Skip to main content
OpenTrials
Recruiting

NCT Number: NCT07775365

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.

Recruiting

Interested in participating?

Request Info

Key information

Conditions

Gingival Recessions Gingival Diseases Gingival Recession Mouth Diseases Periodontal Atrophy Periodontal Diseases Stomatognathic Diseases

Age range

18 year–65 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Marmara University Faculty of Dentistry Department of Periodontology

Istanbul, 34854, Turkey (Türkiye)

Location status: Recruiting

Location contact

Muhammed F Dogan, Resident

CONTACT

[email protected]

+905433890065

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Coronally advanced flap with subepithelial connective tissue graft

Procedure

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.

Deep learning based prediction of root coverage outcome

Diagnostic Test

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.

Primary outcomes

  1. Accuracy of the model in predicting root coverage at six months

    Time frame: 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 outcomes

  1. Sensitivity and specificity of the model at the selected decision threshold

    Time frame: 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.

Other outcomes

  1. Calibration of the model

    Time frame: 6 months

    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.

  2. Agreement between the model-assigned and the clinician-assigned recession type

    Time frame: Baseline

    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.

Study contacts

Contact information is provided by the study sponsor or research team.

Muhammed F Dogan, Resident

CONTACT

[email protected]

+905433890065

Sponsors and collaborators

Lead sponsor

Marmara University

Other

Registry information

Official study title

Development and Internal Validation of a Deep Learning Model Predicting the Outcome of Root Coverage Surgery From Preoperative Intraoral Photographs: A Prospective Observational Cohort Study

Important dates

Study start
2025
Primary completion
2026
Study completion
2027
First posted
Aug 20, 2026
Registry last updated
Aug 20, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Related clinical trials

Published trials that share one or more normalized conditions with this study.