Predicting root fracture after root canal treatment and crown installation using deep learning

Background/purpose: Vertical root fracture (VRF) is a prevalent reason for tooth extraction following root canal treatment and even after crown placement. Predicting fractures is challenging due to multifactorial nature. The current study aimed to predict the likelihood of fracture following root canal treatment and crown placement by developing a deep learning (DL) model. Materials and methods: DL techniques were employed to analyze a dataset comprising 145 clinical cases consisting of 97 fractured teeth and 48 non-fractured teeth. This dataset spanned a five-year period and encompassed cases involving root canal therapy and crown installation. The analysis identified several root fracture-related parameters, which were incorporated into the DL system. The dataset consisted of 17 features presented in a mixed-type tabular format. Results: The deep neural network (DNN) model surpassed the support vector machine (SVM) model with a higher accuracy (80.7 % vs. 71.7 %) and F1-score value (0.857 vs. 0.817) for predicting root fracture. Furthermore, in determining root fracture occurrence, it was observed that 17 significant characteristics in the DNN model outperformed the 7 features by 11.7 % in accuracy and 10 % in F1-score. Conclusion: DL shows promise in predicting root fracture post root canal therapy and prosthesis, and it may have the potential to aid clinicians in assessing fracture risk and improving decision-making.