Faculty of Physics, Warsaw University of Technology, Koszykowa St. 75, 00-662, Warsaw, Poland.
School of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences & Medicine, King's College London, Strand, London WC2R 2LS, United Kingdom.
Physiol Meas. 2023 Aug 29;44(8). doi: 10.1088/1361-6579/acee41.
. The quality of spirometry manoeuvres is crucial for correctly interpreting the values of spirometry parameters. A fundamental guideline for proper quality assessment is the American Thoracic Society and European Respiratory Society (ATS/ERS) Standards for spirometry, updated in 2019, which describe several start-of-test and end-of-test criteria which can be assessed automatically. However, the spirometry standards also require a visual evaluation of the spirometry curve to determine the spirograms' acceptability or usability. In this study, we present an automatic algorithm based on a convolutional neural network (CNN) for quality assessment of the spirometry curves as an alternative to manual verification performed by specialists.. The algorithm for automatic assessment of spirometry measurements was created using a set of randomly selected 1998 spirograms which met all quantitative criteria defined by ATS/ERS Standards. Each spirogram was annotated as 'confirm' (remaining acceptable or usable status) or 'reject' (change the status to unacceptable) by four pulmonologists, separately for FEV1 and FVC parameters. The database was split into a training (80%) and test set (20%) for developing the CNN classification algorithm. The algorithm was optimised using a cross-validation method.. The accuracy, sensitivity and specificity obtained for the algorithm were 92.6%, 93.1% and 90.0% for FEV1 and 94.1%, 95.6% and 88.3% for FVC, respectively.The algorithm provides an opportunity to significantly improve the quality of spirometry tests, especially during unsupervised spirometry. It can also serve as an additional tool in clinical trials to quickly assess the quality of a large group of tests.
. 肺功能检查操作的质量对于正确解释肺功能检查参数值至关重要。正确质量评估的基本准则是美国胸科学会和欧洲呼吸学会（ATS/ERS）于 2019 年更新的肺功能检查标准，其中描述了一些可以自动评估的测试开始和结束标准。然而，肺功能检查标准还要求对肺功能检查曲线进行视觉评估，以确定肺功能图的可接受性或可用性。在这项研究中，我们提出了一种基于卷积神经网络（CNN）的肺功能检查曲线质量评估自动算法，作为专家进行手动验证的替代方法。. 自动评估肺功能测量的算法是使用一组随机选择的符合 ATS/ERS 标准定义的所有定量标准的 1998 份肺功能图创建的。每个肺功能图都由四名肺病专家分别对 FEV1 和 FVC 参数进行注释为“确认”（保持可接受或可用状态）或“拒绝”（将状态更改为不可接受）。数据库分为训练集（80%）和测试集（20%），用于开发 CNN 分类算法。使用交叉验证方法对算法进行了优化。. 对于 FEV1，算法获得的准确性、灵敏度和特异性分别为 92.6%、93.1%和 90.0%，对于 FVC 分别为 94.1%、95.6%和 88.3%。该算法为提高肺功能检查的质量提供了机会，特别是在非监督肺功能检查期间。它还可以作为临床试验中的附加工具，快速评估大量测试的质量。
