CClinicalTrials.gg
RecruitingNCT07775365Updated Aug 20, 2026

AI-Based Prediction of Root Coverage Outcome From Intraoral Photographs

An observational study in Gingival Recessions, sponsored by Marmara University. Recruiting at 1 site in Turkey (Türkiye). Open to participants aged 18 Years to 65 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-08-20.

Sponsored by Marmara University · Observational

Study type
Observational
Model
Cohort
Time perspective
Prospective
Enrollment
36
Ages
18 Years to 65 Years
Sex
All
01

Study 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.

Read the 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.

02

Conditions studied

  • Gingival Recessions

Browse trials for

Keywords

  • Gingival Recession
  • Artificial Intelligence
  • Image Analysis
  • Deep Learning
  • prognostic model
03

Who can participate

Ages eligible
18 Years to 65 Years
Sexes eligible
All
Accepts healthy volunteers
Yes
Sampling method
Non-probability sample

Study 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.

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

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.
04

Study design

Observational model
Cohort
Time perspective
Prospective
Enrollment
36 participants (estimated)
Target follow-up
6 Months
Patient registry
Yes

Groups and cohorts

  • Root coverage surgery cohort

    Systemically healthy adults aged 18 to 65 years with Cairo RT1,RT2 pr RT3 gingival recessions, treated with a coronally advanced flap combined with a subepithelial connective tissue graft by a single operator and followed for six months. All participants received the same surgical technique; no comparison group was formed and no participant was assigned to a treatment for the purposes of this study. Standardised intraoral photographs and clinical measurements were obtained before surgery and at three and six months.

    Procedure: Coronally advanced flap with subepithelial connective tissue graft · Diagnostic Test: Deep learning based prediction of root coverage outcome

Interventions

  • ProcedureCoronally advanced flap with subepithelial connective tissue graft

    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.

05

What researchers measure

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 months

Secondary 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 months

Other 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 months

  2. 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: Baseline

06

Study locations

1 of 1 sites recruiting
  • Marmara University Faculty of Dentistry Department of Periodontology
    Istanbul, Istanbul 34854, Turkey (Türkiye)
    Recruiting
07

Registry details

Key details

Study ID
NCT07775365
Lead sponsor
Marmara University
Responsible party
Sponsor
First posted
Aug 20, 2026
Start date
Sep 17, 2025
Primary completion
Sep 17, 2026 (estimated)
Completion
Sep 17, 2027 (estimated)
Last update
Aug 20, 2026

Study contacts

Muhammed F Dogan, Resident
Contact
mfurkandogaan@gmail.com
+905433890065
Leyla Kuru, Professor
principal investigator · Marmara University Faculty of Dentistry Department of Periodontology

Oversight

FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Interested in this study?

Eligibility is decided by the study team. Share this record with your doctor or contact the team directly.

Contact study team

Follow this study

Get an email when the registry record changes — status, dates, results — or when someone posts here.

Sign in to follow

Discussion

Questions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.

Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.

Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.

Start the discussion