Design of an Efficient Endodontic File Material Utilizing Artificial Intelligence

Nickel-titanium (NiTi) alloys have led to a revolutionary development in endodontics due to their flexibility and shape-memory properties. However, despite this advancement, one of the most critical issues limiting the clinical success of NiTi rotary instruments is their fracture within the root canal. The most common cause of these fractures is the material's limited resistance to cyclic fatigue. In this study, machine learning was utilized to determine the ideal NiTi composition that would maximize the cyclic fatigue resistance of NiTi rotary instruments. For this purpose, thermal and structural properties were first obtained through SEM-EDX and DSC analyses conducted on various commercial endodontic files, and these features were included as inputs in the model. Feature selection was performed using Pearson Correlation Coefficient (PCC) and Random Forest (RF) importance-based ranking methods. Subsequently, six different regression algorithms (RF, GBDT, SVR, LR, KNN, XGB) were trained, and their performances were evaluated using R², RMSE, and MAE criteria. Preprocessing steps, including outlier detection, standardization, and 10-fold cross-validation, were applied to the dataset. As a result of analyses conducted using a stepwise feature addition strategy, the highest generalization performance was achieved with the K-Nearest Neighbors (KNN) and Support Vector Regression (SVR) models. To evaluate the effect of training/test ratios, various data splitting scenarios were tested, and a 10% testing ratio was determined to be optimal. Using the best-performing model, synthetic NiTi compositions were generated in the range of 48–57 at.% Ni with a resolution of 0.03 at.%. Based on the predictions, the three compositions with the highest NCF values were identified and selected for experimental validation. The results obtained demonstrate the effectiveness of machine learning in material design and contribute to the development of next-generation endodontic instruments.