesia was administered at the mucobuccal fold in the maxillary anterior region. At the pre-marked facial site, a size 25 K-file was inserted perpendicular to the long axis of the tooth, penetrating the gingival tissue until definite contact with the root surface was felt. A rubber stopper on the K-file was used to mark the penetration depth. The file was then removed, and the distance from the file tip to the stopper was measured using a digital caliper with an accuracy of 0.01 mm. Each site was measured three times, and the mean value was recorded as the TGP-derived gingival thickness.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using SPSS software (version 23.0, IBM Corp.). The normality of data distribution was assessed using the Shapiro-Wilk test. For normally distributed continuous data, independent samples t-test, paired samples t-test, and one-way analysis of variance (ANOVA) were used to compare gingival thickness differences, with post-hoc pairwise comparisons conducted using the Least Significant Difference (LSD) test. For continuous data that violated the assumption of normality, the Mann-Whitney U test and the Wilcoxon signed-rank test were employed to compare differences between genders and between measurement methods, respectively. The agreement between the TGP and CBCT measurement methods was further analyzed using Bland-Altman plots with 95% limits of agreement and Deming regression analysis. The diagnostic performance of the CBCT method for assessing gingival phenotype was evaluated using diagnostic test parameters (sensitivity, specificity, accuracy). A p-value of less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. Comparison of Gingival Thickness Measured by TGP and CBCT\u003c/h2\u003e\n\u003cp\u003eGingival thickness was measured at a total of 702 sites (234 teeth from 39 subjects) using both TGP method and the CBCT-based method. The overall descriptive statistics for the measurements obtained by the two methods are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eOverall descriptive statistics of gingival thickness measured by TGP and CBCT.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMethod\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003en\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (mm)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMedian (IQR) (mm)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRange (mm)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTGP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e702\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.18 (0.84, 1.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.28\u0026ndash;2.96\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCBCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e702\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.12 (0.79, 1.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.25\u0026ndash;2.93\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eSD, standard deviation; IQR, interquartile range (P25, P75).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs the data violated the assumption of normality, the Wilcoxon signed-rank test was used for comparison. Overall, the median gingival thickness measured by TGP was 1.18 mm, which was slightly but significantly higher than the median of 1.12 mm measured by CBCT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eA further analysis of measurements at different apico-coronal levels is shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The median TGP measurements were 1.17 mm, 1.18 mm, and 1.20 mm at the 2 mm, 4 mm, and 6 mm levels, respectively. The corresponding CBCT measurements were 1.12 mm at all three levels. The TGP measurements were consistently slightly higher than the CBCT measurements at each level, and all differences were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparison of gingival thickness at different measurement levels.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLevel\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMethod\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003en\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMedian (mm)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIQR (P25-P75) (mm)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eZ statistic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTGP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.84\u0026ndash;1.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-12.950\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCBCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.79\u0026ndash;1.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTGP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.83\u0026ndash;1.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-12.942\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCBCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.79\u0026ndash;1.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTGP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.87\u0026ndash;1.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-12.538\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCBCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.79\u0026ndash;1.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2. Agreement Analysis between TGP and CBCT Methods\u003c/h2\u003e\n\u003cp\u003eSubsequently, the agreement between the two measurement methods was analyzed. A scatter plot and a Bland-Altman plot were generated based on the TGP and CBCT results. Since the data and their differences were not normally distributed (Shapiro-Wilk test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the 95% limits of agreement (LoA) were calculated using the percentile method (2.5th and 97.5th percentitles were \u0026minus;\u0026thinsp;0.19 mm and 0.01 mm, respectively).\u003c/p\u003e\n\u003cp\u003eAs shown in Figure. 2, the linear correlation coefficient between the two methods was 0.989, indicating a very strong positive correlation. In the Bland-Altman plot (Figure. 3), the differences were concentrated between \u0026minus;\u0026thinsp;0.19 and 0.01 mm and were not symmetrically distributed around zero, suggesting a systematic bias in the CBCT measurements. However, the spread of the differences did not show a clear increasing or decreasing trend with the magnitude of the measurements, indicating an absence of proportional bias.\u003c/p\u003e\n\u003cp\u003eDeming regression was further performed to quantify the systematic error (Figure. 4, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The intercept was \u0026minus;\u0026thinsp;0.044 mm (95% CI: -0.053 to -0.035), indicating a constant error where CBCT systematically underestimated the thickness by approximately 0.04 mm. The slope was 0.997 (95% CI: 0.990 to 1.005), and its confidence interval included 1, confirming the absence of proportional error. This constant error of 0.04 mm is considered acceptable in clinical practice.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDeming regression parameters for overall agreement between TGP and CBCT measurements.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eParameter\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eValue\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e95% Confidence Interval\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLower Bound\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUpper Bound\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntercept (Constant Error)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.044 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.053 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.035 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSlope (Proportional Error)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.997\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.990\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe error pattern was further examined at different measurement levels (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The slope confidence intervals for all levels included 1, confirming no proportional error at any level. The intercept confidence interval for the 2 mm level included 0, suggesting no significant constant error at this most coronal level. However, significant constant errors were present at the 4 mm (intercept: -0.042 mm) and 6 mm (intercept: -0.027 mm) levels. While these errors are also clinically acceptable, the sequence of increasing error from 2 mm to 4/6 mm provides a reference for clinical application.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDeming regression parameters stratified by measurement level.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eLevel\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eParameter\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eValue\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e95% Confidence Interval\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLower\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUpper\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntercept\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.027 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.060 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSlope\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.989\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.966\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.011\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntercept\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.042 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.057 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.027 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSlope\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.984\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntercept\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.027 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.050 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.004 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSlope\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.973\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.945\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe agreement was also assessed for different tooth types at each level using Spearman's rank correlation (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The results showed significant positive correlations (\u0026rho;\u0026thinsp;\u0026gt;\u0026thinsp;0.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for all tooth-type and level combinations, indicating consistently high agreement across the anterior dentition (Figure. 5).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCorrelation between TGP and CBCT measurements for different tooth types (Spearman's \u0026rho;).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLevel\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTooth Type\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003en\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026rho; (Spearman)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95% CI for \u0026rho;\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCentral Incisor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.989\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.982\u0026ndash;0.993\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLateral Incisor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.992\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.987\u0026ndash;0.995\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCanine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.997\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.996\u0026ndash;0.998\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCentral Incisor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.986\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.978\u0026ndash;0.991\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLateral Incisor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.993\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.989\u0026ndash;0.996\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCanine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.994\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.991\u0026ndash;0.996\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCentral Incisor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.982\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.971\u0026ndash;0.989\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLateral Incisor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.990\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.984\u0026ndash;0.994\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCanine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.993\u0026ndash;0.997\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3. Diagnostic Performance of CBCT for Gingival Phenotype Classification\u003c/h2\u003e\n\u003cp\u003eIn clinical practice, a gingival thickness threshold of 1 mm is commonly used to classify phenotype (thin: \u0026le;1 mm, thick: \u0026gt;1 mm) for guiding restorative and implant decisions [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. Using TGP as the gold standard, the diagnostic performance of CBCT was evaluated (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDiagnostic performance of CBCT for gingival phenotype classification using a 1-mm threshold.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eTGP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSensitivity\u003c/p\u003e\n\u003cp\u003e%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSpecificity\u003c/p\u003e\n\u003cp\u003e%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAccuracy\u003c/p\u003e\n\u003cp\u003e%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMcNemar Test\u003c/p\u003e\n\u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(P value)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThick\u003c/p\u003e\n\u003cp\u003en(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThin\u003c/p\u003e\n\u003cp\u003en(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003cp\u003en(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKappa\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e2mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eCBCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThick\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e141(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0(0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e141(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e99.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e99.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0(1.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.991\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1(0.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92(99.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e142(60.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92(39.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e234(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e4mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eCBCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThick\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e143(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0(0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e143(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e99.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e99.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0(1.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.991\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1(1.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90(98.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e144(61.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90(38.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e234(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e6mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eCBCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThick\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e144(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0(0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e144(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e96.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e97.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.60(0.063)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.954\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5(5.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85(94.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e149(63.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85(36.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e234(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eoverall\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eCBCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThick\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e428(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0(0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e428(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e98.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e99.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.71(0.016*)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.979\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7(2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e267(97.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e274(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e435(62.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e267(38.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e702(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003eFrom Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, the specificity of CBCT for diagnosing the thick phenotype was 100% at all levels and overall. Sensitivity was slightly lower but still excellent, being highest at the 2 mm and 4 mm levels (99.3%) and lowest at the 6 mm level (96.6%). This indicates that CBCT misclassified a very small number of thick phenotypes as thin, while it correctly identified all thin phenotypes, which aligns with the systematic underestimation identified by Deming regression.\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eMcNemar's test showed no significant difference in the diagnostic distribution between the two methods at individual measurement levels (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, when data were pooled across all levels, a significant difference emerged (p\u0026thinsp;=\u0026thinsp;0.016). This apparent contradiction can be explained by the low frequency of false-negative cases at each individual level, which was insufficient for the test to detect a difference. Pooling the data amplified this effect by summing the false-negative cases from all levels, allowing the test to reach statistical significance. The pooled result, showing more false negatives than false positives, further corroborates the conclusion of systematic underestimation by CBCT.\u003c/p\u003e\n\u003cp\u003eThe agreement between the two methods for phenotype classification was almost satisfied, with Kappa values exceeding 0.95 at all levels and overall. In summary, despite the systematic underestimation, the error is minimal and the agreement with the gold standard is excellent for clinical phenotype classification.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4. Comparison of Gingival Thickness between Genders\u003c/h2\u003e\n\u003cp\u003eThe distribution of gingival thickness between male and female participants was compared at each measurement level using the Mann-Whitney U test, as the data were non-normally distributed (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). No statistically significant differences were found at any of the three levels (2 mm, 4 mm, 6 mm; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab8\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparison of gingival thickness between genders at different levels.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLevel\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003en\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMedian (mm)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eU statistic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eZ score\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6160.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.636\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.525\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6703.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.657\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5685.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1.587\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.113\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAccurate assessment of gingival phenotype is fundamental for predicting tissue responses and achieving stable outcomes following periodontal, restorative, and implant therapies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This study introduces and validates a novel, non-invasive CBCT-based protocol for measuring gingival thickness, demonstrating its high agreement with the TGP gold standard while elucidating a consistent, quantifiable error profile.\u003c/p\u003e \u003cp\u003eThe primary finding was a strong correlation (ρ\u0026thinsp;\u0026gt;\u0026thinsp;0.98) and excellent diagnostic agreement (Kappa\u0026thinsp;\u0026gt;\u0026thinsp;0.95) between CBCT and TGP measurements. A pivotal and nuanced finding was the identification of a constant systematic error through Deming regression, where CBCT consistently underestimated gingival thickness by approximately 0.04 mm, without evidence of proportional error. This systematic underestimation, likely attributable to the inherent challenge of defining low-density soft tissue boundaries in CBCT imaging[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], provides crucial context for interpreting comparative results. It explains the statistically significant differences in median thickness values reported in Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e, framing them not as random discrepancy but as a predictable, unidirectional bias.\u003c/p\u003e \u003cp\u003eThe analysis of diagnostic agreement further illuminates the distinction between statistical significance and clinical relevance. While McNemar's test showed no significant difference in phenotype classification at individual measurement levels, a significant difference emerged when all data were pooled (p\u0026thinsp;=\u0026thinsp;0.016). This apparent paradox is resolved by understanding statistical power: the pooled analysis (n\u0026thinsp;=\u0026thinsp;702) amplified the detection of the minute, systematic bias revealed by Deming regression. This bias resulted in a very small number of false-negative classifications (7 out of 702 sites, 1.0%). Crucially, the near-perfect agreement indices (Kappa\u0026thinsp;\u0026gt;\u0026thinsp;0.95, accuracy 99.7%) demonstrate that this statistically significant difference carries negligible clinical consequence for phenotype assessment. The 0.04 mm error is substantially smaller than the clinical probing tolerance and the 1 mm diagnostic threshold, making it highly unlikely to alter key clinical decisions, such as the need for connective tissue grafting [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur protocol\u0026rsquo;s innovation\u0026mdash;the use of radiopaque resin markers\u0026mdash;addresses a limitation of conventional CBCT assessment. It eliminates the need for lip retractors, thereby avoiding the soft tissue distortion and measurement inaccuracy associated with such maneuvers [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This enhances patient comfort and measurement fidelity in a single step. The high diagnostic performance (100% specificity, \u0026gt;\u0026thinsp;96% sensitivity) confirms the protocol's reliability for chairside phenotype assessment.\u003c/p\u003e \u003cp\u003eContrary to some literature [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], we found no significant gender-based differences in gingival thickness within our cohort. This discrepancy may relate to sample size, ethnic background, or precise measurement methodology. Our findings align more closely with studies reporting no significant gender association [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], highlighting the need for further investigation in larger, multi-ethnic populations.\u003c/p\u003e \u003cp\u003eThe use of digital impression data and marked CBCT scans, as piloted in this study, points toward a clinical advancement. This fusion combines the superior hard tissue resolution of CBCT with the exquisite surface detail of intraoral scans. The resulting integrated digital model allows for precise visualization of the mucogingival complex and non-invasive measurement of keratinized tissue width, which is critical for planning procedures like implant placement in the aesthetic zone or mucogingival surgeries [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. While this approach requires additional training and time, it represents a powerful tool for pre-surgical digital planning and patient-specific outcome simulation.\u003c/p\u003e \u003cp\u003eNonetheless, this study has limitations. The single-center design and specific CBCT parameters used warrant validation across different devices and settings. The sample size, while sufficient for the primary agreement analysis, may be underpowered for detecting subtle demographic associations. Future multi-center studies with larger, diverse populations are recommended to enhance generalizability and explore correlations with long-term clinical outcomes.\u003c/p\u003e \u003cp\u003eIn conclusion, this study presents a validated, clinically practical CBCT protocol for non-invasive gingival thickness measurement. By employing radiopaque markers to eliminate lip retraction, it overcomes a common source of error. The identified constant systematic error is minimal, quantifiable, and clinically acceptable. This protocol provides a reliable, patient-friendly alternative to invasive probing for gingival phenotype assessment. Its inherent compatibility with digital dentistry workflows further enhances its value for comprehensive, digitally guided treatment planning. Further studies are needed to confirm these results and to explore more useful, reliable, and accurate method for assessing the gingival phenotype in clinical practice and research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This study was conducted in accordance with the principles outlined in the Declaration of Helsinki. This study was approved by the Medical Ethics Committee of the General Hospital of the Northern Theater Command (Approval No.: Y 2026036). Prior to the commencement of the study, the research protocol was explained in detail to each participant or their legal guardian, and written informed consent was obtained.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors have no conflicts of interest relevant to this article.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study was supported by the Department of Science and Technology of Liaoning Province, China (grant number: 2025JH2/101800034).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: J Wang. Data curation: J Wang, S Qu. Formal analysis: N Zhang, S Qu. Funding acquisition: J Wang. Investigation: J Dong, N Zhang. Methodology: J Wang, S Qu. Software: S Qu, G Li. Validation: L Kluwe. Visualization: J Dong. Writing - original draft: J Wang. Writing - review \u0026amp; editing: J Wang, S Qu.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data that support the findings of this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarootchi S, Tavelli L, Di Gianfilippo R, Shedden K, Oh TJ, Rasperini G, Neiva R, Giannobile WV, Wang HL. Soft tissue phenotype modification predicts gingival margin long-term (10-year) stability: Longitudinal analysis of six randomized clinical trials. J Clin Periodontol. 2022;49(7):672\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalpartida-Carrillo V, Tinedo-Lopez PL, Guerrero ME, Amaya-Pajares SP, \u0026Ouml;zcan M, R\u0026ouml;sing CK. Periodontal phenotype: A review of historical and current classifications evaluating different methods and characteristics. J Esthet Restor Dent. 2021;33(3):432\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlasiri MM, Almalki A, Alotaibi S, Alshehri A, Alkhuraiji AA, Thomas JT. Association between Gingival Phenotype and Periodontal Disease Severity-A Comparative Longitudinal Study among Patients Undergoing Fixed Orthodontic Therapy and Invisalign Treatment. Healthc (Basel) 2024, 12(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim DM, Bassir SH, Nguyen TT. Effect of gingival phenotype on the maintenance of periodontal health: An American Academy of Periodontology best evidence review. J Periodontol. 2020;91(3):311\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSabri H, Nava P, Hazrati P, Alrmali A, Galindo-Fernandez P, Saleh MHA, Calatrava J, Barootchi S, Tavelli L, Wang HL. Comparison of Ultrasonography, CBCT, Transgingival Probing, Colour-Coded and Periodontal Probe Transparency With Histological Gingival Thickness: A Diagnostic Accuracy Study Revisiting Thick Versus Thin Gingiva. J Clin Periodontol. 2025;52(4):547\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah R, Sowmya NK, Mehta DS. Prevalence of gingival biotype and its relationship to clinical parameters. Contemp Clin Dent. 2015;6(Suppl 1):S167\u0026ndash;171.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbraham S, Deepak KT, Ambili R, Preeja C, Archana V. Gingival biotype and its clinical significance \u0026ndash; A review. Saudi J Dent Res. 2014;5(1):3\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAşkın D, Tayman MA, \u0026Ccedil;elik B, Kamburoğlu K, \u0026Ouml;zen D. Comparison of gingival and periodontal phenotype classification methods and phenotype-related clinical parameters: cross-sectional observational study. BMC Oral Health. 2025;25(1):620.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYilmaz HG, Boke F, Ayali A. Cone-beam computed tomography evaluation of the soft tissue thickness and greater palatine foramen location in the palate. J Clin Periodontol. 2015;42(5):458\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong G, He Y, Liu X, Dai J, Xie Y, Liang X. Better Cone-Beam CT Artifact Correction via Spatial and Channel Reconstruction Convolution Based on Unsupervised Adversarial Diffusion Models. Bioeng (Basel) 2025, 12(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMing X, Cheng X, Tian C, Li W, Wang R, Qian C, Zeng X. Evaluation of condylar osseous changes using a wireless detector with proton density-weighted imaging sequences. Quant Imaging Med Surg. 2023;13(1):17\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKloukos D, Koukos G, Gkantidis N, Sculean A, Katsaros C, Stavropoulos A. Transgingival probing: a clinical gold standard for assessing gingival thickness. Quintessence Int. 2021;52(5):394\u0026ndash;401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchertel Cassiano L, Barriviera M, Suzuki S, Giacomelli Nascimento G, Louren\u0026ccedil;o Januario A, Hilgert LA, Rodrigues Duarte W. Soft tissue cone beam computed tomography (ST-CBCT) for the planning of esthetic crown lengthening procedures. Int J Esthet Dent. 2016;11(4):482\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNikiforidou M, Tsalikis L, Angelopoulos C, Menexes G, Vouros I, Konstantinides A. Classification of periodontal biotypes with the use of CBCT. A cross-sectional study. Clin Oral Investig. 2016;20(8):2061\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEsfahanizadeh N, Daneshparvar N, Askarpour F, Akhoundi N, Panjnoush M. Correlation Between Bone and Soft Tissue Thickness in Maxillary Anterior Teeth. J Dent (Tehran). 2016;13(5):302\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodrigues DM, Petersen RL, de Moraes JR, Barboza EP. Gingival landmarks and cutting points for gingival phenotype determination: A clinical and tomographic cross-sectional study. J Periodontol. 2022;93(12):1916\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreira Rodrigues D, Avila-Ortiz G, Porto Barboza E, Chambrone L, Fonseca M, Couso-Queiruga E. Relationship Between Gingival Thickness and Other Periodontal Phenotypic Features: A Cross-Sectional Study. Int J Periodontics Restor Dent. 2025;45(5):589\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollins JR, Pannuti CM, Veras K, Ogando G, Brache M. Gingival phenotype and its relationship with different clinical parameters: a study in a Dominican adult sample. Clin Oral Investig. 2021;25(8):4967\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlhajj WA. Gingival phenotypes and their relation to age, gender and other risk factors. BMC Oral Health. 2020;20(1):87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFischer KR, B\u0026uuml;chel J, Kauffmann F, Heumann C, Friedmann A, Schmidlin PR. Gingival phenotype distribution in young Caucasian women and men - An investigative study. Clin Exp Dent Res. 2022;8(1):374\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBednarz-Tumidajewicz M, Sender-Janeczek A, Zborowski J, Gedrange T, Konopka T, Prylińska-Czyżewska A, Dembowska E, Bednarz W. In Vivo Evaluation of Periodontal Phenotypes Using Cone-Beam Computed Tomography, Intraoral Scanning by Computer-Aided Design, and Prosthetic-Driven Implant Planning Technology. Med Sci Monit. 2020;26:e924469.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTolentino ES, Yamashita FC, de Albuquerque S, Walewski LA, Iwaki LCV, Takeshita WM, Silva MC. Reliability and accuracy of linear measurements in cone-beam computed tomography using different software programs and voxel sizes. J Conserv Dent. 2018;21(6):607\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gingival Thickness, Gingival Phenotype, Cone-Beam Computed Tomography, Transgingival Probing, Measurement Agreement","lastPublishedDoi":"10.21203/rs.3.rs-9010834/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9010834/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aimed to evaluate the accuracy and clinical utility of a novel, non-invasive cone-beam computed tomography (CBCT)-based protocol for measuring gingival thickness in the maxillary anterior region, using transgingival probing (TGP) as the reference standard.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this cross-sectional study, 39 periodontally healthy patients (24 males, 15 females; mean age 36\u0026thinsp;\u0026plusmn;\u0026thinsp;12 years) were recruited. Gingival thickness was measured at 2 mm, 4 mm, and 6 mm apical to the gingival zenith on the facial aspect of maxillary anterior teeth (234 teeth, 702 sites) using both TGP and a CBCT protocol involving radiopaque resin markers. Method agreement was analyzed via Bland-Altman plots and Deming regression. Diagnostic performance of CBCT for classifying gingival phenotype was assessed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCBCT measurements showed a strong correlation with TGP (ρ\u0026thinsp;\u0026gt;\u0026thinsp;0.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Deming regression indicated a constant systemic error, with CBCT underestimating thickness by approximately 0.04 mm (95% CI: -0.053 to -0.035), but no proportional error. Despite this minor bias, agreement was excellent (Kappa\u0026thinsp;\u0026gt;\u0026thinsp;0.95). CBCT demonstrated 100% specificity and \u0026gt;\u0026thinsp;96% sensitivity for identifying thick phenotypes. No significant differences in gingival thickness were found between genders.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe novel CBCT-based measurement protocol shows high agreement with gold standard. The identified constant error is clinically negligible, supporting its utility as a reliable, non-invasive method for assessing gingival thickness and phenotype, with potential for integration with digital impression technology.\u003c/p\u003e","manuscriptTitle":"Gingival Phenotype Assessment: A Comparative Study of A Novel CBCT-Based Measurement Technique and Transgingival Probing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 16:52:09","doi":"10.21203/rs.3.rs-9010834/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-28T16:19:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92775753657864319629055560797493484370","date":"2026-04-16T20:55:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271453602900314305993093131505298944296","date":"2026-04-16T15:15:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T17:26:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293080672260270070551626657365034926606","date":"2026-04-12T13:11:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-11T15:04:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76181245991115023890420804507680297518","date":"2026-04-10T20:06:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"72697137269729071386720182414286836977","date":"2026-04-10T08:34:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-03T14:24:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-10T12:13:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-10T10:00:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-10T10:00:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Oral Health","date":"2026-03-02T13:34:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ce1bd065-eb9b-4d1b-96d4-40381f9edfd7","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T16:52:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 16:52:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9010834","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9010834","identity":"rs-9010834","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}