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Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies01:27

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常规胸部计算机断层扫描中的基于人工智能的肺量化:与螺旋计和视觉肺分级的相关性

Damian Wiedbrauck, Maciej Karczewski1, Stefan O Schoenberg

  • 1Department of Applied Mathematics, Wrocław University of Environmental and Life Sciences, Wroclaw, Poland.

Journal of computer assisted tomography
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此摘要是机器生成的。

基于人工智能 (AI) 的低衰减体积百分比 (LAV%) 与CT扫描中的肺功能 (FEV1/FVC) 和肺重度有很强的相关性. 这种人工智能测量显示了在常规胸部CT成像中评估肺气的前景.

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科学领域:

  • 肺部医学 肺部医学
  • 放射学 放射学是一门学科.
  • 医疗成像中的人工智能

背景情况:

  • 慢性阻塞性肺病 (COPD) 是一种主要的健康问题,其特点是空气流量限制和肺气.
  • 准确评估肺气的严重程度及其与肺功能的相关性对于患者管理至关重要.
  • 常规的胸部计算机断层扫描 (CT) 被广泛使用,但肺气的定量分析正在发展.

研究的目的:

  • 评估AI衍生的低衰减体积百分比 (LAV%) 和螺旋测量 (FEV1/FVC) 之间的相关性.
  • 评估基于AI的LAV%与胸部CT上的视觉确定肺气等级之间的关系.
  • 为了确定最佳的LAV%截止值来预测气流阻塞和中度至重度的肺瘤.

主要方法:

  • 追溯分析298名常规胸部CT和螺旋计的患者.
  • 使用人工智能软件量化LAV% (门<-950 HU).
  • 放射科医生对肺的视觉分级;计算LAV%和FEV1/FVC的斯皮尔曼相关性和ROC曲线.

主要成果:

  • 在LAV%和FEV1/FVC之间发现了显著的负相关性 (ρ = -0.477,P < 0.001).
  • 增加LAV%与更高的视力肺度相关.
  • 确定了最佳的LAV%截止值:预测FEV1/FVC<70%的6.1 (AUC=0.764) 和中度至重度肺瘤的4.7 (AUC=0.802).
  • 基于AI的LAV%在检测空气流阻塞方面表现出比视觉分级 (56.3%) 更高的灵敏度 (77.3%).

结论:

  • 基于AI的LAV%与常规胸部CT中的FEV1/FVC和视觉肺气等级有显著的相关性.
  • 基于人工智能的LAV%测量可以作为评估肺的有价值的功能参数.
  • 建议将基于AI的LAV%整合到常规的胸部CT分析中,以期未来的临床应用.