基于胸部X射线图像和临床参数的深度学习的慢性阻塞性肺病的查和分期
XiaoLing Zou1, Yong Ren2,3, HaiLing Yang1
1Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, 510630, China.
BMC pulmonary medicine
|March 27, 2024
概括
使用胸部X射线和临床数据的深度学习模型显示了早期慢性阻塞性肺病 (COPD) 检测和严重程度评估的前景,优于传统方法.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 肺部病理学 肺部病理学
背景情况:
- 慢性阻塞性肺病 (COPD) 由于当前肺功能测试 (PFTs) 的局限性,经常被诊断不足.
- 需要更敏感和更容易使用的方法来早期检测和评估COPD的严重程度.
- 改进的诊断工具可以显著地使临床医生和患者受益.
研究的目的:
- 开发和验证用于COPD查和分期的深度学习 (DL) 算法.
- 评估将胸部X射线 (CXR) 图像与COPD诊断的临床数据相结合的疗效.
- 评估DL模型预测COPD疾病严重程度的能力.
主要方法:
- 一项多中心的回顾性研究分析了1055名参与者的前额胸部X射线 (CXR) 图像和临床数据.
- 深度学习和转移学习模型在666名受试者中进行了培训,并对284名参与者进行了验证.
- 与105名参与者的外部验证评估了该模型对COPD诊断和严重程度分级的概括能力.
主要成果:
- 集团DL模型使用合并的临床和CXR数据实现了0.969的COPD分类AUC,超过仅使用临床数据 (AUC=0.963) 或CXR (AUC=0.946) 的模型.
- 外部验证表明,COPD预测的强大的AUC为0.934.
- 该模型实现了0.894 (3类) 和0.852 (5类) 的AUC,用于预测COPD疾病严重程度.
结论:
- 深度学习算法有效地选COPD,并使用CXR和临床参数预测疾病严重程度.
- 这种人工智能驱动的方法显示出作为一种有效的,低辐射病例查找工具的潜力,用于COPD诊断和分期.
- 开发的模型在管理COPD诊断和患者分层方面提供了有前途的进展.
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