牙内科治疗牙的恢复寿命:来自随机临床试验的机器学习生存分析
Luiz Alexandre Chisini1,2, Maximiliano Sergio Cenci2, Jovito Adiel Skupien3
1Graduate Program in Dentistry, Federal University of Pelotas, Pelotas, RS, Brazil.
International endodontic journal
|October 16, 2025
概括
机器学习模型准确地预测了内牙治疗牙 (ETT) 修复的寿命. 渐变增强生存显示了生存率的最佳表现,而随机生存森林在预测成功率方面表现出色.
科学领域:
- 牙科研究 牙科研究
- 生物统计学 生物统计学
- 机器学习在医疗保健中的应用
背景情况:
- 经过内治疗的牙 (ETT) 修复需要准确的预后模型.
- 估计ETT恢复的寿命对于患者的治疗结果和治疗计划至关重要.
研究的目的:
- 开发和评估机器学习 (ML) 存活模型,用于预测ETT.恢复的成功率和存活率.
- 确定ETT中恢复寿命的关键预测因素.
主要方法:
- 来自四项受控临床试验的综合数据 (424名患者,618次恢复,长达17年的随访).
- 评估梯度增强生存,随机生存森林和生存支持矢量机器模型.
- 使用 10 倍交叉验证和 hyperopt 进行超参数调整. 使用AUC,C指数,IPCW C指数和Brier分数来评估性能.
主要成果:
- 渐变增强生存模型在预测生存率方面表现优异 (AUC=0.83).
- 随机生存森林模型显示,成功率预测的准确性更高 (AUC=0.73).
- 患者的年龄,牙类型和牙医经验被确定为重要的预测因素. 公平性分析表明,性别和国家之间存在绩效差异.
结论:
- 机器学习模型对ETT恢复寿命的预测性能很高,特别是对于生存率.
- ML提供了一个有前途的框架,用于数据驱动的评估成功和存活在内牙治疗的结果.
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