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The formation of teeth, also known as odontogenesis, is a complex process that begins in utero, around the sixth week of embryonic development. There are three stages to this process: the bud stage, the cap stage, and the bell stage.
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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相关实验视频

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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
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PubMed
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.

关键词:
临床诊断 临床诊断 临床诊断牙腐烂是指牙的腐烂.机器学习 机器学习永久性牙科修复 永久性牙科修复

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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.