Development and Validation of a Machine Learning-Based Model to Predict Peri-Implant Mucositis in Patients With Dental Implant Placement After 1-Year.

OBJECTIVES

To develop and validate an interpretable machine learning (ML) model for early prediction of peri-implant mucositis (PIM).

MATERIAL AND

METHODS

This retrospective study enrolled patients receiving dental implants between October 2011 to June 2023 in our cohort to develop a ML-based model. External testing was subsequently performed utilizing datasets from two other hospitals. The least absolute shrinkage and selection operator (LASSO) was utilized for feature selection. Six ML algorithms were applied to analyze and identify the optimal model. Shapley Additive exPlanations (SHAP) interpretation was developed for model interpretation and personalized risk assessment.

RESULTS

A total of 341 patients with 605 implants were included in our cohort and 101 patients with 170 implants were included in the external testing cohort. The Area Under Curves (AUCs) and Brier scores of the Support Vector Machine (SVM) model with 10 features were 0.902 and 0.129 in the validation set, while 0.797 and 0.256 in the external testing cohort, demonstrating superior discrimination and calibration compared to other algorithms. The SHAP algorithm interpreted that inadequate keratinized mucosa width, poor oral hygiene, restoration type (splinted crown/bridge), free-end edentulism, gingivitis, smoking habit, thin gingival biotype, and cement-retained restoration would elevate the occurrence of PIM. On the contrary, use of interproximal brushing/flossing and use of oral irrigator served as protective factors.

CONCLUSION

This study developed the first interpretable ML-based tool for predicting PIM in patients with dental implant placement after 1-year, with the SVM model showing high discriminative ability and clinical utility for risk stratification.