Dataset Information

Machine learning to predict distal caries in mandibular second molars associated with impacted third molars.

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.

SUBMITTER: Hur SH

PROVIDER: S-EPMC8322059 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundVarious injection methods have been used to improve the success rate of inferior alveolar nerve (IAN) block and reduce the pain and complications. But these methods also have their advantages and disadvantages. A modified 3-distal site anesthesia injection method proposed in 2015 was used clinically with satisfactory results at some dental clinics in China. This study aimed to determine the feasibility of this modified 3-distal site anesthesia injection method for extraction of an impacted mandibular third molar from an anatomical point of view.MethodsThree-dimensional (3D) reconstruction and analysis of the mandibles was performed using cone-beam computed tomography (CBCT) scanning to measure the simulated needle insertion depth, infiltration distance, and other important parameters. These parameters were compared with an actual depth of 20 mm and a theoretic infiltration distance of 10 mm. The main parameters were compared between males and females.ResultsSixty-three CBCT datasets were imported into Simplant OMS software for 3D reconstruction. The left simulated insertion depth did not differ from 20 mm, while right insertion depth was >20 mm (P<0.05). The bilateral infiltration distances were <10 mm (P<0.05) and articaine blocked the IAN. The widths of the mandibular rami were greater than the syringe length (21 mm), thus it was not possible for the syringe to reach the distal edge of the mandible. There was no difference in the simulated needle insertion depth and infiltration distance between the left and right (P>0.05). The bilateral simulated insertion depths, infiltration distances, widths of the mandibular rami, and height of the left mandibular foramen in females were less than in males (P<0.05), while there was no difference in the height of the right mandibular foramen and bilateral insertion angles (P>0.05).ConclusionsThe modified 3-distal site anesthesia injection method was shown to block the IAN based on anatomic evidence derived from 3D analysis on the measurements by CBCT.

Dataset Information

Machine learning to predict distal caries in mandibular second molars associated with impacted third molars.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets