Deep learning for spirometry quality assurance with spirometric indices and curves.

ABSTRACT:

Background

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.

Methods

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 FEV₁ and FVC. System warning messages and patient instructio

SUBMITTER: Wang Y 

PROVIDER: S-EPMC9028127 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature