一个可解释的可预测模型的直接肉质封闭在有病的成熟永久牙
Yunzi Long1, Xiaowei Xu2, Jiaqi Chen3
1Department of Cariology and Endodontology, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials, Beijing 100081, PR China; Department of General Dentistry II, Peking University School and Hospital of Stomatology, Beijing 100081, PR China.
Journal of dentistry
|August 2, 2024
概括
本研究引入了一种可解释的机器学习模型,以预测永久牙的直接肉质封闭 (DPC) 治疗成功. XGBoost模型准确预测治疗结果,帮助个性化牙科护理.
科学领域:
- 牙科 牙科是指牙科的专业.
- 机器学习 机器学习
- 生物医学信息学 生物医学信息学
背景情况:
- 直接肉质封闭 (DPC) 是对牙成熟永久牙的一种保守治疗方法.
- 预测DPC的成功对于患者的治疗结果和治疗计划至关重要.
- 目前的预测方法缺乏个性化和可解释性.
研究的目的:
- 开发和验证一种可解释的机器学习 (ML) 模型,用于预测个人化DPC治疗的成功概率.
- 通过可解释的AI提高临床决策在内牙科.
主要方法:
- 分析了372名接受DPC的患者的393颗牙的回顾性队列.
- 训练和验证了六个ML模型,使用Shapley添加式解释 (SHAP) 评估特征重要性.
- 性能最好的XGBoost模型得到了改进,并转化为一个Web应用程序.
主要成果:
- 在1年的随访期间,DPC治疗失败率为9.67%.
- XGBoost模型实现了0.86的曲线下的面积 (AUC),用于1年的成功预测.
- 开发了一个可解释的11个特征的XGBoost模型,优先考虑关键的预测因素.
结论:
- 开发的XGBoost模型提供了一个用户友好的工具,用于预测个性化的DPC成功概率.
- 可解释的人工智能 (SHAP) 提供透明的见解,支持改善临床决策和患者咨询.
- 这种方法通过整合人口和临床数据来增强个性化牙科护理.
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