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一种以数据为导向的方法,用于评估外科医生在第三骨提取过程中的特定难度.

Chun Kang1,2, Ziyu Yan3, Xiya Xiong1,2

  • 1School of Artificial Intelligence, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China.

Frontiers in medicine
|November 24, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种数据驱动的方法,用于评估初级医生受影响的智慧牙提取困难. 该模型确定了关键的困难因素和学习曲线,改善了外科培训和评估.

关键词:
数据脱的数据脱.难度评估难度评估的困难受到冲击的下第三.机器学习是机器学习.拔牙拔牙的方法 拔牙拔牙的方法

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科学领域:

  • 口腔和牙面部外科手术
  • 医疗教育 技术 技术 医学教育
  • 在医疗保健中的数据科学.

背景情况:

  • 评估外科手术困难在冲击智慧牙的提取对于培训初级医生至关重要.
  • 现有的难度表可能无法完全捕捉程序复杂性的细微差别.
  • 对手术难度的客观分析可以提高外科手术培训的有效性.

研究的目的:

  • 开发一种数据驱动的方法来分析影响下智牙提取难度的时间和因素.
  • 建立程序难度的数学模型并评估现有的尺度.
  • 为初级医生提供冲击牙提取培训的难度指标.

主要方法:

  • 收集了9名住院医生从419次下部冲击性智慧牙提取中进行的手术记录.
  • 提出了一种使用拉索回归的数据驱动的外科医生特定难度评估 (DDSS) 方法.
  • 将医生分为培训等级,并使用预先训练的模型进行有针对性的难度预测.

主要成果:

  • 该DDSS方法实现了80%的准确性和0.85AUC与支持矢量机器 (SVM).
  • 没有经验的外科医生受到更多因素的影响,而经验丰富的外科医生则专注于四个关键因素:冠状阻力,冲击类型,口腔开口和性别.
  • 学习曲线表明,外科手术能力通常在经过8个月的实践后才能达到.

结论:

  • 一个数据驱动的脱预测模型增强了牙科手术难度评估.
  • 拟议的方法为手术难度评估和外科医生培训提供了新的视角.
  • 该研究为新手外科医生提供了可靠的结论和学习曲线.