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

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.