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
This trial accepts healthy volunteersQualifiers
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 population
The study population consists of systemically healthy adult patients (aged 18-65) presenting to the Department of Periodontology at Marmara University with esthetic concerns or dentin hypersensitivity associated with gingival recession. The cohort includes individuals diagnosed with Cairo Class RT1 or RT2 (Miller Class I or II) gingival recession defects who are scheduled to undergo mucogingival root coverage surgery.
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
Coronally advanced flap with subepithelial connective tissue graft
Procedure/SurgeryA coronally advanced flap is raised over the recession defect and a subepithelial connective tissue graft harvested from the palate is positioned beneath it, after which the flap is sutured coronal to the cemento-enamel junction. Graft thickness, length and width are recorded for each treated site. The procedure was performed as routine clinical care and was not assigned for research purposes.
Deep learning based prediction of root coverage outcome
Diagnostic testPreoperative intraoral photographs and baseline clinical variables are analysed by a deep learning model that predicts the outcome of root coverage surgery. The model output is not used in clinical decision making and does not influence treatment; it is compared retrospectively with the outcome measured by the treating periodontist at six months. The same photographs are also used to assign the recession type automatically, which is compared with the clinical assignment.
Treatment groups
36 Participants
are divided into 1 treatment groupGroup A: Root coverage surgery cohort2 interventions
Trial outcomes
Primary outcomes
1
Accuracy of the model in predicting root coverage at six months
Difference between the root coverage predicted by a model based on preoperative intraoral photographs and baseline clinical characteristics, and the root coverage observed at six months. Root coverage is expressed as the percentage of the baseline recession depth that is covered, calculated as \[(baseline recession depth - six-month recession depth) / baseline recession depth\] × 100, from probing measurements made by the treating periodontist from the cemento-enamel junction to the gingival margin. Predictive accuracy is summarised as the mean absolute error in percentage points across all treated sites.
Time frame
6 monthsSecondary outcomes
1
Sensitivity and specificity of the model at the selected decision threshold
Proportion of sites correctly identified by the model among those that achieved the outcome (sensitivity) and among those that did not (specificity), evaluated at the operating point selected on the receiver operating characteristic curve. Both proportions are reported with 95% confidence intervals. The reference standard is the clinical measurement made at six months by the treating periodontist, using a periodontal probe from the cemento-enamel junction to the gingival margin.
Time frame
6 monthsOther outcomes
1
Calibration of the model
Agreement between the probability predicted by the model and the frequency observed in the cohort, assessed by the calibration slope and intercept and displayed as a calibration plot. Discrimination indicates whether the model ranks sites correctly; calibration indicates whether the predicted probabilities are numerically correct, and the two are reported separately because a model may rank well while producing miscalibrated probabilities. The reference standard is the clinical measurement made at six months.
Time frame
6 months2
Agreement between the model-assigned and the clinician-assigned recession type
Proportion of treated sites at which the recession type assigned by the model from the preoperative photograph matches the type assigned by the examining periodontist, reported together with quadratic weighted kappa. The reference standard is the clinical assignment recorded at baseline according to the criteria of Cairo et al.
Time frame
BaselineSponsors and contacts
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Source ClinicalTrials.gov