Gingival RecessionsNCT07775365
AI-Based Prediction of Root Coverage Outcome From Intraoral Photographs
An observational study of Gingival Recessions, run by Marmara University at 1 site in Turkey (Türkiye).
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 65 yearsHealthy volunteers can take part
- Conditions the study is for
- Gingival Recessions
- What is being tested?
- Coronally advanced flap with subepithelial connective tissue graft and Deep learning based prediction of root coverage outcome
- Type of treatment
- Surgery or procedure
- How many people
- 36 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 17 Sep 2025to 17 Sep 2027
- How long will I take part?
- 6 Months
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 65 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- 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).
Who cannot join
- 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 locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Turkey (Türkiye)1 site, 1 recruiting
- Recruiting
Marmara University Faculty of Dentistry Department of Periodontology
Istanbul, Istanbul, 34854
Muhammed F Dogan, Resident, +905433890065, mfurkandogaan@gmail.com
About this study
Official 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
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.
- Conditions
- Gingival Recessions
- Being tested
- Coronally advanced flap with subepithelial connective tissue graft, Deep learning based prediction of root coverage outcome
- Keywords
- Gingival Recession, Artificial Intelligence, Image Analysis, Deep Learning, prognostic model
- Medical subject headings
- Gingival Recession
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Coronally advanced flap with subepithelial connective tissue graftProcedure or surgery
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 outcomeDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Procedure: Coronally advanced flap with subepithelial connective tissue graft; Diagnostic Test: Deep learning based prediction of root coverage outcome |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Aug 2026
- Study start17 Sep 2025
- Primary completion17 Sep 2026 (estimated)
- Study completion17 Sep 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gingival RecessionsStudies of the same condition, open and under way.
- More trials run by Marmara UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 36 participants (estimated)
- Target duration
- 6 Months
Sponsor and investigators
- Lead sponsor
- Marmara University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Leyla Kuru, Professor, Principal investigator, Marmara University Faculty of Dentistry Department of Periodontology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07775365
- Sponsor's protocol ID
- 2025-09-02/2025-58
- Status last verified
- Aug 2026
- First submitted
- 16 Aug 2026
- Last updated
- 20 Aug 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 20 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official 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
- ClinicalTrials.gov ID
- NCT07775365
- Acronym / sponsor ID
- 2025-09-02/2025-58
- Conditions
- Gingival Recessions
- Keywords
- Gingival Recession, Artificial Intelligence, Image Analysis, Deep Learning, prognostic model
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 36 participants (estimated)
- Lead sponsor
- Marmara UniversityUniversity, hospital or other
- Study start
- 17 Sep 2025 (actual)
- Primary completion
- 17 Sep 2026 (estimated)
- Study completion
- 17 Sep 2027 (estimated)
- First posted
- 20 Aug 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 6 Months
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Root coverage surgery cohort | – | Procedure: Coronally advanced flap with subepithelial connective tissue graft, Diagnostic Test: Deep learning based prediction of root coverage outcomeSystemically 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. |
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (5 listed)
- 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).
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Coronally advanced flap with subepithelial connective tissue graftA 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. | Procedure or surgery | Root coverage surgery cohort |
| Deep learning based prediction of root coverage outcomePreoperative 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. | Diagnostic test | Root coverage surgery cohort |
Primary outcome
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.
1 secondary outcome
Sensitivity and specificity of the model at the selected decision threshold
Time frame: 6 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Aug 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Leyla Kuru, Professor principal investigator
Marmara University Faculty of Dentistry Department of Periodontology
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Gingival Recessions, run by Marmara University at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 20 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Muhammed F Dogan, Resident (+905433890065, mfurkandogaan@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of the model in predicting root coverage at six months (time frame: 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Aug 2026; AzTrials last refreshed its copy on 7 Nov 2026.