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

This study aims to develop and evaluate an artificial intelligence-based clinical image model for the detection, classification, and management recommendations of anterior gingival recession. The study will utilize clinical images of patients presenting with gingival recession to train and validate a machine learning model capable of accurately identifying and classifying the condition according to established clinical criteria. In addition, the model will provide preliminary treatment recommendations based on the severity and type of recession. This is a diagnostic and model-development study designed to support clinicians in improving the accuracy and consistency of diagnosis and treatment planning for gingival recession in the anterior region.

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.