Kug Jin Jeon

Important feature selection and age classification model development Random forest (RF) was used for radiomics feature selection and age classification model. RF is one of the machine learning methods for discrete variables and is an ensemble classifier that produces multiple decision trees using a randomly selected subset of training samples and variables [18]. It calculates the importance of input variables based on Gini impurity for feature selection [19].

Abstract This study investigated whether metal artifact reduction (MAR) applied computed tomography (CT) scans could be used to generate precise digital models and explored possible correlations between the amount of metal artifact and model accuracy. Thirty maxillofacial CT scans were randomly selected and a MAR algorithm was applied. By subtracting the original and MAR-applied CT images, the amount of metal artifact was quantified.

Abstract The goal of this study was to develop a deep learning-based algorithm to predict temporomandibular joint (TMJ) disc perforation based on the findings of magnetic resonance imaging (MRI) and to validate its performance through comparison with previously reported results. The study objects were obtained by reviewing medical records from January 2005 to June 2018.