Intraoral Diagnostic Ultrasound to Assess Gingival Thickness Measurement
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».