National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Yanjiang Road 151, Guangzhou, 510120, Guangdong, People's Republic of China.
Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, 518055, China.
Respir Res. 2022 Apr 21;23(1):98. doi: 10.1186/s12931-022-02014-9.
Spirometry quality assurance is a challenging task across levels of healthcare tiers, especially in primary care. Deep learning may serve as a support tool for enhancing spirometry quality. We aimed to develop a high accuracy and sensitive deep learning-based model aiming at assisting high-quality spirometry assurance.
Spirometry PDF files retrieved from one hospital between October 2017 and October 2020 were labeled according to ATS/ERS 2019 criteria and divided into training and internal test sets. Additional files from three hospitals were used for external testing. A deep learning-based model was constructed and assessed to determine acceptability, usability, and quality rating for FEV and FVC. System warning messages and patient instructions were also generated for general practitioners (GPs).
A total of 16,502 files were labeled. Of these, 4592 curves were assigned to the internal test set, the remaining constituted the training set. In the internal test set, the model generated 95.1%, 92.4%, and 94.3% accuracy for FEV acceptability, usability, and rating. The accuracy for FVC acceptability, usability, and rating were 93.6%, 94.3%, and 92.2%. With the assistance of the model, the performance of GPs in terms of monthly percentages of good quality (A, B, or C grades) tests for FEV and FVC was higher by ~ 21% and ~ 36%, respectively.
The proposed model assisted GPs in spirometry quality assurance, resulting in enhancing the performance of GPs in quality control of spirometry.
肺功能检测质量保证在各级医疗保健机构中都是一项具有挑战性的任务，尤其是在初级保健领域。深度学习可以作为一种支持工具，用于提高肺功能检测的质量。我们旨在开发一种高精度、高灵敏度的基于深度学习的模型，旨在协助高质量的肺功能检测保证。
根据 ATS/ERS 2019 标准对 2017 年 10 月至 2020 年 10 月期间从一家医院检索到的肺功能检测 PDF 文件进行标记，并将其分为训练集和内部测试集。另外还使用了来自三家医院的文件进行外部测试。构建并评估了一个基于深度学习的模型，以确定 FEV 和 FVC 的可接受性、可用性和质量评分。还为全科医生（GP）生成了系统警告消息和患者说明。
共标记了 16502 个文件。其中，4592 条曲线被分配到内部测试集，其余的构成了训练集。在内部测试集中，该模型对 FEV 的可接受性、可用性和评分的准确性分别为 95.1%、92.4%和 94.3%。FVC 的可接受性、可用性和评分的准确性分别为 93.6%、94.3%和 92.2%。在模型的协助下，GP 每月进行高质量（A、B 或 C 级）FEV 和 FVC 测试的百分比分别提高了约 21%和 36%。
所提出的模型协助了 GP 进行肺功能检测质量保证，从而提高了 GP 在肺功能检测质量控制方面的表现。
