Development of an Artificial Intelligence-Based Clinical Image Model for Detection, Classification, and Management Recommendations of Anterior Gingival Recession
Aperçu de l'étude
Statut
Les conditions
Intervention / Traitement
Description détaillée
This study is designed to develop and validate an artificial intelligence (AI)-based clinical image analysis model for the detection, classification, and management recommendation of anterior gingival recession. Gingival recession is a common periodontal condition characterized by apical displacement of the gingival margin, which may lead to aesthetic concerns, dentinal hypersensitivity, and increased risk of root caries.
Clinical intraoral images of patients presenting with anterior gingival recession will be collected following standardized imaging protocols. The dataset will be used to train, validate, and test a machine learning model capable of identifying the presence of gingival recession and classifying its severity and/or type according to established periodontal classification systems.
The AI model will also be designed to generate preliminary management recommendations based on the detected class, supporting clinical decision-making. Model performance will be evaluated using standard metrics such as accuracy, sensitivity, specificity, precision, recall, and area under the receiver operating characteristic curve (AUC-ROC).
The study is observational in nature with a diagnostic and model-development component. All patient data will be anonymized to ensure confidentiality, and ethical approval will be obtained prior to data collection. The final output is intended to support clinicians in improving diagnostic consistency and treatment planning efficiency for anterior gingival recession.
Type d'étude
Inscription (Réel)
Contacts et emplacements
Lieux d'étude
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Cairo, Egypte
- Faculty of Dental Medicine for Girls, Al-Azhar University
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Critères de participation
Critère d'éligibilité
Âges éligibles pour étudier
- Adulte
- Adulte plus âgé
Accepte les volontaires sains
Méthode d'échantillonnage
Population étudiée
La description
Inclusion Criteria:
- Patients aged 18 years or older
- Presence of at least one anterior tooth exhibiting gingival recession classified according to the Cairo classification system (RT1, RT2, or RT3). - The gingival margin must be clearly visible.
- High-quality images (good focus, lighting, and resolution) are required.
- Clinically visible and intact cementoenamel junction (CEJ).
Exclusion Criteria:
- Presence of cervical restorations or fixed prostheses that interfere with CEJ identification.
- Patients undergoing active orthodontic treatment.
- Pregnant individuals, due to hormonal changes affecting gingival tissues.
- Images with poor photographic quality.
Plan d'étude
Comment l'étude est-elle conçue ?
Détails de conception
Cohortes et interventions
Groupe / Cohorte |
Intervention / Traitement |
|---|---|
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Gingival Recession Patients
This group consists of patients presenting with anterior gingival recession.
Clinical intraoral images will be collected from eligible participants and used for the development and validation of an artificial intelligence-based classification model.
The dataset includes cases with varying degrees and types of gingival recession according to established clinical classification criteria.
No therapeutic intervention will be performed as part of the study, and all images will be analyzed for diagnostic and classification purposes only.
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An artificial intelligence-based clinical image model will be developed and evaluated using standardized clinical photographs of anterior teeth presenting with gingival recession.
The model will be trained to detect the presence of gingival recession, classify lesions according to the Cairo classification system (RT1, RT2, and RT3), and generate preliminary management recommendations based on the identified classification.
The system's performance will be assessed by comparing its diagnostic and classification outputs with expert clinical assessments.
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Que mesure l'étude ?
Principaux critères de jugement
Mesure des résultats |
Description de la mesure |
Délai |
|---|---|---|
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Sensitivity and specificity of the AI system in detecting gingival recession, compared to clinical probing measurements.
Délai: Through study completion, an average of 6 months
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-Primary Outcome 1 Outcome Measure: Sensitivity and specificity of the AI system for detecting gingival recession compared with clinical probing measurements. Primary Outcome 2 Outcome Measure: Agreement between the AI system and expert clinicians in classifying gingival recession according to the Cairo classification, assessed using Cohen's kappa coefficient. |
Through study completion, an average of 6 months
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Mesures de résultats secondaires
Mesure des résultats |
Description de la mesure |
Délai |
|---|---|---|
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- Error in automated CEJ identification, compared to manual annotations.
Délai: Immediately after AI analysis of the clinical images
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Immediately after AI analysis of the clinical images
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Collaborateurs et enquêteurs
Parrainer
Dates d'enregistrement des études
Dates principales de l'étude
Début de l'étude (Réel)
Achèvement primaire (Réel)
Achèvement de l'étude (Réel)
Dates d'inscription aux études
Première soumission
Première soumission répondant aux critères de contrôle qualité
Première publication (Réel)
Mises à jour des dossiers d'étude
Dernière mise à jour publiée (Réel)
Dernière mise à jour soumise répondant aux critères de contrôle qualité
Dernière vérification
Plus d'information
Termes liés à cette étude
Termes MeSH pertinents supplémentaires
Autres numéros d'identification d'étude
- OMPDR-108-1r
Informations sur les médicaments et les dispositifs, documents d'étude
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produit fabriqué et exporté des États-Unis.
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