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开源人工智能模型用于从胸部X射线图中预测COPD呼吸道死亡率.

Jong Hyuk Lee1, Chang-Hoon Lee2, Jayoun Kim3

  • 1Department of Radiology, Seoul National University Hospital, Seoul, Korea.

Radiology. Cardiothoracic imaging
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概括
此摘要是机器生成的。

人工智能 (AI) 来自胸部放射图 (CXR-肺风险) 的得分有效预测慢性阻塞性肺病 (COPD) 患者的呼吸道死亡率. 与现有的分级系统相比,这种AI工具提供了更好的预测.

关键词:
人工智能的人工智能胸部X射线图 胸部X射线图慢性阻塞性肺病 慢性阻塞性肺病预测 预测 预测 预测

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科学领域:

  • 医学成像和人工智能 医学成像和人工智能
  • 肺部医学和呼吸道疾病

背景情况:

  • 慢性阻塞性肺病 (COPD) 构成了全球严重的健康负担.
  • 准确的预后对于管理COPD患者和改善治疗结果至关重要.
  • 现有的风险分层方法可能无法完全捕捉疾病的严重程度或进展.

研究的目的:

  • 评估人工智能 (AI) 在预测呼吸道死亡率方面从胸部放射图 (CXR-肺风险) 获得的临床有用性.
  • 评估CXR-肺风险得分与全球慢性阻塞性肺病倡议 (GOLD) 分级系统的性能.

主要方法:

  • 来自第三级中心的4226名COPD患者的回顾性分析 (2011-2015).
  • 使用开源AI算法生成的CXR-肺风险得分,应用于胸部X射线图.
  • 多变量细灰模型用于评估CXR-肺风险和呼吸道死亡率之间的关联,调整为临床因素和肺功能;使用AUC评估的歧视.

主要成果:

  • CXR-肺风险得分显著预测呼吸道死亡率 (每5年增加的分发危险比,1.16;P <.001),证明了超出肺功能和临床因素的附加值.
  • 对于CXR-Lung-Risk的接收器运行特征曲线 (AUC) 下的面积为0.76,在预测死亡率至10年时表现优于GOLD等级 (AUC = 0.61;P <.001).
  • 较高的CXR-肺风险得分与肺功能下降相关 (P < .001).

结论:

  • 开源人工智能工具CXR-Lung-Risk是COPD患者呼吸道死亡率的有价值和有效预测器.
  • 与传统方法相比,这种人工智能驱动的方法提供了更好的风险分层,可能有助于临床决策.
  • 这些发现支持将人工智能支持的图像分析集成到常规COPD管理中.