Gingival Recessions
Conditions
Keywords
Gingival Recession, Artificial Intelligence, Image Analysis, Deep Learning, prognostic model
Brief summary
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.
Detailed description
Whether an exposed root surface can be completely covered is the central question in planning mucogingival surgery. The Cairo classification is the current diagnostic standard for that judgement, but assignment of the recession type varies between examiners and prediction of the individual surgical outcome remains largely subjective. In this cohort, consecutive systemically healthy adults with Cairo RT1,RT2 or RT3 gingival recessions are treated by a single operator with a coronally advanced flap combined with a subepithelial connective tissue graft. Recession depth, keratinised tissue width and gingival thickness are recorded at baseline and at three and six months. Standardised intraoral photographs are obtained at each time point under fixed conditions. A deep learning model is developed to predict the six-month outcome from the preoperative photograph together with baseline clinical variables. Model performance is assessed by discrimination, calibration and prediction error, using the clinical measurement at six months as the reference standard. A secondary analysis examines whether the recession type assigned automatically from the photograph agrees with the type assigned by the examining periodontist. The model is developed and validated internally within this cohort; no external validation set is available. Its output is not shown to the operator and does not influence treatment. Reporting follows the TRIPOD recommendations for prediction model studies.
Interventions
A 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.
DIAGNOSTIC_TESTDeep learning based prediction of root coverage outcome
Preoperative 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.
Sponsors
Marmara University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 65 Years
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy of the model in predicting root coverage at six months | 6 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity and specificity of the model at the selected decision threshold | 6 months | 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. |
Countries
Turkey (Türkiye)
Contacts
CONTACTMuhammed F Dogan, Resident
PRINCIPAL_INVESTIGATORLeyla Kuru, Professor
Marmara University Faculty of Dentistry Department of Periodontology
Outcome results
None listed