用可解释模型分析儿童龋齿风险，关注社会因素而非仅影像数据。
Explainable Machine Learning for Pediatric Dental Risk Stratification Using Socio-Demographic Determinants
- 基于年龄、收入比等社会人口特征构建可解释预测模型。
- 模型AUC为0.61，虽不高但校准稳健，高风险时保守估计。
- 适合公共卫生筛查与资源公平分配，非临床诊断参考。
儿童口腔疾病是全球最普遍且不平等的慢性健康问题之一。尽管流行病学证据表明口腔健康与社会经济及人口因素密切相关，但当前大多数牙科人工智能应用依赖图像诊断和黑箱预测模型，难以在儿童群体中实现透明与伦理化使用。本研究旨在开发并评估一种以可解释性、校准性和伦理部署为核心的儿科龋齿风险分层机器学习框架，而非追求最高预测精度。模型基于包括年龄、收入-贫困比、种族/族裔、性别和医疗史在内的群体级数据进行训练，通过受试者工作特征曲线（ROC）和校准曲线评估性能。利用SHapley Additive exPlanations（SHAP）实现全局与个体层面的预测解释。结果显示，模型具备适度判别能力（AUC = 0.61），校准较保守，在高风险水平下低估风险。SHAP分析表明，年龄和收入-贫困比是预测风险最强的变量，其次为种族/族裔和性别。结论：可解释机器学习支持透明、以预防为导向的儿童龋齿风险分层，有助于人群筛查与公平资源配置，而非替代临床诊断决策。
原文摘要 · Abstract (English)
Background: Pediatric dental disease remains one of the most prevalent and inequitable chronic health conditions worldwide. Although strong epidemiological evidence links oral health outcomes to socio-economic and demographic determinants, most artificial intelligence (AI) applications in dentistry rely on image-based diagnosis and black-box prediction models, limiting transparency and ethical applicability in pediatric populations. Objective: This study aimed to develop and evaluate an explainable machine learning framework for pediatric dental risk stratification that prioritizes interpretability, calibration, and ethical deployment over maximal predictive accuracy. Methods: A supervised machine learning model was trained using population-level pediatric data including age, income-to-poverty ratio, race/ethnicity, gender, and medical history. Model performance was assessed using receiver operating characteristic (ROC) analysis and calibration curves. Explainability was achieved using SHapley Additive exPlanations (SHAP) to provide global and individual-level interpretation of predictions. Results: The model achieved modest discrimination (AUC = 0.61) with conservative calibration, underestimating risk at higher probability levels. SHAP analysis identified age and income-to-poverty ratio as the strongest contributors to predicted risk, followed by race/ethnicity and gender. Conclusion: Explainable machine learning enables transparent, prevention-oriented pediatric dental risk stratification and supports population screening and equitable resource allocation rather than diagnostic decision-making.
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