Diagnosis of In Vivo Vertical Root Fracture in Endodontically Treated Teeth Using Machine Learning Techniques.

Introduction: This study aimed to diagnose vertical root fracture (VRF) of endodontically treated teeth using clinical features and bone loss information from cone beam computed tomography with machine learning models.

Methods: A total of 887 patients with 941 teeth undergoing endodontic surgery were included in this retrospective study. The clinical factors and bone defects detected via cone beam computed tomography were measured and recorded. Linear machine learning models, logistic regression model and nonlinear models, including XGBoost, LightGBM, and CatBoost were used to diagnose VRF. Model performance was evaluated using 5-fold cross-validation and based on various performance parameters, including the area under the curve, sensitivity, specificity, precision, and F score. Model interpretations were visualized by Shapley Additive Explanations.

Results: Of the 941 teeth, 112 VRF teeth (11.9%) were identified during endodontic surgery or after tooth extraction. XGBoost and LightGBM showed excellent performance with area under the curves of 0.98 [0.96, 0.99], specificity of 0.978 and 0.983, sensitivity of 0.883 and 0.803, and precision of 0.846 and 0.865, respectively. Shapley Additive Explanations values showed that lingual/buccal bone defect, the ratio of bone defect height above the root apex to the defect total height, width of bone defect and age were the top 5 contributors.

Conclusions: Machine learning models for the diagnosis of VRF using age, sex, tooth type, the quality of root canal filling and bone loss position, height, width, and depth are valuable for clinical decision making after root canal treatment.