Comparison of Artificial Intelligence and Clinicians With Different Experience Levels in Assessing Gingival Phenotype
The goal of this observational study is to compare the performance of clinicians with different experience levels and a deep learning-based artificial intelligence (AI) model in assessing gingival phenotype using two diagnostic methods: the periodontal probe transparency method and visual assessment from standardized clinical photographs. The main questions the study aims to answer are:
Can AI achieve comparable accuracy to human examiners in both probe transparency and visual assessment methods?
Does examiner experience level influence diagnostic performance and agreement with the reference standard in these methods?
Researchers will compare AI, dental students, and periodontology research assistants to determine accuracy, sensitivity, specificity, and agreement with the gold standard for each method.
Participants will:
Undergo standardized intraoral photography of maxillary anterior teeth, with and without a periodontal probe in place, following a validated protocol.
Have gingival phenotype determined by a reference periodontologist using the probe transparency method as the gold standard.
Have their photographs evaluated by AI, dental students, and research assistants for phenotype classification using both methods.
Aperçu de l'étude
Statut
Les conditions
Description détaillée
Gingival phenotype, representing the thickness and morphological characteristics of the gingival soft tissues, plays a critical role in periodontal health, treatment planning, and the long-term stability of clinical outcomes. A thin phenotype is associated with increased risk of gingival recession, papilla loss, and inflammatory complications, while a thick phenotype offers better soft tissue stability but may mask inflammation. Accurate and reproducible assessment of gingival phenotype is therefore essential in clinical dentistry.
The periodontal probe transparency method is considered the gold standard for phenotype assessment due to its simplicity and non-invasiveness. In this method, a periodontal probe is inserted into the sulcus from the buccal aspect, and if the probe is visible through the gingival tissue, the phenotype is classified as thin; if not visible, it is classified as thick. However, the method is susceptible to variability depending on examiner experience, lighting conditions, and subjective interpretation.
Visual assessment, which relies solely on the inspection of gingival and tooth morphology in photographs without a probe, offers a non-contact alternative but is similarly subject to examiner-related variability. These limitations highlight the need for standardized and objective approaches to phenotype determination.
Artificial intelligence (AI), particularly deep learning-based image analysis, has shown promising results in dental diagnostics, enabling automated classification of clinical images with high accuracy and reproducibility. In periodontal research, AI has been applied for lesion detection and radiographic interpretation, but its application in gingival phenotype assessment-especially using the probe transparency method and visual assessment-remains unexplored.
This observational study aims to compare the diagnostic performance of a deep learning-based AI model with human examiners of different experience levels (periodontology residents vs. dental students) in assessing gingival phenotype from standardized intraoral photographs using both the periodontal probe transparency method and visual assessment. The reference standard will be the classification provided by an experienced periodontologist using the probe transparency method in a clinical setting.
The study will evaluate and compare accuracy, sensitivity, specificity, and inter-/intra-examiner agreement across examiner groups and the AI model. The findings are expected to provide insights into the potential of AI as a standardizing tool, reducing inter-examiner variability and supporting clinical decision-making, particularly for less experienced clinicians. Additionally, the study may inform the integration of AI-assisted diagnostic tools in dental education and practice, improving training efficiency and clinical outcomes.
Type d'étude
Inscription (Estimé)
Contacts et emplacements
Coordonnées de l'étude
- Nom: Sude Yıldırım Bolat, DDS
- Numéro de téléphone: +905378947645
- E-mail: sugde.sude@gmail.com
Critères de participation
Critère d'éligibilité
Âges éligibles pour étudier
- Adulte
- Adulte plus âgé
Accepte les volontaires sains
Méthode d'échantillonnage
Population étudiée
The study population will consist of systemically and periodontally healthy adults attending the Department of Periodontology at Ondokuz Mayıs University, Faculty of Dentistry, for routine dental care or check-up. Eligible participants will have natural maxillary anterior incisors and meet all inclusion criteria.
Additionally, the examiner population will include:
Periodontology research assistants currently working in the department.
Fourth- and fifth-year dental intern students who have completed the periodontology clinical rotation.
La description
Inclusion Criteria for Volunteer Participants Who Will Participate in Transparency and Visual Assessment:
- Systemically and periodontally healthy individuals.
- Presence of natural maxillary anterior incisors.
Exclusion Criteria:
- Presence of fixed crowns or cervical restorations on the evaluated teeth.
- Pregnant or breastfeeding women.
- Signs of gingival inflammation or periodontal disease with attachment loss.
- Presence of buccal gingival recession.
- Use of medications known to cause gingival enlargement.
- Presence of congenital anomalies or dental structural defects.
Inclusion Criteria for Clinicians:
- Research assistants: Must be currently working in the Department of Periodontology.
- Dental Intern Students: Fourth- or fifth-year students who have completed periodontology clinical rotation.
Exclusion Criteria for Clinicians:
- Those who are confirmed to be color blind by the Ishihara test
Plan d'étude
Comment l'étude est-elle conçue ?
Détails de conception
Cohortes et interventions
Groupe / Cohorte |
Intervention / Traitement |
|---|---|
|
Dental Students
Fourth- and fifth-year dental intern students will assess standardized intraoral photographs using both the periodontal probe transparency method and visual assessment to classify gingival phenotype.
|
Standardized intraoral photography of the maxillary anterior teeth with a periodontal probe placed according to the transparency method protocol to determine probe visibility status.
Standardized intraoral photography of the maxillary anterior teeth without a periodontal probe, evaluated for gingival phenotype classification based on morphological features.
|
|
Artificial Intelligence Model
A deep learning-based image classification model will analyze standardized intraoral photographs, detecting probe visibility and classifying gingival phenotype according to the periodontal probe transparency method and visual assessment criteria.
|
Standardized intraoral photography of the maxillary anterior teeth with a periodontal probe placed according to the transparency method protocol to determine probe visibility status.
Standardized intraoral photography of the maxillary anterior teeth without a periodontal probe, evaluated for gingival phenotype classification based on morphological features.
A deep learning image classification algorithm trained to assess probe visibility and gingival phenotype from standardized intraoral photographs.
|
|
Periodontology Research Assistants
Research assistants in periodontology will assess standardized intraoral photographs using both the periodontal probe transparency method and visual assessment to classify gingival phenotype.
|
Standardized intraoral photography of the maxillary anterior teeth with a periodontal probe placed according to the transparency method protocol to determine probe visibility status.
Standardized intraoral photography of the maxillary anterior teeth without a periodontal probe, evaluated for gingival phenotype classification based on morphological features.
|
Que mesure l'étude ?
Principaux critères de jugement
Mesure des résultats |
Description de la mesure |
Délai |
|---|---|---|
|
Diagnostic Accuracy of Each Examiner Group and AI Model in the Periodontal Probe Transparency Method
Délai: At the time of image evaluation (single session).
|
Accuracy in determining probe visibility (visible vs. not visible) compared to the gold standard classification by an experienced periodontologist. Measure Type: Proportion (%). Analysis: Accuracy, sensitivity, specificity, and Cohen's kappa coefficient will be calculated. |
At the time of image evaluation (single session).
|
Mesures de résultats secondaires
Mesure des résultats |
Description de la mesure |
Délai |
|---|---|---|
|
Diagnostic Accuracy of Each Examiner Group and AI Model in Visual Assessment Method
Délai: At the time of image evaluation (single session).
|
Accuracy in classifying gingival phenotype (thin vs. thick) without probe, compared to the gold standard classification. Measure Type: Proportion (%). |
At the time of image evaluation (single session).
|
|
Agreement Between Examiner Groups and AI Model
Délai: At the time of image evaluation and at 2-week retest (for a random subset of evaluators).
|
Inter-examiner and intra-examiner agreement for each method, evaluated using Cohen's kappa coefficient and intraclass correlation coefficient (ICC).
|
At the time of image evaluation and at 2-week retest (for a random subset of evaluators).
|
|
Effect of Examiner Experience Level on Diagnostic Performance
Délai: At the time of image evaluation (single session).
|
Comparison of accuracy and agreement between research assistants and dental intern students for each method. Proportion (%), agreement statistic. |
At the time of image evaluation (single session).
|
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Autres numéros d'identification d'étude
- OMUKAEK NO:225/335
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- CIF
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