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PhacoTrainer:用于白内障手术的自动人工智能生成的性能评级.

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人工智能 (AI) 的指标可以区分实习生和教师白内障外科医生. 这些人工智能生成的外科技能指标与专家评估相关,为外科教育增强提供了潜力.

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科学领域:

  • 眼科医生 眼科 眼科
  • 外科教育的外科教育
  • 人工智能在医学中的应用

背景情况:

  • 评估外科技能对于有效的培训至关重要.
  • 客观指标可以补充外科教育中的主观专家评估.
  • 人工智能 (AI) 为分析外科手术性能提供了新的方法.

研究的目的:

  • 确定人工智能生成的指标是否可以区分实习生和教师白内障外科医生.
  • 评估AI衍生的外科性能指标和专家评级技能之间的相关性.

主要方法:

  • 深度学习模型从常规白内障手术视频中生成了视频级 (例如,探针分散) 和特定仪器 (例如,路径长度) 的指标.
  • 外科医生包括住院医生 (N=28) 和临床医生 (N=29).
  • 专家人类评委使用了白内障外科技能客观结构化评估 (OSACSS) 来进行评估. 统计分析包括t测试和皮尔森相关系数.

主要成果:

  • 人工智能指标,如化探头总路径长度和最大速度,在参加外科医生视频中明显较低.
  • 参与手术的外科医生表现出基于人工智能分析的卓越的发酵和眼睛集中.
  • 大多数人工智能指标与OSACSS分数呈负相关性,这表明人工智能指标值较低时的表现更好 (例如,化分散:r = -0.369).

结论:

  • 自动生成的AI指标有效地区分了当前和实习者白内障手术.
  • 人工智能指标显示,与人类专家对手术绩效的评估有显著的相关性.
  • 人工智能驱动的指标有望提高白内障外科教育和培训.