What question did this study set out to answer?

To develop a model for predicting the dose-dependent release of chlorhexidine from glass ionomer cements over time.

March 26, 2026Dental Materials8 citationsOpen Access

Data-driven modeling of dose-dependent chlorhexidine release from glass ionomer cements using gradient boosting models

MUMert UçankaleMarmara University CCCelal Cakiroglu GBGebrail Bekdaş

Key Points

To develop a model for predicting the dose-dependent release of chlorhexidine from glass ionomer cements over time.
Used extreme gradient boosting (XGBoost) to model chlorhexidine release.
Trained models on data collected over 663 days for multiple dose levels (1%, 2%, 5%, and 10%).
Applied Bayesian hyperparameter optimization to enhance model performance.
Created an online tool for users to predict chlorhexidine release.
Utilized conformal prediction to provide uncertainty intervals for release estimates.
XGBoost models achieved R 2 scores of over 0.96 for cumulative and daily chlorhexidine release predictions.
Predictions for cumulative release were accurate across all dose levels, with R 2 scores exceeding 0.92.
Conformal prediction techniques offered reliable prediction intervals for daily release rates.
An online application was developed that facilitates predictions based on user-selected parameters.

Abstract

Long term prediction of antimicrobial release from bioactive dental materials remains challenging due to complex, nonlinear release kinetics. This study presents the application of the extreme gradient boosting (XGBoost) predictor to model sustained chlorhexidine (CHX) release from glass ionomer cements (GICs), enabling accurate long-term predictions. XGBoost models were trained using a comprehensive dataset of cumulative CHX release from CHX-hexametaphosphate (CHX-HMP) functionalized GICs measured over 663 days across 1%, 2%, 5% and 10% dose levels. Cross-validated models demonstrated accurate prediction of the CHX release within the 663-day observation period. The model performance has been maximized using Bayesian hyperparameter optimization. Closed-form predictive equations have been developed for all doses to forecast the CHX release beyond 663 days. An online graphical user interface has been developed on the Streamlit platform. The optimized XGBoost models demonstrated high predictive accuracy for cumulative CHX release and daily release rates across all dose levels. Exponential forecasting equations achieved R 2 scores greater than 0.92 for all dose levels. The conformal prediction technique provided reliable prediction intervals for the daily CHX release rate. This study presents accessible, and uncertainty-aware models for predicting long term CHX release from antimicrobial GICs. The approach enables reliable forecasting beyond the experimental time window, supporting the design and clinical application of bioactive dental materials. The online tool facilitates practical adoption by researchers and clinicians, addressing the challenge of long-term antimicrobial release prediction in restorative dentistry. • Optimized XGBoost predicts cumulative and daily chlorhexidine (CHX) release with R 2 > 0 . 96 based on CHX content. • Conformal prediction added intervals to point estimates, quantifying uncertainty in predicted release rates. • Equations, calibrated on subsets of the 663‑day data, forecast cumulative CHX release with R 2 > 0.92 for all CHX levels. • An online application provides point and interval CHX release predictions from user-selected parameters.

Demander à l'IA

Bookmark

View Full Paper