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用机器学习预测乳腺重建手术结果:一个系统性审查.

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  • 1From the Department of Plastic Surgery, University of California, Irvine, Orange, CA.

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机器学习 (ML) 模型在预测乳房重建手术的结果方面表现有希望. 预测患者满意度 (BREAST-Q) 和使用类失衡技术的模型显示出更高的准确性,有助于手术规划.

关键词:
人工智能的人工智能是人工智能.乳房重建 乳房重建机器学习是机器学习.预测建模预测建模风险分层的分层化

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科学领域:

  • 整形外科 整形外科 整形外科
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 人工智能 (AI) 和机器学习 (ML) 在整形手术中越来越多地用于预测患者的结果和指导决策.
  • 本综述侧重于ML模型的性能,特别是在乳房重建中.

研究的目的:

  • 系统地审查和评估机器学习 (ML) 预测模型在乳房重建中的性能.
  • 为了比较不同ML模型和结果指标在预测手术结果方面的有效性.

主要方法:

  • 进行了PubMed,Scopus和EMBASE的系统审查.
  • 包括使用ML预测乳房重建结果的研究,报告模型类型和性能指标 (例如,接收器操作特征曲线下的面积).
  • 统计分析包括描述性统计,多变量线性回归和元回归.

主要成果:

  • 分析了14项涉及19个ML模型和11,013名患者的研究.
  • 在所有模型中,接收器操作特征曲线下的中位面积为0.71.
  • 预测BREAST-Q结果的模型和使用阶级不平衡缓解的模型显示歧视明显更高.

结论:

  • 机器学习模型对于预测乳房重建的各种结果是有效的,包括手术并发症和患者满意度.
  • 预测BREAST-Q和采用阶级失衡方法的模型显示出优越的歧视.
  • 标准化报告对于未来在整形外科的ML应用至关重要,以确保可重复性并促进比较.