AI-Based Prediction of Root Coverage Outcome From Intraoral Photographs

This study evaluates whether the outcome of root coverage surgery can be predicted from a preoperative intraoral photograph. Adults with Cairo RT1,RT2 or RT3 gingival recessions treated with a coronally advanced flap and a connective tissue graft are followed for six months. Standardised photographs and clinical measurements are obtained before surgery and at each follow-up visit. A deep learning model is developed to predict the surgical outcome from the preoperative photograph and baseline clinical variables, and its performance is compared with the outcome measured clinically at six months. The model does not influence treatment decisions.

Inclusion Criteria: * Systemically healthy patients (ASA I or II status) with no contraindications for periodontal surgery. * Adult patients aged 18 to 65 years. * Presence of isolated or multiple gingival recessions classified as Cairo RT1, RT2 or RT3 in the maxilla or mandible. * Patients with good oral hygiene standards, defined as a Full Mouth Plaque Score (FMPS) and Full Mouth Bleeding Score (FMBS) of \< 20% at baseline. * Presence of an identifiable Cemento-Enamel Junction (CEJ) (Crucial for AI segmentation). Exclusion Criteria: * Patients with uncontrolled diabetes, immune system disorders, or pregnant/lactating women. * Teeth with cervical restorations or abrasions that obscure the CEJ. * Malpositioned or rotated teeth that would distort the photographic angle for AI analysis.