CompletedNCT07693322

Development of an Artificial Intelligence-Based Clinical Image Model for Detection, Classification, and Management Recommendations of Anterior Gingival Recession

Sponsor: Al-Azhar University

Trial Details

Condition	Gingival Recessions
Intervention	Artificial Intelligence-Based Clinical Image Analysis Model
Status	Completed
Participants	149
Est. Completion	15 Jan 2026
Last Updated	9 July 2026

Primary Outcome

Sensitivity and specificity of the AI system in detecting gingival recession, compared to clinical probing measurements.

Eligibility Criteria

Inclusion Criteria

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.

Trial Sites

Countries

Egypt

View Registry Entry

Related Analysis

AI-Based Image Analysis Model for Gingival Recession Detection and Classification: What Periodontists and General Dentists Need to Know

A completed diagnostic study from Al-Azhar University has developed and evaluated an AI-based clinical image model for detecting and classifying anterior gingival recession using the Cairo classification system. The 149-participant study assessed the model's sensitivity and specificity against clinical probing measurements, with potential implications for standardising periodontal diagnosis in both specialist and primary care dental settings.

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