从鼓动性胸部CT估计小气管疾病的深度学习与FEV相关1 慢性肺炎的减少
Muhammad F A Chaudhary1,2, Hira A Awan1, Sarah E Gerard1
1The Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, 52242.
medRxiv : the preprint server for health sciences
|September 24, 2024
概括
人工智能现在可以使用全肺容量 (TLC) CT 扫描来量化功能性小气道疾病 (fSAD). 这种AI衍生的fSADTLC是可靠的,并且与COPD患者的疾病进展有关.
科学领域:
- 肺部医学 肺部医学
- 放射学 放射学是一门学科.
- 人工智能在医学中的应用
背景情况:
- 量化功能性小气道疾病 (fSAD) 通常需要额外的呼吸道CT扫描,限制其临床使用.
- 人工智能 (AI) 提供了一个潜在的解决方案,通过在全肺容量 (TLC) 的单个启发性CT扫描中实现fSAD量化,称为fSADTLC.
研究的目的:
- 评估一个AI模型来估计fSADTLC.
- 在慢性阻塞性肺病 (COPD) 患者中研究fSADTLC的临床关联.
主要方法:
- 在SPIROMICS研究中,使用1055名COPD参与者的CT扫描开发了一个生成AI模型,以估计fSADTLC.
- 人工智能模型应用于1458名SPIROMICS参与者,并在COPDGene研究中的458名参与者中得到验证.
- fSADTLC与双体积,参数响应映射fSADPRM进行了比较,并分析了其与肺功能 (FEV1,FEV1/FVC),6MWD,SGRQ和FEV1的相关性.
主要成果:
- 在SPIROMICS (R=0.895) 和COPDGene (R=0.897) 队列中,fSADTLC与fSADPRM具有很高的相关性.
- 在这两组研究中,fSADTLC与FEV1,FEV1/FVC,SGRQ得分以及FEV1的下降有显著的关联.
- 与fSADPRM (ICC=0.83) 相比,AI衍生的fSADTLC显示出更高的重复性 (ICC=0.99).
结论:
- 基于AI的fSADTLC量化来自启发性CT扫描是评估小气道疾病的可靠方法.
- fSADTLC是临床相关的,显示了与COPD严重程度和进展的显著关联.
- 这种人工智能方法简化了fSAD评估,潜在地提高了临床适用性和患者管理.
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