Intraoral Diagnostic Ultrasound to Assess Gingival Thickness Measurement
Bibliographic record
Abstract
Abstract Background: Assessment of periodontal parameters such as gingival thickness is relevant during diagnosis and treatment planning in all disciplines of dentistry. Thin and thick gingival thickness respond differently to inflammation, restorative trauma, and surgical insult. Current methods of evaluating gingival thickness include transgingival probing (TGP), visual assessment (VA), and probe visibility (PV) methods. Ultrasound (US) has been shown to be an accurate tool to assess periodontium, including gingival thickness. Evidence comparing the diagnostic performance of ultrasound against conventional methods for accurate classification of periodontal gingival thickness remains limited. Objectives: The primary objective of our study was to assess the performance of US gingival thickness measurements compared to transgingival probing as the gold standard. The secondary objective was to assess US gingival thickness measurements compared to probe visibility and visual assessment methods. Materials and Methods: This prospective diagnostic accuracy study included adult patients from the Graduate Periodontics clinic at the Oral Health Clinic, Mike Petryk School of Dentistry, University of Alberta. Maxillary central incisors of these patients were considered for the study. The US imaging was performed using a 20MHz intraoral transducer, and the gingival thickness was measured from three high-quality B-mode images, and the average was recorded. TGP was performed using a #8 endodontic file with a stopper under topical anesthesia, with thickness measured by digital callipers. VA was performed with the patient sitting in the upright position. This was followed by PV assessment using Colorvue gingival probe) (HuFriedy, Chicago, Illinois). Triplicate measurements were obtained for TGP and US. Diagnostic accuracy, agreement, and reproducibility were evaluated using Intraclass Correlation Coefficient (ICC), Bland-Altman analysis, non-inferiority testing, and Fisher’s Exact Test. To assess the performance of VA and PV, we compared them to the gingival thickness category of their numerical counterparts (TGP and US). Sensitivity and specificity were calculated using a 1.46 mm threshold to define thin gingiva. Results: Of the 34 participants recruited, 31 completed all assessments. TGP and US classified the majority of cases as having thin gingiva (<1.46mm). US demonstrated excellent agreement with TGP when triplicate measurements were averaged (ICC(3,k) =0.91). Non-inferiority testing confirmed that US was not inferior to TGP (p = 0.0448), and Bland-Altman analysis showed a slight underestimation by US (mean difference = -0.0663mm, p = 0.246, Student’s t-test). Fisher’s Exact test revealed no significant association between VA, PV-white, PV-green or PV-blue (p = 1.00), while PV-none showed a significant association (p = 0.0065). VA and PV methods showed poor sensitivity in identifying thin gingiva (10.3-10.7% and 7.1%-89.3% respectively). US demonstrated the highest sensitivity for detecting thin gingiva (96.4%). VA and PV-white showed 100% specificity, while PV-green showed 33.3-66.7%, PV-blue showed 0% and US showed 66.6% specificity at detecting thick gingiva. Conclusion: Within the limits of our study, we can say that the US method of measuring gingival thickness is comparable to the gold standard (TGP). US performs superiorly to VA and PV, making it a potential non-ionizing, real-time tool to assess gingival thickness in patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".