预测原始和永久牙的修复失败 - - 一种机器学习方法
Vitor Henrique Digmayer Romero1, Eduardo Trota Chaves1, Shankeeth Vinayahalingam2
1Department of Dentistry, Radboud Research Institute for Medical Innovation, Radboud University Medical Center, Nijmegen, Netherlands; Graduate Program in Dentistry, School of Dentistry, Federal University of Pelotas, Pelotas, Brazil.
Dental materials : official publication of the Academy of Dental Materials
|September 26, 2025
概括
机器学习模型可以预测母牙的后牙修复失败,AUC值为0.67-0.75. 永久牙模型的预测能力较低 (AUC为0.53-0.62),表明需要在临床环境中进一步验证.
科学领域:
- 牙科信息学 牙科信息学
- 机器学习在医疗保健中的应用
- 在牙科中预测建模.
背景情况:
- 机器学习 (ML) 为分析复杂的医疗数据提供了先进的功能.
- 预测模型对于改善牙科患者的治疗结果至关重要.
- 准确预测牙科修复失败对于有效的治疗规划至关重要.
研究的目的:
- 开发和评估机器学习 (ML) 预测模型,用于后牙修复失败.
- 用临床数据集评估母牙和永久牙的模型性能.
- 探索ML在预测恢复寿命方面的实用性.
主要方法:
- 利用了两项针对初级牙 (CARDEC 3) 和永久牙 (CaCIA 试验) 的随机对照试验 (RCT) 的数据.
- 使用决策树,随机森林,XGBoost,CatBoost和神经网络算法开发模型.
- 评估模型性能,使用准确度,精度,回忆,F1得分,ROC AUC和SHAP图表进行解释.
主要成果:
- 原始牙的模型显示了可接受的性能,AUC值在0.67-0.75.5之间.
- 永久牙模型的预测能力较低,AUC值在0.53至0.62.2之间.
- 所有的模型都在精度和牙回收之间取得了平衡的权衡.
结论:
- 机器学习模型可以有效地处理复杂的多维数据,以预测恢复寿命.
- 为了临床实施和提高准确性,需要对更大,更多样化的数据集进行进一步验证.
- 这些模型可能有助于牙医量身定制召回间隔并优化牙科护理分配.
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