Prediction based on machine learning of tooth sensitivity for in-office dental bleaching

Abstract

Objective

To develop a supervised machine learning model to predict the occurrence and intensity of tooth sensitivity (TS) in patients undergoing in-office dental bleaching testing various algorithm models.

Materials and methods

Retrospective data from 458 patients were analyzed, including variables such as the occurrence and intensity of TS, basal tooth color, bleaching material characteristics (concentration and pH), intervention details (number and duration of applications), and patient age. Classification and regression models were evaluated using 5-fold cross-validation and assessed based on various performance parameters.

Results

For the predictive classification task (occurrence of TS), the developed models achieved a maximum area under the receiver operating characteristic curve (AUC) of 0.76 [0.62–0.88] on the test data, with an F1-score of 0.80 [0.71–0.87]. In cross-validation, the highest AUC reached 0.86 [0.84–0.88], and the highest F1-score was 0.78 [0.75–0.83]. For predicting TS intensity, the regression models demonstrated a minimum mean absolute error (MAE) of 1.76 [1.45–2.06] and a root mean square error (RMSE) of 2.38 [2.06–2.69] on the test set. During cross-validation, the lowest MAE was 1.84 [1.67–2.03], with an RMSE of 2.39 [2.20–2.58].

Conclusions

The supervised machine learning model for estimating the occurrence and intensity of TS in patients undergoing in-office bleaching demonstrated good predictive power. The Gradient Boosting Classifier and Support Vector Machine Regressor algorithms stood out as having the greatest predictive power among those tested.

Clinical relevance

These models can serve as valuable tools for anticipating tooth sensitivity in this patient population, facilitating better post-treatment management and control.