RecruitingNCT07775365

AI-Based Prediction of Root Coverage Outcome From Intraoral Photographs

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


Sponsor

Marmara University

Enrollment

36 participants

Start Date

Sep 17, 2025

Study Type

OBSERVATIONAL

Conditions

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.


Eligibility

Min Age: 18 YearsMax Age: 65 Years

Plain Language Summary

Simplified for easier understanding

This clinical trial is studying a procedure called Coronally advanced flap with subepithelial connective tissue graft and Deep learning based prediction of root coverage outcome for people with gingival recessions. The study is currently recruiting participants at 1 location. People eligible for this study include aged 18 Years to 65 Years.

This summary was AI-generated to explain the trial in plain language. It is not medical advice. Always discuss eligibility with your doctor before enrolling in a clinical trial.

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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.


Locations(1)

Marmara University Faculty of Dentistry Department of Periodontology

Istanbul, Istanbul, Turkey (Türkiye)

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NCT07775365


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