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科学领域:

  • 医疗成像医学成像
  • 医疗保健中的人工智能
  • 放射学 放射学是一门学科.

背景情况:

  • 在CT扫描中,以患者为中心对于最佳的图像质量和诊断准确性至关重要.
  • 手动评估患者中心化可能是耗时和主观的.
  • 在多中心研究中,对评估图像细分和集中的自动化客观方法的需求正在增加.

研究的目的:

  • 开发和验证开放式人工智能 (AI) 算法,用于评估CT检查中的图像细分和患者集中.
  • 评估人工智能算法在多个身体区域 (头部,胸部,腹部和骨盆),患者人口统计和扫描仪类型中的性能.
  • 为了比较人工智能算法的中心测量与手动估计.

主要方法:

  • 开发了一个开放式的AI算法 (AIc) 用于图像细分和患者中心分析.
  • 分析了来自275名患者的825张CT扫描 (头部,胸部,腹部和骨盆).
  • 从AIc获得的垂直和水平中心测量与手动测量进行了比较.

主要成果:

  • 对于垂直 (r=0.93-0.95) 和水平 (r=0.80-0.85) 的偏离中心,AIc与手工估计有很强的相关性.
  • 在五个机构的不同年龄组,性别和多个扫描仪中观察到高性能.
  • AIc在接收器操作特征曲线下的面积达到0.72到0.99的范围,以区分中心和离中心的扫描.

结论:

  • 开发的AI算法有效地评估了CT检查中的垂直和水平患者脱中心.
  • PET/CT扫描经常显示出显著的偏离中心,特别是超过30毫米的垂直偏差.
  • AIc提供了一种可靠的,自动化的工具来评估患者的中心位置,在垂直中心位置的评估中表现稍好一些.