Wu Yafei, Jia Maoni, Fang Ya, Duangthip Duangporn, Chu Chun Hung, Gao Sherry Shiqian
School of Public Health, Xiamen University, Xiamen, China.
School of Nursing, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China.
BMC Oral Health. 2025 Mar 15;25(1):389. doi: 10.1186/s12903-025-05768-y.
Early childhood caries (ECC) is a major oral health problem among preschool children that can significantly influence children's quality of life. Machine learning can accurately predict the treatment outcome but its use in ECC management is limited. The aim of this study is to explore the application of machine learning in predicting the treatment outcome of ECC.
This study was a secondary analysis of a recently published clinical trial that recruited 1,070 children aged 3- to 4-year-old with ECC. Machine learning algorithms including Naive Bayes, logistic regression, decision tree, random forest, support vector machine, and extreme gradient boosting were adopted to predict the caries-arresting outcome of ECC at 30-month follow-up after receiving fluoride and silver therapy. Candidate predictors included clinical parameters (caries experience and oral hygiene status), oral health-related behaviours (toothbrushing habits, feeding history and snacking preference) and socioeconomic backgrounds of the children. Model performance was evaluated using discrimination and calibration metrics including accuracy, recall, precision, F1 score, area under the receiver operating characteristic curve (AUROC) and Brier score. Shapley additive explanations were deployed to identify the important predictors.
All machine learning models showed good performance in predicting the treatment outcome of ECC. The accuracy, recall, precision, F1 score, AUROC, and Brier score of the six models ranged from 0.674 to 0.740, 0.731 to 0.809, 0.762 to 0.802, 0.741 to 0.804, 0.771 to 0.859, and 0.134 to 0.227, respectively. The important predictors of the caries-arresting outcome were the surface and tooth location of the carious lesions, newly developed caries during follow-ups, baseline caries experience, whether the children had assisted toothbrushing and oral hygiene status.
Machine learning can provide promising predictions of the treatment outcome of ECC. The identified key predictors would be particularly informative for targeted management of ECC.
幼儿龋（ECC）是学龄前儿童主要的口腔健康问题，会显著影响儿童的生活质量。机器学习能够准确预测治疗结果，但其在ECC管理中的应用有限。本研究旨在探讨机器学习在预测ECC治疗结果中的应用。
本研究是对最近发表的一项临床试验的二次分析，该试验招募了1070名3至4岁患有ECC的儿童。采用包括朴素贝叶斯、逻辑回归、决策树、随机森林、支持向量机和极端梯度提升在内的机器学习算法，预测接受氟化物和银治疗后30个月随访时ECC的龋病停止结果。候选预测因素包括临床参数（龋病经历和口腔卫生状况）、口腔健康相关行为（刷牙习惯、喂养史和零食偏好）以及儿童的社会经济背景。使用包括准确性、召回率、精确率、F1分数、受试者工作特征曲线下面积（AUROC）和布里尔分数在内的判别和校准指标评估模型性能。采用夏普利加性解释来确定重要的预测因素。
所有机器学习模型在预测ECC治疗结果方面均表现良好。六个模型的准确性、召回率、精确率、F1分数、AUROC和布里尔分数分别在0.674至0.740、0.731至0.809、0.762至0.802、0.741至0.804、0.771至0.859和0.134至0.227之间。龋病停止结果的重要预测因素是龋损的表面和牙齿位置、随访期间新出现的龋病、基线龋病经历、儿童是否有助刷牙以及口腔卫生状况。
机器学习能够对ECC的治疗结果提供有前景的预测。所确定的关键预测因素对于ECC的针对性管理将具有特别的指导意义。
