Machine Learning for Root Fracture Diagnosis
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11m
This paper details research exploring the use of machine learning models to improve the diagnosis of vertical root fractures (VRFs) in teeth that have undergone endodontic treatment. Researchers analyzed clinical data and bone loss characteristics from Cone Beam Computed Tomography (CBCT) scans of over 900 teeth. They compared linear and non-linear machine learning approaches, finding that nonlinear models, particularly XGBoost and LightGBM, demonstrated high accuracy and predictive power in identifying VRFs. The study highlights that factors like the shape and location of bone defects, tooth type, age, and the quality of root canal fillings are significant predictors of VRF.
1.Ran, S. et al. Diagnosis of in vivo vertical root fracture in endodontically treated teeth using machine learning techniques. J. Endod. (2025) doi:10.1016/j.joen.2025.05.004.
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