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
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.
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mfurkandogaan@gmail.com+905433890065
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.
mfurkandogaan@gmail.com+905433890065