Predicting case difficulty in endodontic microsurgery using machine learning algorithms

Objectives: The study aimed to develop and validate machine learning models for case difficulty prediction in endodontic microsurgery, assisting clinicians in preoperative analysis.Methods: The cone-beam computed tomographic images were collected from 261 patients with 341 teeth and used for radiographic examination and measurement. Through linear regression (LR), support vector regression (SVR), and extreme gradient boosting (XGBoost) algorithms, four models were established according to different loss functions, including the L1-loss LR model, L2-loss LR model, SVR model and XGBoost model. Five-fold cross -validation was applied in model training and validation. Explained variance score (EVS), coefficient of deter-mination (R2), mean absolute error (MAE), mean squared error (MSE) and median absolute error (MedAE) were calculated to evaluate the prediction performance.Results: The MAE, MSE and MedAE values of the XGBoost model were the lowest, which were 0.1010, 0.0391 and 0.0235, respectively. The EVS and R2 values of the XGBoost model were the highest, which were 0.7885 and 0.7967, respectively. The factors used to predict the case difficulty in endodontic microsurgery were ordered according to their relative importance, including lesion size, the distance between apex and adjacent important anatomical structures, root filling density, root apex diameter, root resorption, tooth type, tooth length, root filling length, root canal curvature and the number of root canals.Conclusions: The XGBoost model outperformed the LR and SVR models on all evaluation metrics, which can assist clinicians in preoperative analysis. The relative feature importance provides a reference to develop the scoring system for case difficulty assessment in endodontic microsurgery. Clinical significance: Preoperative case assessment is a crucial step to identify potential risks and make referral decisions. Machine learning models for case difficulty prediction in endodontic microsurgery can assist clinicians in preoperative analysis efficiently and accurately.

Automated summary

The study aimed to develop and validate machine learning models for predicting case difficulty in endodontic microsurgery, assisting clinicians in preoperative analysis. Four models were established using linear regression, support vector regression, and extreme gradient boosting algorithms. The XGBoost model outperformed the LR and SVR models on all evaluation metrics, providing important references for predicting surgical difficulty. Machine learning models can efficiently and accurately assist clinicians in preoperative analysis in endodontic microsurgery.