1. Machine learning to predict distal caries in mandibular second molars associated with impacted third molars
- Author
-
Sung-Hwi Hur, Minkyung Kim, Jae Seok Lim, Eun-Young Lee, Somi Kim, and Ji-Yeon Kang
- Subjects
Adult ,Male ,0301 basic medicine ,Molar ,Dental Caries Susceptibility ,Science ,Clinical Decision-Making ,Mandible ,Dental Caries ,Machine learning ,computer.software_genre ,Logistic regression ,Sensitivity and Specificity ,Tooth Cervix ,Article ,Machine Learning ,Mandibular second molar ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,Humans ,Medicine ,Retrospective Studies ,Multidisciplinary ,Receiver operating characteristic ,Artificial neural network ,business.industry ,Tooth, Impacted ,030206 dentistry ,Third molar removal ,Data Accuracy ,Random forest ,Support vector machine ,Cementoenamel junction ,Cross-Sectional Studies ,030104 developmental biology ,Risk factors ,Female ,Molar, Third ,Artificial intelligence ,business ,computer - Abstract
Impacted mandibular third molars (M3M) are associated with the occurrence of distal caries on the adjacent mandibular second molars (DCM2M). In this study, we aimed to develop and validate five machine learning (ML) models designed to predict the occurrence of DCM2Ms due to the proximity with M3Ms and determine the relative importance of predictive variables for DCM2Ms that are important for clinical decision making. A total of 2642 mandibular second molars adjacent to M3Ms were analyzed and DCM2Ms were identified in 322 cases (12.2%). The models were trained using logistic regression, random forest, support vector machine, artificial neural network, and extreme gradient boosting ML methods and were subsequently validated using testing datasets. The performance of the ML models was significantly superior to that of single predictors. The area under the receiver operating characteristic curve of the machine learning models ranged from 0.88 to 0.89. Six features (sex, age, contact point at the cementoenamel junction, angulation of M3Ms, Winter's classification, and Pell and Gregory classification) were identified as relevant predictors. These prediction models could be used to detect patients at a high risk of developing DCM2M and ultimately contribute to caries prevention and treatment decision-making for impacted M3Ms.
- Published
- 2021