AI-Based Prediction of Root Coverage Outcome From Intraoral Photographs
ConditionGingival Recessions
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-65
SponsorMarmara University
About this trial
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.
Eligibility criteria
Qualifiers
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.
Disqualifiers
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.
Trial design
Treatments tested in this trial
- Coronally advanced flap with subepithelial connective tissue graft
- Deep learning based prediction of root coverage outcome
Treatment groups
36 Participants
are divided into 1 treatment groupSponsors and collaborators
Source ClinicalTrials.gov