Department of Oral and Maxillofacial Surgery, Hankook General Hospital, Cheongju, South Korea.
Department of Oral and Maxillofacial Surgery, College of Medicine and Medical Research Institute Chungbuk, National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk, 28644, South Korea.
Sci Rep. 2021 Jul 29;11(1):15447. doi: 10.1038/s41598-021-95024-4.
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.
阻生的下颌第三磨牙（M3M）与下颌第二磨牙（DCM2M）远中龋的发生有关。本研究旨在开发和验证五种机器学习（ML）模型，用于预测由于 M3M 接近而导致 DCM2M 的发生，并确定对临床决策重要的 DCM2M 的预测变量的相对重要性。共分析了 2642 颗与 M3M 相邻的下颌第二磨牙，322 例（12.2%）发现有 DCM2M。使用逻辑回归、随机森林、支持向量机、人工神经网络和极端梯度提升 ML 方法对模型进行训练，然后使用测试数据集进行验证。ML 模型的性能明显优于单一预测因子。机器学习模型的受试者工作特征曲线下面积范围为 0.88 至 0.89。确定了六个特征（性别、年龄、牙骨质牙合面接触点、M3M 的倾斜角度、Winter 分类和 Pell 和 Gregory 分类）作为相关预测因子。这些预测模型可用于检测发生 DCM2M 风险较高的患者，最终有助于预防和治疗阻生 M3M 的龋齿。
